Pareto-Optimal Model Selection via SPRINT-Race.

Science.gov (United States)

Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

2018-02-01

In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

Pareto Optimal Design for Synthetic Biology.

Science.gov (United States)

Patanè, Andrea; Santoro, Andrea; Costanza, Jole; Carapezza, Giovanni; Nicosia, Giuseppe

2015-08-01

Recent advances in synthetic biology call for robust, flexible and efficient in silico optimization methodologies. We present a Pareto design approach for the bi-level optimization problem associated to the overproduction of specific metabolites in Escherichia coli. Our method efficiently explores the high dimensional genetic manipulation space, finding a number of trade-offs between synthetic and biological objectives, hence furnishing a deeper biological insight to the addressed problem and important results for industrial purposes. We demonstrate the computational capabilities of our Pareto-oriented approach comparing it with state-of-the-art heuristics in the overproduction problems of i) 1,4-butanediol, ii) myristoyl-CoA, i ii) malonyl-CoA , iv) acetate and v) succinate. We show that our algorithms are able to gracefully adapt and scale to more complex models and more biologically-relevant simulations of the genetic manipulations allowed. The Results obtained for 1,4-butanediol overproduction significantly outperform results previously obtained, in terms of 1,4-butanediol to biomass formation ratio and knock-out costs. In particular overproduction percentage is of +662.7%, from 1.425 mmolh⁻¹gDW⁻¹ (wild type) to 10.869 mmolh⁻¹gDW⁻¹, with a knockout cost of 6. Whereas, Pareto-optimal designs we have found in fatty acid optimizations strictly dominate the ones obtained by the other methodologies, e.g., biomass and myristoyl-CoA exportation improvement of +21.43% (0.17 h⁻¹) and +5.19% (1.62 mmolh⁻¹gDW⁻¹), respectively. Furthermore CPU time required by our heuristic approach is more than halved. Finally we implement pathway oriented sensitivity analysis, epsilon-dominance analysis and robustness analysis to enhance our biological understanding of the problem and to improve the optimization algorithm capabilities.

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

Science.gov (United States)

Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

2010-01-01

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

Diversity comparison of Pareto front approximations in many-objective optimization.

Science.gov (United States)

Li, Miqing; Yang, Shengxiang; Liu, Xiaohui

2014-12-01

Diversity assessment of Pareto front approximations is an important issue in the stochastic multiobjective optimization community. Most of the diversity indicators in the literature were designed to work for any number of objectives of Pareto front approximations in principle, but in practice many of these indicators are infeasible or not workable when the number of objectives is large. In this paper, we propose a diversity comparison indicator (DCI) to assess the diversity of Pareto front approximations in many-objective optimization. DCI evaluates relative quality of different Pareto front approximations rather than provides an absolute measure of distribution for a single approximation. In DCI, all the concerned approximations are put into a grid environment so that there are some hyperboxes containing one or more solutions. The proposed indicator only considers the contribution of different approximations to nonempty hyperboxes. Therefore, the computational cost does not increase exponentially with the number of objectives. In fact, the implementation of DCI is of quadratic time complexity, which is fully independent of the number of divisions used in grid. Systematic experiments are conducted using three groups of artificial Pareto front approximations and seven groups of real Pareto front approximations with different numbers of objectives to verify the effectiveness of DCI. Moreover, a comparison with two diversity indicators used widely in many-objective optimization is made analytically and empirically. Finally, a parametric investigation reveals interesting insights of the division number in grid and also offers some suggested settings to the users with different preferences.

Projections onto the Pareto surface in multicriteria radiation therapy optimization

International Nuclear Information System (INIS)

Bokrantz, Rasmus; Miettinen, Kaisa

2015-01-01

Purpose: To eliminate or reduce the error to Pareto optimality that arises in Pareto surface navigation when the Pareto surface is approximated by a small number of plans. Methods: The authors propose to project the navigated plan onto the Pareto surface as a postprocessing step to the navigation. The projection attempts to find a Pareto optimal plan that is at least as good as or better than the initial navigated plan with respect to all objective functions. An augmented form of projection is also suggested where dose–volume histogram constraints are used to prevent that the projection causes a violation of some clinical goal. The projections were evaluated with respect to planning for intensity modulated radiation therapy delivered by step-and-shoot and sliding window and spot-scanned intensity modulated proton therapy. Retrospective plans were generated for a prostate and a head and neck case. Results: The projections led to improved dose conformity and better sparing of organs at risk (OARs) for all three delivery techniques and both patient cases. The mean dose to OARs decreased by 3.1 Gy on average for the unconstrained form of the projection and by 2.0 Gy on average when dose–volume histogram constraints were used. No consistent improvements in target homogeneity were observed. Conclusions: There are situations when Pareto navigation leaves room for improvement in OAR sparing and dose conformity, for example, if the approximation of the Pareto surface is coarse or the problem formulation has too permissive constraints. A projection onto the Pareto surface can identify an inaccurate Pareto surface representation and, if necessary, improve the quality of the navigated plan

Projections onto the Pareto surface in multicriteria radiation therapy optimization.

Science.gov (United States)

Bokrantz, Rasmus; Miettinen, Kaisa

2015-10-01

To eliminate or reduce the error to Pareto optimality that arises in Pareto surface navigation when the Pareto surface is approximated by a small number of plans. The authors propose to project the navigated plan onto the Pareto surface as a postprocessing step to the navigation. The projection attempts to find a Pareto optimal plan that is at least as good as or better than the initial navigated plan with respect to all objective functions. An augmented form of projection is also suggested where dose-volume histogram constraints are used to prevent that the projection causes a violation of some clinical goal. The projections were evaluated with respect to planning for intensity modulated radiation therapy delivered by step-and-shoot and sliding window and spot-scanned intensity modulated proton therapy. Retrospective plans were generated for a prostate and a head and neck case. The projections led to improved dose conformity and better sparing of organs at risk (OARs) for all three delivery techniques and both patient cases. The mean dose to OARs decreased by 3.1 Gy on average for the unconstrained form of the projection and by 2.0 Gy on average when dose-volume histogram constraints were used. No consistent improvements in target homogeneity were observed. There are situations when Pareto navigation leaves room for improvement in OAR sparing and dose conformity, for example, if the approximation of the Pareto surface is coarse or the problem formulation has too permissive constraints. A projection onto the Pareto surface can identify an inaccurate Pareto surface representation and, if necessary, improve the quality of the navigated plan.

Diversity shrinkage: Cross-validating pareto-optimal weights to enhance diversity via hiring practices.

Science.gov (United States)

Song, Q Chelsea; Wee, Serena; Newman, Daniel A

2017-12-01

To reduce adverse impact potential and improve diversity outcomes from personnel selection, one promising technique is De Corte, Lievens, and Sackett's (2007) Pareto-optimal weighting strategy. De Corte et al.'s strategy has been demonstrated on (a) a composite of cognitive and noncognitive (e.g., personality) tests (De Corte, Lievens, & Sackett, 2008) and (b) a composite of specific cognitive ability subtests (Wee, Newman, & Joseph, 2014). Both studies illustrated how Pareto-weighting (in contrast to unit weighting) could lead to substantial improvement in diversity outcomes (i.e., diversity improvement), sometimes more than doubling the number of job offers for minority applicants. The current work addresses a key limitation of the technique-the possibility of shrinkage, especially diversity shrinkage, in the Pareto-optimal solutions. Using Monte Carlo simulations, sample size and predictor combinations were varied and cross-validated Pareto-optimal solutions were obtained. Although diversity shrinkage was sizable for a composite of cognitive and noncognitive predictors when sample size was at or below 500, diversity shrinkage was typically negligible for a composite of specific cognitive subtest predictors when sample size was at least 100. Diversity shrinkage was larger when the Pareto-optimal solution suggested substantial diversity improvement. When sample size was at least 100, cross-validated Pareto-optimal weights typically outperformed unit weights-suggesting that diversity improvement is often possible, despite diversity shrinkage. Implications for Pareto-optimal weighting, adverse impact, sample size of validation studies, and optimizing the diversity-job performance tradeoff are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

TU-C-17A-01: A Data-Based Development for Pratical Pareto Optimality Assessment and Identification

International Nuclear Information System (INIS)

Ruan, D; Qi, S; DeMarco, J; Kupelian, P; Low, D

2014-01-01

Purpose: To develop an efficient Pareto optimality assessment scheme to support plan comparison and practical determination of best-achievable practical treatment plan goals. Methods: Pareto efficiency reflects the tradeoffs among competing target coverage and normal tissue sparing in multi-criterion optimization (MCO) based treatment planning. Assessing and understanding Pareto optimality provides insightful guidance for future planning. However, current MCO-driven Pareto estimation makes relaxed assumptions about the Pareto structure and insufficiently account for practical limitations in beam complexity, leading to performance upper bounds that may be unachievable. This work proposed an alternative data-driven approach that implicitly incorporates the practical limitations, and identifies the Pareto frontier subset by eliminating dominated plans incrementally using the Edgeworth Pareto hull (EPH). The exactness of this elimination process also permits the development of a hierarchical procedure for speedup when the plan cohort size is large, by partitioning the cohort and performing elimination in each subset before a final aggregated elimination. The developed algorithm was first tested on 2D and 3D where accuracy can be reliably assessed. As a specific application, the algorithm was applied to compare systematic plan quality for lower head-and-neck, amongst 4 competing treatment modalities. Results: The algorithm agrees exactly with brute-force pairwise comparison and visual inspection in low dimensions. The hierarchical algorithm shows sqrt(k) folds speedup with k being the number of data points in the plan cohort, demonstrating good efficiency enhancement for heavy testing tasks. Application to plan performance comparison showed superiority of tomotherapy plans for the lower head-and-neck, and revealed a potential nonconvex Pareto frontier structure. Conclusion: An accurate and efficient scheme to identify Pareto frontier from a plan cohort has been

TU-C-17A-01: A Data-Based Development for Pratical Pareto Optimality Assessment and Identification

Energy Technology Data Exchange (ETDEWEB)

Ruan, D; Qi, S; DeMarco, J; Kupelian, P; Low, D [UCLA Department of Radiation Oncology, Los Angeles, CA (United States)

2014-06-15

Purpose: To develop an efficient Pareto optimality assessment scheme to support plan comparison and practical determination of best-achievable practical treatment plan goals. Methods: Pareto efficiency reflects the tradeoffs among competing target coverage and normal tissue sparing in multi-criterion optimization (MCO) based treatment planning. Assessing and understanding Pareto optimality provides insightful guidance for future planning. However, current MCO-driven Pareto estimation makes relaxed assumptions about the Pareto structure and insufficiently account for practical limitations in beam complexity, leading to performance upper bounds that may be unachievable. This work proposed an alternative data-driven approach that implicitly incorporates the practical limitations, and identifies the Pareto frontier subset by eliminating dominated plans incrementally using the Edgeworth Pareto hull (EPH). The exactness of this elimination process also permits the development of a hierarchical procedure for speedup when the plan cohort size is large, by partitioning the cohort and performing elimination in each subset before a final aggregated elimination. The developed algorithm was first tested on 2D and 3D where accuracy can be reliably assessed. As a specific application, the algorithm was applied to compare systematic plan quality for lower head-and-neck, amongst 4 competing treatment modalities. Results: The algorithm agrees exactly with brute-force pairwise comparison and visual inspection in low dimensions. The hierarchical algorithm shows sqrt(k) folds speedup with k being the number of data points in the plan cohort, demonstrating good efficiency enhancement for heavy testing tasks. Application to plan performance comparison showed superiority of tomotherapy plans for the lower head-and-neck, and revealed a potential nonconvex Pareto frontier structure. Conclusion: An accurate and efficient scheme to identify Pareto frontier from a plan cohort has been

Pareto Optimization of a Half Car Passive Suspension Model Using a Novel Multiobjective Heat Transfer Search Algorithm

Directory of Open Access Journals (Sweden)

Vimal Savsani

2017-01-01

Full Text Available Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS algorithm, which is based on the search technique of heat transfer search (HTS algorithm. MOHTS employs the elitist nondominated sorting and crowding distance approach of an elitist based nondominated sorting genetic algorithm-II (NSGA-II for obtaining different nondomination levels and to preserve the diversity among the optimal set of solutions, respectively. The capability in yielding a Pareto front as close as possible to the true Pareto front of MOHTS has been tested on the multiobjective optimization problem of the vehicle suspension design, which has a set of five second-order linear ordinary differential equations. Half car passive ride model with two different sets of five objectives is employed for optimizing the suspension parameters using MOHTS and NSGA-II. The optimization studies demonstrate that MOHTS achieves the better nondominated Pareto front with the widespread (diveresed set of optimal solutions as compared to NSGA-II, and further the comparison of the extreme points of the obtained Pareto front reveals the dominance of MOHTS over NSGA-II, multiobjective uniform diversity genetic algorithm (MUGA, and combined PSO-GA based MOEA.

Pareto-optimal multi-objective design of airplane control systems

Science.gov (United States)

Schy, A. A.; Johnson, K. G.; Giesy, D. P.

1980-01-01

A constrained minimization algorithm for the computer aided design of airplane control systems to meet many requirements over a set of flight conditions is generalized using the concept of Pareto-optimization. The new algorithm yields solutions on the boundary of the achievable domain in objective space in a single run, whereas the older method required a sequence of runs to approximate such a limiting solution. However, Pareto-optimality does not guarantee a satisfactory design, since such solutions may emphasize some objectives at the expense of others. The designer must still interact with the program to obtain a well-balanced set of objectives. Using the example of a fighter lateral stability augmentation system (SAS) design over five flight conditions, several effective techniques are developed for obtaining well-balanced Pareto-optimal solutions. For comparison, one of these techniques is also used in a recently developed algorithm of Kreisselmeier and Steinhauser, which replaces the hard constraints with soft constraints, using a special penalty function. It is shown that comparable results can be obtained.

Pareto optimal design of sectored toroidal superconducting magnet for SMES

Science.gov (United States)

Bhunia, Uttam; Saha, Subimal; Chakrabarti, Alok

2014-10-01

A novel multi-objective optimization design approach for sectored toroidal superconducting magnetic energy storage coil has been developed considering the practical engineering constraints. The objectives include the minimization of necessary superconductor length and torus overall size or volume, which determines a significant part of cost towards realization of SMES. The best trade-off between the necessary conductor length for winding and magnet overall size is achieved in the Pareto-optimal solutions, the compact magnet size leads to increase in required superconducting cable length or vice versa The final choice among Pareto optimal configurations can be done in relation to other issues such as AC loss during transient operation, stray magnetic field at outside the coil assembly, and available discharge period, which is not considered in the optimization process. The proposed design approach is adapted for a 4.5 MJ/1 MW SMES system using low temperature niobium-titanium based Rutherford type cable. Furthermore, the validity of the representative Pareto solutions is confirmed by finite-element analysis (FEA) with a reasonably acceptable accuracy.

Approximating convex Pareto surfaces in multiobjective radiotherapy planning

International Nuclear Information System (INIS)

Craft, David L.; Halabi, Tarek F.; Shih, Helen A.; Bortfeld, Thomas R.

2006-01-01

Radiotherapy planning involves inherent tradeoffs: the primary mission, to treat the tumor with a high, uniform dose, is in conflict with normal tissue sparing. We seek to understand these tradeoffs on a case-to-case basis, by computing for each patient a database of Pareto optimal plans. A treatment plan is Pareto optimal if there does not exist another plan which is better in every measurable dimension. The set of all such plans is called the Pareto optimal surface. This article presents an algorithm for computing well distributed points on the (convex) Pareto optimal surface of a multiobjective programming problem. The algorithm is applied to intensity-modulated radiation therapy inverse planning problems, and results of a prostate case and a skull base case are presented, in three and four dimensions, investigating tradeoffs between tumor coverage and critical organ sparing

A New Methodology to Select the Preferred Solutions from the Pareto-optimal Set: Application to Polymer Extrusion

International Nuclear Information System (INIS)

Ferreira, Jose C.; Gaspar-Cunha, Antonio; Fonseca, Carlos M.

2007-01-01

Most of the real world optimization problems involve multiple, usually conflicting, optimization criteria. Generating Pareto optimal solutions plays an important role in multi-objective optimization, and the problem is considered to be solved when the Pareto optimal set is found, i.e., the set of non-dominated solutions. Multi-Objective Evolutionary Algorithms based on the principle of Pareto optimality are designed to produce the complete set of non-dominated solutions. However, this is not allays enough since the aim is not only to know the Pareto set but, also, to obtain one solution from this Pareto set. Thus, the definition of a methodology able to select a single solution from the set of non-dominated solutions (or a region of the Pareto frontier), and taking into account the preferences of a Decision Maker (DM), is necessary. A different method, based on a weighted stress function, is proposed. It is able to integrate the user's preferences in order to find the best region of the Pareto frontier accordingly with these preferences. This method was tested on some benchmark test problems, with two and three criteria, and on a polymer extrusion problem. This methodology is able to select efficiently the best Pareto-frontier region for the specified relative importance of the criteria

Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front.

Science.gov (United States)

Saborido, Rubén; Ruiz, Ana B; Luque, Mariano

2017-01-01

In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA ( global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.

Pareto-optimal reversed-phase chromatography separation of three insulin variants with a solubility constraint.

Science.gov (United States)

Arkell, Karolina; Knutson, Hans-Kristian; Frederiksen, Søren S; Breil, Martin P; Nilsson, Bernt

2018-01-12

With the shift of focus of the regulatory bodies, from fixed process conditions towards flexible ones based on process understanding, model-based optimization is becoming an important tool for process development within the biopharmaceutical industry. In this paper, a multi-objective optimization study of separation of three insulin variants by reversed-phase chromatography (RPC) is presented. The decision variables were the load factor, the concentrations of ethanol and KCl in the eluent, and the cut points for the product pooling. In addition to the purity constraints, a solubility constraint on the total insulin concentration was applied. The insulin solubility is a function of the ethanol concentration in the mobile phase, and the main aim was to investigate the effect of this constraint on the maximal productivity. Multi-objective optimization was performed with and without the solubility constraint, and visualized as Pareto fronts, showing the optimal combinations of the two objectives productivity and yield for each case. Comparison of the constrained and unconstrained Pareto fronts showed that the former diverges when the constraint becomes active, because the increase in productivity with decreasing yield is almost halted. Consequently, we suggest the operating point at which the total outlet concentration of insulin reaches the solubility limit as the most suitable one. According to the results from the constrained optimizations, the maximal productivity on the C 4 adsorbent (0.41 kg/(m 3  column h)) is less than half of that on the C 18 adsorbent (0.87 kg/(m 3  column h)). This is partly caused by the higher selectivity between the insulin variants on the C 18 adsorbent, but the main reason is the difference in how the solubility constraint affects the processes. Since the optimal ethanol concentration for elution on the C 18 adsorbent is higher than for the C 4 one, the insulin solubility is also higher, allowing a higher pool concentration

A divide and conquer approach to determine the Pareto frontier for optimization of protein engineering experiments

Science.gov (United States)

He, Lu; Friedman, Alan M.; Bailey-Kellogg, Chris

2016-01-01

In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability vs. novelty, affinity vs. specificity, activity vs. immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not “dominated”; i.e., no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), in order to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, PEPFR (Protein Engineering Pareto FRontier), that hierarchically subdivides the objective space, employing appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria. PMID:22180081

A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.

Science.gov (United States)

He, Lu; Friedman, Alan M; Bailey-Kellogg, Chris

2012-03-01

In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria. Copyright © 2011 Wiley Periodicals, Inc.

Pareto optimal design of sectored toroidal superconducting magnet for SMES

Energy Technology Data Exchange (ETDEWEB)

Bhunia, Uttam, E-mail: ubhunia@vecc.gov.in; Saha, Subimal; Chakrabarti, Alok

2014-10-15

Highlights: • The optimization approach minimizes both the magnet size and necessary cable length of a sectored toroidal SMES unit. • Design approach is suitable for low temperature superconducting cable suitable for medium size SMES unit. • It investigates coil parameters with respect to practical engineering aspects. - Abstract: A novel multi-objective optimization design approach for sectored toroidal superconducting magnetic energy storage coil has been developed considering the practical engineering constraints. The objectives include the minimization of necessary superconductor length and torus overall size or volume, which determines a significant part of cost towards realization of SMES. The best trade-off between the necessary conductor length for winding and magnet overall size is achieved in the Pareto-optimal solutions, the compact magnet size leads to increase in required superconducting cable length or vice versa The final choice among Pareto optimal configurations can be done in relation to other issues such as AC loss during transient operation, stray magnetic field at outside the coil assembly, and available discharge period, which is not considered in the optimization process. The proposed design approach is adapted for a 4.5 MJ/1 MW SMES system using low temperature niobium–titanium based Rutherford type cable. Furthermore, the validity of the representative Pareto solutions is confirmed by finite-element analysis (FEA) with a reasonably acceptable accuracy.

Pareto optimal design of sectored toroidal superconducting magnet for SMES

International Nuclear Information System (INIS)

Bhunia, Uttam; Saha, Subimal; Chakrabarti, Alok

2014-01-01

Highlights: • The optimization approach minimizes both the magnet size and necessary cable length of a sectored toroidal SMES unit. • Design approach is suitable for low temperature superconducting cable suitable for medium size SMES unit. • It investigates coil parameters with respect to practical engineering aspects. - Abstract: A novel multi-objective optimization design approach for sectored toroidal superconducting magnetic energy storage coil has been developed considering the practical engineering constraints. The objectives include the minimization of necessary superconductor length and torus overall size or volume, which determines a significant part of cost towards realization of SMES. The best trade-off between the necessary conductor length for winding and magnet overall size is achieved in the Pareto-optimal solutions, the compact magnet size leads to increase in required superconducting cable length or vice versa The final choice among Pareto optimal configurations can be done in relation to other issues such as AC loss during transient operation, stray magnetic field at outside the coil assembly, and available discharge period, which is not considered in the optimization process. The proposed design approach is adapted for a 4.5 MJ/1 MW SMES system using low temperature niobium–titanium based Rutherford type cable. Furthermore, the validity of the representative Pareto solutions is confirmed by finite-element analysis (FEA) with a reasonably acceptable accuracy

Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation

International Nuclear Information System (INIS)

Zio, E.; Bazzo, R.

2011-01-01

Reliability-based and risk-informed design, operation, maintenance and regulation lead to multiobjective (multicriteria) optimization problems. In this context, the Pareto Front and Set found in a multiobjective optimality search provide a family of solutions among which the decision maker has to look for the best choice according to his or her preferences. Efficient visualization techniques for Pareto Front and Set analyses are needed for helping decision makers in the selection task. In this paper, we consider the multiobjective optimization of system redundancy allocation and use the recently introduced Level Diagrams technique for graphically representing the resulting Pareto Front and Set. Each objective and decision variable is represented on separate diagrams where the points of the Pareto Front and Set are positioned according to their proximity to ideally optimal points, as measured by a metric of normalized objective values. All diagrams are synchronized across all objectives and decision variables. On the basis of the analysis of the Level Diagrams, we introduce a procedure for reducing the number of solutions in the Pareto Front; from the reduced set of solutions, the decision maker can more easily identify his or her preferred solution.

A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation

Directory of Open Access Journals (Sweden)

Lina Yang

2018-02-01

Full Text Available Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by state-of-the-art algorithms is still not sufficient. To achieve better Pareto solutions, taking the grid-representative land-use allocation problem with two objectives as an example, an artificial bee colony optimization algorithm for multi-objective land-use allocation (ABC-MOLA is proposed. In this algorithm, the traditional ABC’s search direction guiding scheme and solution maintaining process are modified. In addition, a knowledge-informed neighborhood search strategy, which utilizes the auxiliary knowledge of natural geography and spatial structures to facilitate the neighborhood spatial search around each solution, is developed to further improve the Pareto front’s quality. A series of comparison experiments (a simulated experiment with small data volume and a real-world data experiment for a large area shows that all the Pareto fronts obtained by ABC-MOLA totally dominate the Pareto fronts by other algorithms, which demonstrates ABC-MOLA’s effectiveness in achieving Pareto fronts of high quality.

Pareto Optimal Solutions for Network Defense Strategy Selection Simulator in Multi-Objective Reinforcement Learning

Directory of Open Access Journals (Sweden)

Yang Sun

2018-01-01

Full Text Available Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.

Tractable Pareto Optimization of Temporal Preferences

Science.gov (United States)

Morris, Robert; Morris, Paul; Khatib, Lina; Venable, Brent

2003-01-01

This paper focuses on temporal constraint problems where the objective is to optimize a set of local preferences for when events occur. In previous work, a subclass of these problems has been formalized as a generalization of Temporal CSPs, and a tractable strategy for optimization has been proposed, where global optimality is defined as maximizing the minimum of the component preference values. This criterion for optimality, which we call 'Weakest Link Optimization' (WLO), is known to have limited practical usefulness because solutions are compared only on the basis of their worst value; thus, there is no requirement to improve the other values. To address this limitation, we introduce a new algorithm that re-applies WLO iteratively in a way that leads to improvement of all the values. We show the value of this strategy by proving that, with suitable preference functions, the resulting solutions are Pareto Optimal.

Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

Science.gov (United States)

Giesy, D. P.

1978-01-01

A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

Calculating complete and exact Pareto front for multiobjective optimization: a new deterministic approach for discrete problems.

Science.gov (United States)

Hu, Xiao-Bing; Wang, Ming; Di Paolo, Ezequiel

2013-06-01

Searching the Pareto front for multiobjective optimization problems usually involves the use of a population-based search algorithm or of a deterministic method with a set of different single aggregate objective functions. The results are, in fact, only approximations of the real Pareto front. In this paper, we propose a new deterministic approach capable of fully determining the real Pareto front for those discrete problems for which it is possible to construct optimization algorithms to find the k best solutions to each of the single-objective problems. To this end, two theoretical conditions are given to guarantee the finding of the actual Pareto front rather than its approximation. Then, a general methodology for designing a deterministic search procedure is proposed. A case study is conducted, where by following the general methodology, a ripple-spreading algorithm is designed to calculate the complete exact Pareto front for multiobjective route optimization. When compared with traditional Pareto front search methods, the obvious advantage of the proposed approach is its unique capability of finding the complete Pareto front. This is illustrated by the simulation results in terms of both solution quality and computational efficiency.

Approximating the Pareto set of multiobjective linear programs via robust optimization

NARCIS (Netherlands)

Gorissen, B.L.; den Hertog, D.

2012-01-01

We consider problems with multiple linear objectives and linear constraints and use adjustable robust optimization and polynomial optimization as tools to approximate the Pareto set with polynomials of arbitrarily large degree. The main difference with existing techniques is that we optimize a

Optimization of externalities using DTM measures: a Pareto optimal multi objective optimization using the evolutionary algorithm SPEA2+

NARCIS (Netherlands)

Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, Michiel; Allkim, T.P.; van Arem, Bart

2010-01-01

Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto optimal set is determined by employing the SPEA2+ evolutionary algorithm.

Feasibility of identification of gamma knife planning strategies by identification of pareto optimal gamma knife plans.

Science.gov (United States)

Giller, C A

2011-12-01

The use of conformity indices to optimize Gamma Knife planning is common, but does not address important tradeoffs between dose to tumor and normal tissue. Pareto analysis has been used for this purpose in other applications, but not for Gamma Knife (GK) planning. The goal of this work is to use computer models to show that Pareto analysis may be feasible for GK planning to identify dosimetric tradeoffs. We define a GK plan A to be Pareto dominant to B if the prescription isodose volume of A covers more tumor but not more normal tissue than B, or if A covers less normal tissue but not less tumor than B. A plan is Pareto optimal if it is not dominated by any other plan. Two different Pareto optimal plans represent different tradeoffs between dose to tumor and normal tissue, because neither plan dominates the other. 'GK simulator' software calculated dose distributions for GK plans, and was called repetitively by a genetic algorithm to calculate Pareto dominant plans. Three irregular tumor shapes were tested in 17 trials using various combinations of shots. The mean number of Pareto dominant plans/trial was 59 ± 17 (sd). Different planning strategies were identified by large differences in shot positions, and 70 of the 153 coordinate plots (46%) showed differences of 5mm or more. The Pareto dominant plans dominated other nearby plans. Pareto dominant plans represent different dosimetric tradeoffs and can be systematically calculated using genetic algorithms. Automatic identification of non-intuitive planning strategies may be feasible with these methods.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method.

Science.gov (United States)

Li, Yaohang; Rata, Ionel; Chiu, See-wing; Jakobsson, Eric

2010-07-20

Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

Optimal PMU Placement with Uncertainty Using Pareto Method

Directory of Open Access Journals (Sweden)

A. Ketabi

2012-01-01

Full Text Available This paper proposes a method for optimal placement of Phasor Measurement Units (PMUs in state estimation considering uncertainty. State estimation has first been turned into an optimization exercise in which the objective function is selected to be the number of unobservable buses which is determined based on Singular Value Decomposition (SVD. For the normal condition, Differential Evolution (DE algorithm is used to find the optimal placement of PMUs. By considering uncertainty, a multiobjective optimization exercise is hence formulated. To achieve this, DE algorithm based on Pareto optimum method has been proposed here. The suggested strategy is applied on the IEEE 30-bus test system in several case studies to evaluate the optimal PMUs placement.

Active learning of Pareto fronts.

Science.gov (United States)

Campigotto, Paolo; Passerini, Andrea; Battiti, Roberto

2014-03-01

This paper introduces the active learning of Pareto fronts (ALP) algorithm, a novel approach to recover the Pareto front of a multiobjective optimization problem. ALP casts the identification of the Pareto front into a supervised machine learning task. This approach enables an analytical model of the Pareto front to be built. The computational effort in generating the supervised information is reduced by an active learning strategy. In particular, the model is learned from a set of informative training objective vectors. The training objective vectors are approximated Pareto-optimal vectors obtained by solving different scalarized problem instances. The experimental results show that ALP achieves an accurate Pareto front approximation with a lower computational effort than state-of-the-art estimation of distribution algorithms and widely known genetic techniques.

COMPROMISE, OPTIMAL AND TRACTIONAL ACCOUNTS ON PARETO SET

Directory of Open Access Journals (Sweden)

V. V. Lahuta

2010-11-01

Full Text Available The problem of optimum traction calculations is considered as a problem about optimum distribution of a resource. The dynamic programming solution is based on a step-by-step calculation of set of points of Pareto-optimum values of a criterion function (energy expenses and a resource (time.

Determination of Pareto frontier in multi-objective maintenance optimization

International Nuclear Information System (INIS)

Certa, Antonella; Galante, Giacomo; Lupo, Toni; Passannanti, Gianfranco

2011-01-01

The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series-parallel system.

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

Science.gov (United States)

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization

Directory of Open Access Journals (Sweden)

Na Tian

2015-01-01

Full Text Available A study on pareto-ranking based quantum-behaved particle swarm optimization (QPSO for multiobjective optimization problems is presented in this paper. During the iteration, an external repository is maintained to remember the nondominated solutions, from which the global best position is chosen. The comparison between different elitist selection strategies (preference order, sigma value, and random selection is performed on four benchmark functions and two metrics. The results demonstrate that QPSO with preference order has comparative performance with sigma value according to different number of objectives. Finally, QPSO with sigma value is applied to solve multiobjective flexible job-shop scheduling problems.

Prederivatives of gamma paraconvex set-valued maps and Pareto optimality conditions for set optimization problems.

Science.gov (United States)

Huang, Hui; Ning, Jixian

2017-01-01

Prederivatives play an important role in the research of set optimization problems. First, we establish several existence theorems of prederivatives for γ -paraconvex set-valued mappings in Banach spaces with [Formula: see text]. Then, in terms of prederivatives, we establish both necessary and sufficient conditions for the existence of Pareto minimal solution of set optimization problems.

An approach to multiobjective optimization of rotational therapy. II. Pareto optimal surfaces and linear combinations of modulated blocked arcs for a prostate geometry.

Science.gov (United States)

Pardo-Montero, Juan; Fenwick, John D

2010-06-01

The purpose of this work is twofold: To further develop an approach to multiobjective optimization of rotational therapy treatments recently introduced by the authors [J. Pardo-Montero and J. D. Fenwick, "An approach to multiobjective optimization of rotational therapy," Med. Phys. 36, 3292-3303 (2009)], especially regarding its application to realistic geometries, and to study the quality (Pareto optimality) of plans obtained using such an approach by comparing them with Pareto optimal plans obtained through inverse planning. In the previous work of the authors, a methodology is proposed for constructing a large number of plans, with different compromises between the objectives involved, from a small number of geometrically based arcs, each arc prioritizing different objectives. Here, this method has been further developed and studied. Two different techniques for constructing these arcs are investigated, one based on image-reconstruction algorithms and the other based on more common gradient-descent algorithms. The difficulty of dealing with organs abutting the target, briefly reported in previous work of the authors, has been investigated using partial OAR unblocking. Optimality of the solutions has been investigated by comparison with a Pareto front obtained from inverse planning. A relative Euclidean distance has been used to measure the distance of these plans to the Pareto front, and dose volume histogram comparisons have been used to gauge the clinical impact of these distances. A prostate geometry has been used for the study. For geometries where a blocked OAR abuts the target, moderate OAR unblocking can substantially improve target dose distribution and minimize hot spots while not overly compromising dose sparing of the organ. Image-reconstruction type and gradient-descent blocked-arc computations generate similar results. The Pareto front for the prostate geometry, reconstructed using a large number of inverse plans, presents a hockey-stick shape

Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives

International Nuclear Information System (INIS)

Warmflash, Aryeh; Siggia, Eric D; Francois, Paul

2012-01-01

The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input–output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria. (paper)

Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.

Science.gov (United States)

Warmflash, Aryeh; Francois, Paul; Siggia, Eric D

2012-10-01

The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.

Pareto optimality between width of central lobe and peak sidelobe intensity in the far-field pattern of lossless phase-only filters for enhancement of transverse resolution.

Science.gov (United States)

Mukhopadhyay, Somparna; Hazra, Lakshminarayan

2015-11-01

Resolution capability of an optical imaging system can be enhanced by reducing the width of the central lobe of the point spread function. Attempts to achieve the same by pupil plane filtering give rise to a concomitant increase in sidelobe intensity. The mutual exclusivity between these two objectives may be considered as a multiobjective optimization problem that does not have a unique solution; rather, a class of trade-off solutions called Pareto optimal solutions may be generated. Pareto fronts in the synthesis of lossless phase-only pupil plane filters to achieve superresolution with prespecified lower limits for the Strehl ratio are explored by using the particle swarm optimization technique.

Approximating the Pareto Set of Multiobjective Linear Programs via Robust Optimization

NARCIS (Netherlands)

Gorissen, B.L.; den Hertog, D.

2012-01-01

Abstract: The Pareto set of a multiobjective optimization problem consists of the solutions for which one or more objectives can not be improved without deteriorating one or more other objectives. We consider problems with linear objectives and linear constraints and use Adjustable Robust

The Successor Function and Pareto Optimal Solutions of Cooperative Differential Systems with Concavity. I

DEFF Research Database (Denmark)

Andersen, Kurt Munk; Sandqvist, Allan

1997-01-01

We investigate the domain of definition and the domain of values for the successor function of a cooperative differential system x'=f(t,x), where the coordinate functions are concave in x for any fixed value of t. Moreover, we give a characterization of a weakly Pareto optimal solution.......We investigate the domain of definition and the domain of values for the successor function of a cooperative differential system x'=f(t,x), where the coordinate functions are concave in x for any fixed value of t. Moreover, we give a characterization of a weakly Pareto optimal solution....

Comparative analysis of Pareto surfaces in multi-criteria IMRT planning

Energy Technology Data Exchange (ETDEWEB)

Teichert, K; Suess, P; Serna, J I; Monz, M; Kuefer, K H [Department of Optimization, Fraunhofer Institute for Industrial Mathematics (ITWM), Fraunhofer Platz 1, 67663 Kaiserslautern (Germany); Thieke, C, E-mail: katrin.teichert@itwm.fhg.de [Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg (Germany)

2011-06-21

In the multi-criteria optimization approach to IMRT planning, a given dose distribution is evaluated by a number of convex objective functions that measure tumor coverage and sparing of the different organs at risk. Within this context optimizing the intensity profiles for any fixed set of beams yields a convex Pareto set in the objective space. However, if the number of beam directions and irradiation angles are included as free parameters in the formulation of the optimization problem, the resulting Pareto set becomes more intricate. In this work, a method is presented that allows for the comparison of two convex Pareto sets emerging from two distinct beam configuration choices. For the two competing beam settings, the non-dominated and the dominated points of the corresponding Pareto sets are identified and the distance between the two sets in the objective space is calculated and subsequently plotted. The obtained information enables the planner to decide if, for a given compromise, the current beam setup is optimal. He may then re-adjust his choice accordingly during navigation. The method is applied to an artificial case and two clinical head neck cases. In all cases no configuration is dominating its competitor over the whole Pareto set. For example, in one of the head neck cases a seven-beam configuration turns out to be superior to a nine-beam configuration if the highest priority is the sparing of the spinal cord. The presented method of comparing Pareto sets is not restricted to comparing different beam angle configurations, but will allow for more comprehensive comparisons of competing treatment techniques (e.g. photons versus protons) than with the classical method of comparing single treatment plans.

Comparative analysis of Pareto surfaces in multi-criteria IMRT planning.

Science.gov (United States)

Teichert, K; Süss, P; Serna, J I; Monz, M; Küfer, K H; Thieke, C

2011-06-21

In the multi-criteria optimization approach to IMRT planning, a given dose distribution is evaluated by a number of convex objective functions that measure tumor coverage and sparing of the different organs at risk. Within this context optimizing the intensity profiles for any fixed set of beams yields a convex Pareto set in the objective space. However, if the number of beam directions and irradiation angles are included as free parameters in the formulation of the optimization problem, the resulting Pareto set becomes more intricate. In this work, a method is presented that allows for the comparison of two convex Pareto sets emerging from two distinct beam configuration choices. For the two competing beam settings, the non-dominated and the dominated points of the corresponding Pareto sets are identified and the distance between the two sets in the objective space is calculated and subsequently plotted. The obtained information enables the planner to decide if, for a given compromise, the current beam setup is optimal. He may then re-adjust his choice accordingly during navigation. The method is applied to an artificial case and two clinical head neck cases. In all cases no configuration is dominating its competitor over the whole Pareto set. For example, in one of the head neck cases a seven-beam configuration turns out to be superior to a nine-beam configuration if the highest priority is the sparing of the spinal cord. The presented method of comparing Pareto sets is not restricted to comparing different beam angle configurations, but will allow for more comprehensive comparisons of competing treatment techniques (e.g., photons versus protons) than with the classical method of comparing single treatment plans.

Comparative analysis of Pareto surfaces in multi-criteria IMRT planning

International Nuclear Information System (INIS)

Teichert, K; Suess, P; Serna, J I; Monz, M; Kuefer, K H; Thieke, C

2011-01-01

In the multi-criteria optimization approach to IMRT planning, a given dose distribution is evaluated by a number of convex objective functions that measure tumor coverage and sparing of the different organs at risk. Within this context optimizing the intensity profiles for any fixed set of beams yields a convex Pareto set in the objective space. However, if the number of beam directions and irradiation angles are included as free parameters in the formulation of the optimization problem, the resulting Pareto set becomes more intricate. In this work, a method is presented that allows for the comparison of two convex Pareto sets emerging from two distinct beam configuration choices. For the two competing beam settings, the non-dominated and the dominated points of the corresponding Pareto sets are identified and the distance between the two sets in the objective space is calculated and subsequently plotted. The obtained information enables the planner to decide if, for a given compromise, the current beam setup is optimal. He may then re-adjust his choice accordingly during navigation. The method is applied to an artificial case and two clinical head neck cases. In all cases no configuration is dominating its competitor over the whole Pareto set. For example, in one of the head neck cases a seven-beam configuration turns out to be superior to a nine-beam configuration if the highest priority is the sparing of the spinal cord. The presented method of comparing Pareto sets is not restricted to comparing different beam angle configurations, but will allow for more comprehensive comparisons of competing treatment techniques (e.g. photons versus protons) than with the classical method of comparing single treatment plans.

Evolutionary tradeoffs, Pareto optimality and the morphology of ammonite shells.

Science.gov (United States)

Tendler, Avichai; Mayo, Avraham; Alon, Uri

2015-03-07

Organisms that need to perform multiple tasks face a fundamental tradeoff: no design can be optimal at all tasks at once. Recent theory based on Pareto optimality showed that such tradeoffs lead to a highly defined range of phenotypes, which lie in low-dimensional polyhedra in the space of traits. The vertices of these polyhedra are called archetypes- the phenotypes that are optimal at a single task. To rigorously test this theory requires measurements of thousands of species over hundreds of millions of years of evolution. Ammonoid fossil shells provide an excellent model system for this purpose. Ammonoids have a well-defined geometry that can be parameterized using three dimensionless features of their logarithmic-spiral-shaped shells. Their evolutionary history includes repeated mass extinctions. We find that ammonoids fill out a pyramid in morphospace, suggesting five specific tasks - one for each vertex of the pyramid. After mass extinctions, surviving species evolve to refill essentially the same pyramid, suggesting that the tasks are unchanging. We infer putative tasks for each archetype, related to economy of shell material, rapid shell growth, hydrodynamics and compactness. These results support Pareto optimality theory as an approach to study evolutionary tradeoffs, and demonstrate how this approach can be used to infer the putative tasks that may shape the natural selection of phenotypes.

The application of analytical methods to the study of Pareto - optimal control systems

Directory of Open Access Journals (Sweden)

I. K. Romanova

2014-01-01

Full Text Available The subject of research articles - - methods of multicriteria optimization and their application for parametric synthesis of double-circuit control systems in conditions of inconsistency of individual criteria. The basis for solving multicriteria problems is a fundamental principle of a multi-criteria choice - the principle of the Edgeworth - Pareto. Getting Pareto - optimal variants due to inconsistency of individual criteria does not mean reaching a final decision. Set these options only offers the designer (DM.An important issue when using traditional numerical methods is their computational cost. An example is the use of methods of sounding the parameter space, including with use of uniform grids and uniformly distributed sequences. Very complex computational task is the application of computer methods of approximation bounds of Pareto.The purpose of this work is the development of a fairly simple search methods of Pareto - optimal solutions for the case of the criteria set out in the analytical form.The proposed solution is based on the study of the properties of the analytical dependences of criteria. The case is not covered so far in the literature, namely, the topology of the task, in which no touch of indifference curves (lines level. It is shown that for such tasks may be earmarked for compromise solutions. Prepositional use of the angular position of antigradient to the indifference curves in the parameter space relative to the coordinate axes. Formulated propositions on the characteristics of comonotonicity and contramonotonicity and angular characteristics of antigradient to determine Pareto optimal solutions. Considers the General algorithm of calculation: determine the scope of permissible values of parameters; investigates properties comonotonicity and contraventanas; to build an equal level (indifference curves; determined touch type: single sided (task is not strictly multicriteria or bilateral (objective relates to the Pareto

Multi-objective genetic algorithm optimization of 2D- and 3D-Pareto fronts for vibrational quantum processes

International Nuclear Information System (INIS)

Gollub, C; De Vivie-Riedle, R

2009-01-01

A multi-objective genetic algorithm is applied to optimize picosecond laser fields, driving vibrational quantum processes. Our examples are state-to-state transitions and unitary transformations. The approach allows features of the shaped laser fields and of the excitation mechanisms to be controlled simultaneously with the quantum yield. Within the parameter range accessible to the experiment, we focus on short pulse durations and low pulse energies to optimize preferably robust laser fields. Multidimensional Pareto fronts for these conflicting objectives could be constructed. Comparison with previous work showed that the solutions from Pareto optimizations and from optimal control theory match very well.

Necessary and Sufficient Conditions for Pareto Optimality in Infinite Horizon Cooperative Differential Games - Replaced by CentER DP 2011-041

NARCIS (Netherlands)

Reddy, P.V.; Engwerda, J.C.

2010-01-01

In this article we derive necessary and sufficient conditions for the existence of Pareto optimal solutions for an N player cooperative infinite horizon differential game. Firstly, we write the problem of finding Pareto candidates as solving N constrained optimal control subproblems. We derive some

Pareto optimization of an industrial ecosystem: sustainability maximization

Directory of Open Access Journals (Sweden)

J. G. M.-S. Monteiro

2010-09-01

Full Text Available This work investigates a procedure to design an Industrial Ecosystem for sequestrating CO2 and consuming glycerol in a Chemical Complex with 15 integrated processes. The Complex is responsible for the production of methanol, ethylene oxide, ammonia, urea, dimethyl carbonate, ethylene glycol, glycerol carbonate, β-carotene, 1,2-propanediol and olefins, and is simulated using UNISIM Design (Honeywell. The process environmental impact (EI is calculated using the Waste Reduction Algorithm, while Profit (P is estimated using classic cost correlations. MATLAB (The Mathworks Inc is connected to UNISIM to enable optimization. The objective is granting maximum process sustainability, which involves finding a compromise between high profitability and low environmental impact. Sustainability maximization is therefore understood as a multi-criteria optimization problem, addressed by means of the Pareto optimization methodology for trading off P vs. EI.

On the construction of experimental designs for a given task by jointly optimizing several quality criteria: Pareto-optimal experimental designs.

Science.gov (United States)

Sánchez, M S; Sarabia, L A; Ortiz, M C

2012-11-19

Experimental designs for a given task should be selected on the base of the problem being solved and of some criteria that measure their quality. There are several such criteria because there are several aspects to be taken into account when making a choice. The most used criteria are probably the so-called alphabetical optimality criteria (for example, the A-, E-, and D-criteria related to the joint estimation of the coefficients, or the I- and G-criteria related to the prediction variance). Selecting a proper design to solve a problem implies finding a balance among these several criteria that measure the performance of the design in different aspects. Technically this is a problem of multi-criteria optimization, which can be tackled from different views. The approach presented here addresses the problem in its real vector nature, so that ad hoc experimental designs are generated with an algorithm based on evolutionary algorithms to find the Pareto-optimal front. There is not theoretical limit to the number of criteria that can be studied and, contrary to other approaches, no just one experimental design is computed but a set of experimental designs all of them with the property of being Pareto-optimal in the criteria needed by the user. Besides, the use of an evolutionary algorithm makes it possible to search in both continuous and discrete domains and avoid the need of having a set of candidate points, usual in exchange algorithms. Copyright © 2012 Elsevier B.V. All rights reserved.

Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array.

Science.gov (United States)

Abdul Rani, Khairul Najmy; Abdulmalek, Mohamedfareq; A Rahim, Hasliza; Siew Chin, Neoh; Abd Wahab, Alawiyah

2017-04-20

This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler-Deb-Thiele's (ZDT's) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously.

Phase transitions in Pareto optimal complex networks.

Science.gov (United States)

Seoane, Luís F; Solé, Ricard

2015-09-01

The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.

Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network

DEFF Research Database (Denmark)

Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar

2013-01-01

Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines...... and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four...... well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable...

Discrepancies between selected Pareto optimal plans and final deliverable plans in radiotherapy multi-criteria optimization.

Science.gov (United States)

Kyroudi, Archonteia; Petersson, Kristoffer; Ghandour, Sarah; Pachoud, Marc; Matzinger, Oscar; Ozsahin, Mahmut; Bourhis, Jean; Bochud, François; Moeckli, Raphaël

2016-08-01

Multi-criteria optimization provides decision makers with a range of clinical choices through Pareto plans that can be explored during real time navigation and then converted into deliverable plans. Our study shows that dosimetric differences can arise between the two steps, which could compromise the clinical choices made during navigation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space

Science.gov (United States)

Szekely, Pablo; Korem, Yael; Moran, Uri; Mayo, Avi; Alon, Uri

2015-01-01

When organisms need to perform multiple tasks they face a fundamental tradeoff: no phenotype can be optimal at all tasks. This situation was recently analyzed using Pareto optimality, showing that tradeoffs between tasks lead to phenotypes distributed on low dimensional polygons in trait space. The vertices of these polygons are archetypes—phenotypes optimal at a single task. This theory was applied to examples from animal morphology and gene expression. Here we ask whether Pareto optimality theory can apply to life history traits, which include longevity, fecundity and mass. To comprehensively explore the geometry of life history trait space, we analyze a dataset of life history traits of 2105 endothermic species. We find that, to a first approximation, life history traits fall on a triangle in log-mass log-longevity space. The vertices of the triangle suggest three archetypal strategies, exemplified by bats, shrews and whales, with specialists near the vertices and generalists in the middle of the triangle. To a second approximation, the data lies in a tetrahedron, whose extra vertex above the mass-longevity triangle suggests a fourth strategy related to carnivory. Each animal species can thus be placed in a coordinate system according to its distance from the archetypes, which may be useful for genome-scale comparative studies of mammalian aging and other biological aspects. We further demonstrate that Pareto optimality can explain a range of previous studies which found animal and plant phenotypes which lie in triangles in trait space. This study demonstrates the applicability of multi-objective optimization principles to understand life history traits and to infer archetypal strategies that suggest why some mammalian species live much longer than others of similar mass. PMID:26465336

The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space.

Science.gov (United States)

Szekely, Pablo; Korem, Yael; Moran, Uri; Mayo, Avi; Alon, Uri

2015-10-01

When organisms need to perform multiple tasks they face a fundamental tradeoff: no phenotype can be optimal at all tasks. This situation was recently analyzed using Pareto optimality, showing that tradeoffs between tasks lead to phenotypes distributed on low dimensional polygons in trait space. The vertices of these polygons are archetypes--phenotypes optimal at a single task. This theory was applied to examples from animal morphology and gene expression. Here we ask whether Pareto optimality theory can apply to life history traits, which include longevity, fecundity and mass. To comprehensively explore the geometry of life history trait space, we analyze a dataset of life history traits of 2105 endothermic species. We find that, to a first approximation, life history traits fall on a triangle in log-mass log-longevity space. The vertices of the triangle suggest three archetypal strategies, exemplified by bats, shrews and whales, with specialists near the vertices and generalists in the middle of the triangle. To a second approximation, the data lies in a tetrahedron, whose extra vertex above the mass-longevity triangle suggests a fourth strategy related to carnivory. Each animal species can thus be placed in a coordinate system according to its distance from the archetypes, which may be useful for genome-scale comparative studies of mammalian aging and other biological aspects. We further demonstrate that Pareto optimality can explain a range of previous studies which found animal and plant phenotypes which lie in triangles in trait space. This study demonstrates the applicability of multi-objective optimization principles to understand life history traits and to infer archetypal strategies that suggest why some mammalian species live much longer than others of similar mass.

Necessary and Sufficient Conditions for Pareto Optimality in Infinite Horizon Cooperative Differential Games

NARCIS (Netherlands)

Reddy, P.V.; Engwerda, J.C.

2011-01-01

In this article we derive necessary and sufficient conditions for the existence of Pareto optimal solutions for infinite horizon cooperative differential games. We consider games defined by non autonomous and discounted autonomous systems. The obtained results are used to analyze the regular

The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space.

Directory of Open Access Journals (Sweden)

Pablo Szekely

2015-10-01

Full Text Available When organisms need to perform multiple tasks they face a fundamental tradeoff: no phenotype can be optimal at all tasks. This situation was recently analyzed using Pareto optimality, showing that tradeoffs between tasks lead to phenotypes distributed on low dimensional polygons in trait space. The vertices of these polygons are archetypes--phenotypes optimal at a single task. This theory was applied to examples from animal morphology and gene expression. Here we ask whether Pareto optimality theory can apply to life history traits, which include longevity, fecundity and mass. To comprehensively explore the geometry of life history trait space, we analyze a dataset of life history traits of 2105 endothermic species. We find that, to a first approximation, life history traits fall on a triangle in log-mass log-longevity space. The vertices of the triangle suggest three archetypal strategies, exemplified by bats, shrews and whales, with specialists near the vertices and generalists in the middle of the triangle. To a second approximation, the data lies in a tetrahedron, whose extra vertex above the mass-longevity triangle suggests a fourth strategy related to carnivory. Each animal species can thus be placed in a coordinate system according to its distance from the archetypes, which may be useful for genome-scale comparative studies of mammalian aging and other biological aspects. We further demonstrate that Pareto optimality can explain a range of previous studies which found animal and plant phenotypes which lie in triangles in trait space. This study demonstrates the applicability of multi-objective optimization principles to understand life history traits and to infer archetypal strategies that suggest why some mammalian species live much longer than others of similar mass.

Pareto-optimal phylogenetic tree reconciliation.

Science.gov (United States)

Libeskind-Hadas, Ran; Wu, Yi-Chieh; Bansal, Mukul S; Kellis, Manolis

2014-06-15

Phylogenetic tree reconciliation is a widely used method for reconstructing the evolutionary histories of gene families and species, hosts and parasites and other dependent pairs of entities. Reconciliation is typically performed using maximum parsimony, in which each evolutionary event type is assigned a cost and the objective is to find a reconciliation of minimum total cost. It is generally understood that reconciliations are sensitive to event costs, but little is understood about the relationship between event costs and solutions. Moreover, choosing appropriate event costs is a notoriously difficult problem. We address this problem by giving an efficient algorithm for computing Pareto-optimal sets of reconciliations, thus providing the first systematic method for understanding the relationship between event costs and reconciliations. This, in turn, results in new techniques for computing event support values and, for cophylogenetic analyses, performing robust statistical tests. We provide new software tools and demonstrate their use on a number of datasets from evolutionary genomic and cophylogenetic studies. Our Python tools are freely available at www.cs.hmc.edu/∼hadas/xscape. . © The Author 2014. Published by Oxford University Press.

A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

International Nuclear Information System (INIS)

Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

2016-01-01

Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

Statement of Problem of Pareto Frontier Management and Its Solution in the Analysis and Synthesis of Optimal Systems

Directory of Open Access Journals (Sweden)

I. K. Romanova

2015-01-01

Full Text Available The article research concerns the multi-criteria optimization (MCO, which assumes that operation quality criteria of the system are independent and specifies a way to improve values of these criteria. Mutual contradiction of some criteria is a major problem in MCO. One of the most important areas of research is to obtain the so-called Pareto - optimal options.The subject of research is Pareto front, also called the Pareto frontier. The article discusses front classifications by its geometric representation for the case of two-criterion task. It presents a mathematical description of the front characteristics using the gradients and their projections. A review of current domestic and foreign literature has revealed that the aim of works in constructing the Pareto frontier is to conduct research in conditions of uncertainty, in the stochastic statement, with no restrictions. A topology both in two- and in three-dimensional case is under consideration. The targets of modern applications are multi-agent systems and groups of players in differential games. However, all considered works have no task to provide an active management of the front.The objective of this article is to discuss the research problem the Pareto frontier in a new production, namely, with the active co-developers of the systems and (or the decision makers (DM in the management of the Pareto frontier. It notes that such formulation differs from the traditionally accepted approach based on the analysis of already existing solutions.The article discusses three ways to describe a quality of the object management system. The first way is to use the direct quality criteria for the model of a closed system as the vibrational level of the General form. The second one is to study a specific two-loop system of an aircraft control using the angular velocity and normal acceleration loops. The third is the use of the integrated quality criteria. In all three cases, the selected criteria are

Pareto-optimal electricity tariff rates in the Republic of Armenia

International Nuclear Information System (INIS)

Kaiser, M.J.

2000-01-01

The economic impact of electricity tariff rates on the residential sector of Yerevan, Armenia, is examined. The effect of tariff design on revenue generation and equity measures is considered, and the combination of energy pricing and compensatory social policies which provides the best mix of efficiency and protection for poor households is examined. An equity measure is defined in terms of a cumulative distribution function which describes the percent of the population that spends x percent or less of their income on electricity consumption. An optimal (Pareto-efficient) tariff is designed based on the analysis of survey data and an econometric model, and the Armenian tariff rate effective 1 January 1997 to 15 September 1997 is shown to be non-optimal relative to this rate. 22 refs

A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization

Czech Academy of Sciences Publication Activity Database

Hájek, J.; Szöllös, A.; Šístek, Jakub

2010-01-01

Roč. 41, 7-8 (2010), s. 1031-1057 ISSN 0965-9978 R&D Projects: GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z10190503 Keywords : multi-objective optimization * micro-genetic algorithm * diversity * Pareto archive Subject RIV: BA - General Mathematics Impact factor: 1.004, year: 2010 http://www.sciencedirect.com/science/article/pii/S0965997810000451

A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization

Czech Academy of Sciences Publication Activity Database

Hájek, J.; Szöllös, A.; Šístek, Jakub

2010-01-01

Roč. 41, 7-8 (2010), s. 1031-1057 ISSN 0965-9978 R&D Projects: GA AV ČR IAA100760702 Institutional research plan: CEZ:AV0Z10190503 Keywords : multi-objective optimization * micro- genetic algorithm * diversity * Pareto archive Subject RIV: BA - General Mathematics Impact factor: 1.004, year: 2010 http://www.sciencedirect.com/science/article/pii/S0965997810000451

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

Science.gov (United States)

A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...

A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

Science.gov (United States)

Danandeh Mehr, Ali; Kahya, Ercan

2017-06-01

Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms

International Nuclear Information System (INIS)

Amanifard, N.; Nariman-Zadeh, N.; Borji, M.; Khalkhali, A.; Habibdoust, A.

2008-01-01

Three-dimensional heat transfer characteristics and pressure drop of water flow in a set of rectangular microchannels are numerically investigated using Fluent and compared with those of experimental results. Two metamodels based on the evolved group method of data handling (GMDH) type neural networks are then obtained for modelling of both pressure drop (ΔP) and Nusselt number (Nu) with respect to design variables such as geometrical parameters of microchannels, the amount of heat flux and the Reynolds number. Using such obtained polynomial neural networks, multi-objective genetic algorithms (GAs) (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism is then used for Pareto based optimization of microchannels considering two conflicting objectives such as (ΔP) and (Nu). It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of microchannels can be discovered by Pareto based multi-objective optimization of the obtained polynomial metamodels representing their heat transfer and flow characteristics. Such important optimal principles would not have been obtained without the use of both GMDH type neural network modelling and the Pareto optimization approach

Optimal Reinsurance Design for Pareto Optimum: From the Perspective of Multiple Reinsurers

Directory of Open Access Journals (Sweden)

Xing Rong

2016-01-01

Full Text Available This paper investigates optimal reinsurance strategies for an insurer which cedes the insured risk to multiple reinsurers. Assume that the insurer and every reinsurer apply the coherent risk measures. Then, we find out the necessary and sufficient conditions for the reinsurance market to achieve Pareto optimum; that is, every ceded-loss function and the retention function are in the form of “multiple layers reinsurance.”

Searching for the Pareto frontier in multi-objective protein design.

Science.gov (United States)

Nanda, Vikas; Belure, Sandeep V; Shir, Ofer M

2017-08-01

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

Pareto-path multitask multiple kernel learning.

Science.gov (United States)

Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

2015-01-01

A traditional and intuitively appealing Multitask Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing among the tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a multiobjective optimization problem, which considers the concurrent optimization of all task objectives involved in the Multitask Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel support vector machine MT-MKL framework that considers an implicitly defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using the algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving a better classification performance, when compared with other similar MTL approaches.

PARETO OPTIMAL SOLUTIONS FOR MULTI-OBJECTIVE GENERALIZED ASSIGNMENT PROBLEM

Directory of Open Access Journals (Sweden)

S. Prakash

2012-01-01

Full Text Available

ENGLISH ABSTRACT: The Multi-Objective Generalized Assignment Problem (MGAP with two objectives, where one objective is linear and the other one is non-linear, has been considered, with the constraints that a job is assigned to only one worker – though he may be assigned more than one job, depending upon the time available to him. An algorithm is proposed to find the set of Pareto optimal solutions of the problem, determining assignments of jobs to workers with two objectives without setting priorities for them. The two objectives are to minimise the total cost of the assignment and to reduce the time taken to complete all the jobs.

AFRIKAANSE OPSOMMING: ‘n Multi-doelwit veralgemeende toekenningsprobleem (“multi-objective generalised assignment problem – MGAP” met twee doelwitte, waar die een lineêr en die ander nielineêr is nie, word bestudeer, met die randvoorwaarde dat ‘n taak slegs toegedeel word aan een werker – alhoewel meer as een taak aan hom toegedeel kan word sou die tyd beskikbaar wees. ‘n Algoritme word voorgestel om die stel Pareto-optimale oplossings te vind wat die taaktoedelings aan werkers onderhewig aan die twee doelwitte doen sonder dat prioriteite toegeken word. Die twee doelwitte is om die totale koste van die opdrag te minimiseer en om die tyd te verminder om al die take te voltooi.

Pareto-Optimal Estimates of California Precipitation Change

Science.gov (United States)

Langenbrunner, Baird; Neelin, J. David

2017-12-01

In seeking constraints on global climate model projections under global warming, one commonly finds that different subsets of models perform well under different objective functions, and these trade-offs are difficult to weigh. Here a multiobjective approach is applied to a large set of subensembles generated from the Climate Model Intercomparison Project phase 5 ensemble. We use observations and reanalyses to constrain tropical Pacific sea surface temperatures, upper level zonal winds in the midlatitude Pacific, and California precipitation. An evolutionary algorithm identifies the set of Pareto-optimal subensembles across these three measures, and these subensembles are used to constrain end-of-century California wet season precipitation change. This methodology narrows the range of projections throughout California, increasing confidence in estimates of positive mean precipitation change. Finally, we show how this technique complements and generalizes emergent constraint approaches for restricting uncertainty in end-of-century projections within multimodel ensembles using multiple criteria for observational constraints.

Many-objective thermodynamic optimization of Stirling heat engine

International Nuclear Information System (INIS)

Patel, Vivek; Savsani, Vimal; Mudgal, Anurag

2017-01-01

This paper presents a rigorous investigation of many-objective (four-objective) thermodynamic optimization of a Stirling heat engine. Many-objective optimization problem is formed by considering maximization of thermal efficiency, power output, ecological function and exergy efficiency. Multi-objective heat transfer search (MOHTS) algorithm is proposed and applied to obtain a set of Pareto-optimal points. Many objective optimization results form a solution in a four dimensional hyper objective space and for visualization it is represented on a two dimension objective space. Thus, results of four-objective optimization are represented by six Pareto fronts in two dimension objective space. These six Pareto fronts are compared with their corresponding two-objective Pareto fronts. Quantitative assessment of the obtained Pareto solutions is reported in terms of spread and the spacing measures. Different decision making approaches such as LINMAP, TOPSIS and fuzzy are used to select a final optimal solution from Pareto optimal set of many-objective optimization. Finally, to reveal the level of conflict between these objectives, distribution of each decision variable in their allowable range is also shown in two dimensional objective spaces. - Highlights: • Many-objective (i.e. four objective) optimization of Stirling engine is investigated. • MOHTS algorithm is introduced and applied to obtain a set of Pareto points. • Comparative results of many-objective and multi-objectives are presented. • Relationship of design variables in many-objective optimization are obtained. • Optimum solution is selected by using decision making approaches.

Using Pareto optimality to explore the topology and dynamics of the human connectome.

Science.gov (United States)

Avena-Koenigsberger, Andrea; Goñi, Joaquín; Betzel, Richard F; van den Heuvel, Martijn P; Griffa, Alessandra; Hagmann, Patric; Thiran, Jean-Philippe; Sporns, Olaf

2014-10-05

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an 'economical' small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.

Optimal beam margins in linac-based VMAT stereotactic ablative body radiotherapy: a Pareto front analysis for liver metastases.

Science.gov (United States)

Cilla, Savino; Ianiro, Anna; Deodato, Francesco; Macchia, Gabriella; Digesù, Cinzia; Valentini, Vincenzo; Morganti, Alessio G

2017-11-27

We explored the Pareto fronts mathematical strategy to determine the optimal block margin and prescription isodose for stereotactic body radiotherapy (SBRT) treatments of liver metastases using the volumetric-modulated arc therapy (VMAT) technique. Three targets (planning target volumes [PTVs] = 20, 55, and 101 cc) were selected. A single fraction dose of 26 Gy was prescribed (prescription dose [PD]). VMAT plans were generated for 3 different beam energies. Pareto fronts based on (1) different multileaf collimator (MLC) block margin around PTV and (2) different prescription isodose lines (IDL) were produced. For each block margin, the greatest IDL fulfilling the criteria (95% of PTV reached 100%) was considered as providing the optimal clinical plan for PTV coverage. Liver D mean , V7Gy, and V12Gy were used against the PTV coverage to generate the fronts. Gradient indexes (GI and mGI), homogeneity index (HI), and healthy liver irradiation in terms of D mean , V7Gy, and V12Gy were calculated to compare different plans. In addition, each target was also optimized with a full-inverse planning engine to obtain a direct comparison with anatomy-based treatment planning system (TPS) results. About 900 plans were calculated to generate the fronts. GI and mGI show a U-shaped behavior as a function of beam margin with minimal values obtained with a +1 mm MLC margin. For these plans, the IDL ranges from 74% to 86%. GI and mGI show also a V-shaped behavior with respect to HI index, with minimum values at 1 mm for all metrics, independent of tumor dimensions and beam energy. Full-inversed optimized plans reported worse results with respect to Pareto plans. In conclusion, Pareto fronts provide a rigorous strategy to choose clinical optimal plans in SBRT treatments. We show that a 1-mm MLC block margin provides the best results with regard to healthy liver tissue irradiation and steepness of dose fallout. Copyright © 2017 American Association of Medical Dosimetrists

Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach.

Science.gov (United States)

Huang, Adam; Li, Jiang; Summers, Ronald M; Petrick, Nicholas; Hara, Amy K

2010-03-21

We investigated a Pareto front approach to improving polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4 to 60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (pPareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms.

TopN-Pareto Front Search

Energy Technology Data Exchange (ETDEWEB)

2016-12-21

The JMP Add-In TopN-PFS provides an automated tool for finding layered Pareto front to identify the top N solutions from an enumerated list of candidates subject to optimizing multiple criteria. The approach constructs the N layers of Pareto fronts, and then provides a suite of graphical tools to explore the alternatives based on different prioritizations of the criteria. The tool is designed to provide a set of alternatives from which the decision-maker can select the best option for their study goals.

Household Labour Supply in Britain and Denmark: Some Interpretations Using a Model of Pareto Optimal Behaviour

DEFF Research Database (Denmark)

Barmby, Tim; Smith, Nina

1996-01-01

This paper analyses the labour supply behaviour of households in Denmark and Britain. It employs models in which the preferences of individuals within the household are explicitly represented. The households are then assumed to decide on their labour supply in a Pareto-Optimal fashion. Describing...

Kullback-Leibler divergence and the Pareto-Exponential approximation.

Science.gov (United States)

Weinberg, G V

2016-01-01

Recent radar research interests in the Pareto distribution as a model for X-band maritime surveillance radar clutter returns have resulted in analysis of the asymptotic behaviour of this clutter model. In particular, it is of interest to understand when the Pareto distribution is well approximated by an Exponential distribution. The justification for this is that under the latter clutter model assumption, simpler radar detection schemes can be applied. An information theory approach is introduced to investigate the Pareto-Exponential approximation. By analysing the Kullback-Leibler divergence between the two distributions it is possible to not only assess when the approximation is valid, but to determine, for a given Pareto model, the optimal Exponential approximation.

Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

Science.gov (United States)

Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

2018-03-01

In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

The feasibility of using Pareto fronts for comparison of treatment planning systems and delivery techniques

International Nuclear Information System (INIS)

Ottosson, Rickard O.; Sjoestroem, David; Behrens, Claus F.; Karlsson, Anna; Engstroem, Per E.; Knoeoes, Tommy; Ceberg, Crister

2009-01-01

Pareto optimality is a concept that formalises the trade-off between a given set of mutually contradicting objectives. A solution is said to be Pareto optimal when it is not possible to improve one objective without deteriorating at least one of the other. A set of Pareto optimal solutions constitute the Pareto front. The Pareto concept applies well to the inverse planning process, which involves inherently contradictory objectives, high and uniform target dose on one hand, and sparing of surrounding tissue and nearby organs at risk (OAR) on the other. Due to the specific characteristics of a treatment planning system (TPS), treatment strategy or delivery technique, Pareto fronts for a given case are likely to differ. The aim of this study was to investigate the feasibility of using Pareto fronts as a comparative tool for TPSs, treatment strategies and delivery techniques. In order to sample Pareto fronts, multiple treatment plans with varying target conformity and dose sparing of OAR were created for a number of prostate and head and neck IMRT cases. The DVHs of each plan were evaluated with respect to target coverage and dose to relevant OAR. Pareto fronts were successfully created for all studied cases. The results did indeed follow the definition of the Pareto concept, i.e. dose sparing of the OAR could not be improved without target coverage being impaired or vice versa. Furthermore, various treatment techniques resulted in distinguished and well separated Pareto fronts. Pareto fronts may be used to evaluate a number of parameters within radiotherapy. Examples are TPS optimization algorithms, the variation between accelerators or delivery techniques and the degradation of a plan during the treatment planning process. The issue of designing a model for unbiased comparison of parameters with such large inherent discrepancies, e.g. different TPSs, is problematic and should be carefully considered

The feasibility of using Pareto fronts for comparison of treatment planning systems and delivery techniques.

Science.gov (United States)

Ottosson, Rickard O; Engstrom, Per E; Sjöström, David; Behrens, Claus F; Karlsson, Anna; Knöös, Tommy; Ceberg, Crister

2009-01-01

Pareto optimality is a concept that formalises the trade-off between a given set of mutually contradicting objectives. A solution is said to be Pareto optimal when it is not possible to improve one objective without deteriorating at least one of the other. A set of Pareto optimal solutions constitute the Pareto front. The Pareto concept applies well to the inverse planning process, which involves inherently contradictory objectives, high and uniform target dose on one hand, and sparing of surrounding tissue and nearby organs at risk (OAR) on the other. Due to the specific characteristics of a treatment planning system (TPS), treatment strategy or delivery technique, Pareto fronts for a given case are likely to differ. The aim of this study was to investigate the feasibility of using Pareto fronts as a comparative tool for TPSs, treatment strategies and delivery techniques. In order to sample Pareto fronts, multiple treatment plans with varying target conformity and dose sparing of OAR were created for a number of prostate and head & neck IMRT cases. The DVHs of each plan were evaluated with respect to target coverage and dose to relevant OAR. Pareto fronts were successfully created for all studied cases. The results did indeed follow the definition of the Pareto concept, i.e. dose sparing of the OAR could not be improved without target coverage being impaired or vice versa. Furthermore, various treatment techniques resulted in distinguished and well separated Pareto fronts. Pareto fronts may be used to evaluate a number of parameters within radiotherapy. Examples are TPS optimization algorithms, the variation between accelerators or delivery techniques and the degradation of a plan during the treatment planning process. The issue of designing a model for unbiased comparison of parameters with such large inherent discrepancies, e.g. different TPSs, is problematic and should be carefully considered.

Exploring the Environment/Energy Pareto Optimal Front of an Office Room Using Computational Fluid Dynamics-Based Interactive Optimization Method

Directory of Open Access Journals (Sweden)

Kangji Li

2017-02-01

Full Text Available This paper is concerned with the development of a high-resolution and control-friendly optimization framework in enclosed environments that helps improve thermal comfort, indoor air quality (IAQ, and energy costs of heating, ventilation and air conditioning (HVAC system simultaneously. A computational fluid dynamics (CFD-based optimization method which couples algorithms implemented in Matlab with CFD simulation is proposed. The key part of this method is a data interactive mechanism which efficiently passes parameters between CFD simulations and optimization functions. A two-person office room is modeled for the numerical optimization. The multi-objective evolutionary algorithm—non-dominated-and-crowding Sorting Genetic Algorithm II (NSGA-II—is realized to explore the environment/energy Pareto front of the enclosed space. Performance analysis will demonstrate the effectiveness of the presented optimization method.

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.

Science.gov (United States)

Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph

2015-01-01

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin β4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

Multiobjective optimization of the inspection intervals of a nuclear safety system: A clustering-based framework for reducing the Pareto Front

International Nuclear Information System (INIS)

Zio, E.; Bazzo, R.

2010-01-01

In this paper, a framework is developed for identifying a limited number of representative solutions of a multiobjective optimization problem concerning the inspection intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are first clustered into 'families', which are then synthetically represented by a 'head of the family' solution. Three clustering methods are analyzed. Level Diagrams are then used to represent, analyse and interpret the Pareto Fronts reduced to their head-of-the-family solutions. Two decision situations are considered: without or with decision maker preferences, the latter implying the introduction of a scoring system to rank the solutions with respect to the different objectives: a fuzzy preference assignment is then employed to this purpose. The results of the application of the framework of analysis to the problem of optimizing the inspection intervals of a nuclear power plant safety system show that the clustering-based reduction maintains the Pareto Front shape and relevant characteristics, while making it easier for the decision maker to select the final solution.

Using Pareto points for model identification in predictive toxicology

Science.gov (United States)

2013-01-01

Predictive toxicology is concerned with the development of models that are able to predict the toxicity of chemicals. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in cosmetics, drug design or food protection to speed up the process of chemical compound discovery while reducing the need for lab tests. There is an extensive literature associated with the best practice of model generation and data integration but management and automated identification of relevant models from available collections of models is still an open problem. Currently, the decision on which model should be used for a new chemical compound is left to users. This paper intends to initiate the discussion on automated model identification. We present an algorithm, based on Pareto optimality, which mines model collections and identifies a model that offers a reliable prediction for a new chemical compound. The performance of this new approach is verified for two endpoints: IGC50 and LogP. The results show a great potential for automated model identification methods in predictive toxicology. PMID:23517649

Simultaneous navigation of multiple Pareto surfaces, with an application to multicriteria IMRT planning with multiple beam angle configurations.

Science.gov (United States)

Craft, David; Monz, Michael

2010-02-01

To introduce a method to simultaneously explore a collection of Pareto surfaces. The method will allow radiotherapy treatment planners to interactively explore treatment plans for different beam angle configurations as well as different treatment modalities. The authors assume a convex optimization setting and represent the Pareto surface for each modality or given beam set by a set of discrete points on the surface. Weighted averages of these discrete points produce a continuous representation of each Pareto surface. The authors calculate a set of Pareto surfaces and use linear programming to navigate across the individual surfaces, allowing switches between surfaces. The switches are organized such that the plan profits in the requested way, while trying to keep the change in dose as small as possible. The system is demonstrated on a phantom pancreas IMRT case using 100 different five beam configurations and a multicriteria formulation with six objectives. The system has intuitive behavior and is easy to control. Also, because the underlying linear programs are small, the system is fast enough to offer real-time exploration for the Pareto surfaces of the given beam configurations. The system presented offers a sound starting point for building clinical systems for multicriteria exploration of different modalities and offers a controllable way to explore hundreds of beam angle configurations in IMRT planning, allowing the users to focus their attention on the dose distribution and treatment planning objectives instead of spending excessive time on the technicalities of delivery.

Evaluation of the optimal combinations of modulation factor and pitch for Helical TomoTherapy plans made with TomoEdge using Pareto optimal fronts.

Science.gov (United States)

De Kerf, Geert; Van Gestel, Dirk; Mommaerts, Lobke; Van den Weyngaert, Danielle; Verellen, Dirk

2015-09-17

Modulation factor (MF) and pitch have an impact on Helical TomoTherapy (HT) plan quality and HT users mostly use vendor-recommended settings. This study analyses the effect of these two parameters on both plan quality and treatment time for plans made with TomoEdge planning software by using the concept of Pareto optimal fronts. More than 450 plans with different combinations of pitch [0.10-0.50] and MF [1.2-3.0] were produced. These HT plans, with a field width (FW) of 5 cm, were created for five head and neck patients and homogeneity index, conformity index, dose-near-maximum (D2), and dose-near-minimum (D98) were analysed for the planning target volumes, as well as the mean dose and D2 for most critical organs at risk. For every dose metric the median value will be plotted against treatment time. A Pareto-like method is used in the analysis which will show how pitch and MF influence both treatment time and plan quality. For small pitches (≤0.20), MF does not influence treatment time. The contrary is true for larger pitches (≥0.25) as lowering MF will both decrease treatment time and plan quality until maximum gantry speed is reached. At this moment, treatment time is saturated and only plan quality will further decrease. The Pareto front analysis showed optimal combinations of pitch [0.23-0.45] and MF > 2.0 for a FW of 5 cm. Outside this range, plans will become less optimal. As the vendor-recommended settings fall within this range, the use of these settings is validated.

Pareto front estimation for decision making.

Science.gov (United States)

Giagkiozis, Ioannis; Fleming, Peter J

2014-01-01

The set of available multi-objective optimisation algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However, this increase also suggests several issues remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker (DM). Even for algorithms very well suited for a particular problem, it is difficult-mainly due to the computational cost-to use a population large enough to ensure the likelihood of obtaining a solution close to the DM's preferences. In this paper we present a novel methodology that produces additional Pareto optimal solutions from a Pareto optimal set obtained at the end run of any multi-objective optimisation algorithm for two-objective and three-objective problem instances.

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

Science.gov (United States)

Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

2017-07-01

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

Pareto law and Pareto index in the income distribution of Japanese companies

OpenAIRE

Ishikawa, Atushi

2004-01-01

In order to study the phenomenon in detail that income distribution follows Pareto law, we analyze the database of high income companies in Japan. We find a quantitative relation between the average capital of the companies and the Pareto index. The larger the average capital becomes, the smaller the Pareto index becomes. From this relation, we can possibly explain that the Pareto index of company income distribution hardly changes, while the Pareto index of personal income distribution chang...

Mapping the Pareto optimal design space for a functionally deimmunized biotherapeutic candidate.

Science.gov (United States)

Salvat, Regina S; Parker, Andrew S; Choi, Yoonjoo; Bailey-Kellogg, Chris; Griswold, Karl E

2015-01-01

The immunogenicity of biotherapeutics can bottleneck development pipelines and poses a barrier to widespread clinical application. As a result, there is a growing need for improved deimmunization technologies. We have recently described algorithms that simultaneously optimize proteins for both reduced T cell epitope content and high-level function. In silico analysis of this dual objective design space reveals that there is no single global optimum with respect to protein deimmunization. Instead, mutagenic epitope deletion yields a spectrum of designs that exhibit tradeoffs between immunogenic potential and molecular function. The leading edge of this design space is the Pareto frontier, i.e. the undominated variants for which no other single design exhibits better performance in both criteria. Here, the Pareto frontier of a therapeutic enzyme has been designed, constructed, and evaluated experimentally. Various measures of protein performance were found to map a functional sequence space that correlated well with computational predictions. These results represent the first systematic and rigorous assessment of the functional penalty that must be paid for pursuing progressively more deimmunized biotherapeutic candidates. Given this capacity to rapidly assess and design for tradeoffs between protein immunogenicity and functionality, these algorithms may prove useful in augmenting, accelerating, and de-risking experimental deimmunization efforts.

Multiobjective Optimization Involving Quadratic Functions

Directory of Open Access Journals (Sweden)

Oscar Brito Augusto

2014-01-01

Full Text Available Multiobjective optimization is nowadays a word of order in engineering projects. Although the idea involved is simple, the implementation of any procedure to solve a general problem is not an easy task. Evolutionary algorithms are widespread as a satisfactory technique to find a candidate set for the solution. Usually they supply a discrete picture of the Pareto front even if this front is continuous. In this paper we propose three methods for solving unconstrained multiobjective optimization problems involving quadratic functions. In the first, for biobjective optimization defined in the bidimensional space, a continuous Pareto set is found analytically. In the second, applicable to multiobjective optimization, a condition test is proposed to check if a point in the decision space is Pareto optimum or not and, in the third, with functions defined in n-dimensional space, a direct noniterative algorithm is proposed to find the Pareto set. Simple problems highlight the suitability of the proposed methods.

Choosing the optimal Pareto composition of the charge material for the manufacture of composite blanks

Science.gov (United States)

Zalazinsky, A. G.; Kryuchkov, D. I.; Nesterenko, A. V.; Titov, V. G.

2017-12-01

The results of an experimental study of the mechanical properties of pressed and sintered briquettes consisting of powders obtained from a high-strength VT-22 titanium alloy by plasma spraying with additives of PTM-1 titanium powder obtained by the hydride-calcium method and powder of PV-N70Yu30 nickel-aluminum alloy are presented. The task is set for the choice of an optimal charge material composition of a composite material providing the required mechanical characteristics and cost of semi-finished products and items. Pareto optimal values for the composition of the composite material charge have been obtained.

PAPR-Constrained Pareto-Optimal Waveform Design for OFDM-STAP Radar

Energy Technology Data Exchange (ETDEWEB)

Sen, Satyabrata [ORNL

2014-01-01

We propose a peak-to-average power ratio (PAPR) constrained Pareto-optimal waveform design approach for an orthogonal frequency division multiplexing (OFDM) radar signal to detect a target using the space-time adaptive processing (STAP) technique. The use of an OFDM signal does not only increase the frequency diversity of our system, but also enables us to adaptively design the OFDM coefficients in order to further improve the system performance. First, we develop a parametric OFDM-STAP measurement model by considering the effects of signaldependent clutter and colored noise. Then, we observe that the resulting STAP-performance can be improved by maximizing the output signal-to-interference-plus-noise ratio (SINR) with respect to the signal parameters. However, in practical scenarios, the computation of output SINR depends on the estimated values of the spatial and temporal frequencies and target scattering responses. Therefore, we formulate a PAPR-constrained multi-objective optimization (MOO) problem to design the OFDM spectral parameters by simultaneously optimizing four objective functions: maximizing the output SINR, minimizing two separate Cramer-Rao bounds (CRBs) on the normalized spatial and temporal frequencies, and minimizing the trace of CRB matrix on the target scattering coefficients estimations. We present several numerical examples to demonstrate the achieved performance improvement due to the adaptive waveform design.

Multi-objective component sizing of a power-split plug-in hybrid electric vehicle powertrain using Pareto-based natural optimization machines

Science.gov (United States)

Mozaffari, Ahmad; Vajedi, Mahyar; Chehresaz, Maryyeh; Azad, Nasser L.

2016-03-01

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain.

Characterizing the Incentive Compatible and Pareto Optimal Efficiency Space for Two Players, k Items, Public Budget and Quasilinear Utilities

Directory of Open Access Journals (Sweden)

Anat Lerner

2014-04-01

Full Text Available We characterize the efficiency space of deterministic, dominant-strategy incentive compatible, individually rational and Pareto-optimal combinatorial auctions in a model with two players and k nonidentical items. We examine a model with multidimensional types, private values and quasilinear preferences for the players with one relaxation: one of the players is subject to a publicly known budget constraint. We show that if it is publicly known that the valuation for the largest bundle is less than the budget for at least one of the players, then Vickrey-Clarke-Groves (VCG uniquely fulfills the basic properties of being deterministic, dominant-strategy incentive compatible, individually rational and Pareto optimal. Our characterization of the efficient space for deterministic budget constrained combinatorial auctions is similar in spirit to that of Maskin 2000 for Bayesian single-item constrained efficiency auctions and comparable with Ausubel and Milgrom 2002 for non-constrained combinatorial auctions.

Spectral-Efficiency - Illumination Pareto Front for Energy Harvesting Enabled VLC System

KAUST Repository

Abdelhady, Amr Mohamed Abdelaziz

2017-12-13

The continuous improvement in optical energy harvesting devices motivates visible light communication (VLC) system developers to utilize such available free energy sources. An outdoor VLC system is considered where an optical base station sends data to multiple users that are capable of harvesting the optical energy. The proposed VLC system serves multiple users using time division multiple access (TDMA) with unequal time and power allocation, which are allocated to improve the system performance. The adopted optical system provides users with illumination and data communication services. The outdoor optical design objective is to maximize the illumination, while the communication design objective is to maximize the spectral efficiency (SE). The design objectives are shown to be conflicting, therefore, a multiobjective optimization problem is formulated to obtain the Pareto front performance curve for the proposed system. To this end, the marginal optimization problems are solved first using low complexity algorithms. Then, based on the proposed algorithms, a low complexity algorithm is developed to obtain an inner bound of the Pareto front for the illumination-SE tradeoff. The inner bound for the Pareto-front is shown to be close to the optimal Pareto-frontier via several simulation scenarios for different system parameters.

Pareto fronts in clinical practice for pinnacle.

Science.gov (United States)

Janssen, Tomas; van Kesteren, Zdenko; Franssen, Gijs; Damen, Eugène; van Vliet, Corine

2013-03-01

Our aim was to develop a framework to objectively perform treatment planning studies using Pareto fronts. The Pareto front represents all optimal possible tradeoffs among several conflicting criteria and is an ideal tool with which to study the possibilities of a given treatment technique. The framework should require minimal user interaction and should resemble and be applicable to daily clinical practice. To generate the Pareto fronts, we used the native scripting language of Pinnacle(3) (Philips Healthcare, Andover, MA). The framework generates thousands of plans automatically from which the Pareto front is generated. As an example, the framework is applied to compare intensity modulated radiation therapy (IMRT) with volumetric modulated arc therapy (VMAT) for prostate cancer patients. For each patient and each technique, 3000 plans are generated, resulting in a total of 60,000 plans. The comparison is based on 5-dimensional Pareto fronts. Generating 3000 plans for 10 patients in parallel requires on average 96 h for IMRT and 483 hours for VMAT. Using VMAT, compared to IMRT, the maximum dose of the boost PTV was reduced by 0.4 Gy (P=.074), the mean dose in the anal sphincter by 1.6 Gy (P=.055), the conformity index of the 95% isodose (CI(95%)) by 0.02 (P=.005), and the rectal wall V(65 Gy) by 1.1% (P=.008). We showed the feasibility of automatically generating Pareto fronts with Pinnacle(3). Pareto fronts provide a valuable tool for performing objective comparative treatment planning studies. We compared VMAT with IMRT in prostate patients and found VMAT had a dosimetric advantage over IMRT. Copyright © 2013 Elsevier Inc. All rights reserved.

Pareto Fronts in Clinical Practice for Pinnacle

International Nuclear Information System (INIS)

Janssen, Tomas; Kesteren, Zdenko van; Franssen, Gijs; Damen, Eugène; Vliet, Corine van

2013-01-01

Purpose: Our aim was to develop a framework to objectively perform treatment planning studies using Pareto fronts. The Pareto front represents all optimal possible tradeoffs among several conflicting criteria and is an ideal tool with which to study the possibilities of a given treatment technique. The framework should require minimal user interaction and should resemble and be applicable to daily clinical practice. Methods and Materials: To generate the Pareto fronts, we used the native scripting language of Pinnacle 3 (Philips Healthcare, Andover, MA). The framework generates thousands of plans automatically from which the Pareto front is generated. As an example, the framework is applied to compare intensity modulated radiation therapy (IMRT) with volumetric modulated arc therapy (VMAT) for prostate cancer patients. For each patient and each technique, 3000 plans are generated, resulting in a total of 60,000 plans. The comparison is based on 5-dimensional Pareto fronts. Results: Generating 3000 plans for 10 patients in parallel requires on average 96 h for IMRT and 483 hours for VMAT. Using VMAT, compared to IMRT, the maximum dose of the boost PTV was reduced by 0.4 Gy (P=.074), the mean dose in the anal sphincter by 1.6 Gy (P=.055), the conformity index of the 95% isodose (CI 95% ) by 0.02 (P=.005), and the rectal wall V 65 Gy by 1.1% (P=.008). Conclusions: We showed the feasibility of automatically generating Pareto fronts with Pinnacle 3 . Pareto fronts provide a valuable tool for performing objective comparative treatment planning studies. We compared VMAT with IMRT in prostate patients and found VMAT had a dosimetric advantage over IMRT

Optimization of well field management

DEFF Research Database (Denmark)

Hansen, Annette Kirstine

Groundwater is a limited but important resource for fresh water supply. Differ- ent conflicting objectives are important when operating a well field. This study investigates how the management of a well field can be improved with respect to different objectives simultaneously. A framework...... for optimizing well field man- agement using multi-objective optimization is developed. The optimization uses the Strength Pareto Evolutionary Algorithm 2 (SPEA2) to find the Pareto front be- tween the conflicting objectives. The Pareto front is a set of non-inferior optimal points and provides an important tool...... for the decision-makers. The optimization framework is tested on two case studies. Both abstract around 20,000 cubic meter of water per day, but are otherwise rather different. The first case study concerns the management of Hardhof waterworks, Switzer- land, where artificial infiltration of river water...

Pareto navigation-algorithmic foundation of interactive multi-criteria IMRT planning

International Nuclear Information System (INIS)

Monz, M; Kuefer, K H; Bortfeld, T R; Thieke, C

2008-01-01

Inherently, IMRT treatment planning involves compromising between different planning goals. Multi-criteria IMRT planning directly addresses this compromising and thus makes it more systematic. Usually, several plans are computed from which the planner selects the most promising following a certain procedure. Applying Pareto navigation for this selection step simultaneously increases the variety of planning options and eases the identification of the most promising plan. Pareto navigation is an interactive multi-criteria optimization method that consists of the two navigation mechanisms 'selection' and 'restriction'. The former allows the formulation of wishes whereas the latter allows the exclusion of unwanted plans. They are realized as optimization problems on the so-called plan bundle-a set constructed from pre-computed plans. They can be approximately reformulated so that their solution time is a small fraction of a second. Thus, the user can be provided with immediate feedback regarding his or her decisions. Pareto navigation was implemented in the MIRA navigator software and allows real-time manipulation of the current plan and the set of considered plans. The changes are triggered by simple mouse operations on the so-called navigation star and lead to real-time updates of the navigation star and the dose visualizations. Since any Pareto-optimal plan in the plan bundle can be found with just a few navigation operations the MIRA navigator allows a fast and directed plan determination. Besides, the concept allows for a refinement of the plan bundle, thus offering a middle course between single plan computation and multi-criteria optimization. Pareto navigation offers so far unmatched real-time interactions, ease of use and plan variety, setting it apart from the multi-criteria IMRT planning methods proposed so far

Pareto navigation: algorithmic foundation of interactive multi-criteria IMRT planning.

Science.gov (United States)

Monz, M; Küfer, K H; Bortfeld, T R; Thieke, C

2008-02-21

Inherently, IMRT treatment planning involves compromising between different planning goals. Multi-criteria IMRT planning directly addresses this compromising and thus makes it more systematic. Usually, several plans are computed from which the planner selects the most promising following a certain procedure. Applying Pareto navigation for this selection step simultaneously increases the variety of planning options and eases the identification of the most promising plan. Pareto navigation is an interactive multi-criteria optimization method that consists of the two navigation mechanisms 'selection' and 'restriction'. The former allows the formulation of wishes whereas the latter allows the exclusion of unwanted plans. They are realized as optimization problems on the so-called plan bundle -- a set constructed from pre-computed plans. They can be approximately reformulated so that their solution time is a small fraction of a second. Thus, the user can be provided with immediate feedback regarding his or her decisions. Pareto navigation was implemented in the MIRA navigator software and allows real-time manipulation of the current plan and the set of considered plans. The changes are triggered by simple mouse operations on the so-called navigation star and lead to real-time updates of the navigation star and the dose visualizations. Since any Pareto-optimal plan in the plan bundle can be found with just a few navigation operations the MIRA navigator allows a fast and directed plan determination. Besides, the concept allows for a refinement of the plan bundle, thus offering a middle course between single plan computation and multi-criteria optimization. Pareto navigation offers so far unmatched real-time interactions, ease of use and plan variety, setting it apart from the multi-criteria IMRT planning methods proposed so far.

Computing gap free Pareto front approximations with stochastic search algorithms.

Science.gov (United States)

Schütze, Oliver; Laumanns, Marco; Tantar, Emilia; Coello, Carlos A Coello; Talbi, El-Ghazali

2010-01-01

Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though bounds on the quality of the limit approximation-which are entirely determined by the archiving strategy and the value of epsilon-have been obtained, the strategies do not guarantee to obtain a gap free approximation of the Pareto front. That is, such approximations A can reveal gaps in the sense that points f in the Pareto front can exist such that the distance of f to any image point F(a), a epsilon A, is "large." Since such gap free approximations are desirable in certain applications, and the related archiving strategies can be advantageous when memetic strategies are included in the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs, we give some numerical results to visualize the behavior of the different archiving strategies. Finally, we demonstrate the potential for a possible hybridization of a given stochastic search algorithm with a particular local search strategy-multi-objective continuation methods-by showing that the concept of epsilon-dominance can be integrated into this approach in a suitable way.

Pareto printsiip

Index Scriptorium Estoniae

2011-01-01

Itaalia majandusteadlase Vilfredo Pareto jõudmisest oma kuulsa printsiibini ja selle printsiibi mõjust tänapäevasele juhtimisele. Pareto printsiibi kohaselt ei aita suurem osa tegevusest meid tulemuseni jõuda, vaid on aja raiskamine. Diagramm

Finding the Pareto Optimal Equitable Allocation of Homogeneous Divisible Goods Among Three Players

Directory of Open Access Journals (Sweden)

Marco Dall'Aglio

2017-01-01

Full Text Available We consider the allocation of a finite number of homogeneous divisible items among three players. Under the assumption that each player assigns a positive value to every item, we develop a simple algorithm that returns a Pareto optimal and equitable allocation. This is based on the tight relationship between two geometric objects of fair division: The Individual Pieces Set (IPS and the Radon-Nykodim Set (RNS. The algorithm can be considered as an extension of the Adjusted Winner procedure by Brams and Taylor to the three-player case, without the guarantee of envy-freeness. (original abstract

Pareto-Optimization of HTS CICC for High-Current Applications in Self-Field

Directory of Open Access Journals (Sweden)

Giordano Tomassetti

2018-01-01

Full Text Available The ENEA superconductivity laboratory developed a novel design for Cable-in-Conduit Conductors (CICCs comprised of stacks of 2nd-generation REBCO coated conductors. In its original version, the cable was made up of 150 HTS tapes distributed in five slots, twisted along an aluminum core. In this work, taking advantage of a 2D finite element model, able to estimate the cable’s current distribution in the cross-section, a multiobjective optimization procedure was implemented. The aim of optimization was to simultaneously maximize both engineering current density and total current flowing inside the tapes when operating in self-field, by varying the cross-section layout. Since the optimization process involved both integer and real geometrical variables, the choice of an evolutionary search algorithm was strictly necessary. The use of an evolutionary algorithm in the frame of a multiple objective optimization made it an obliged choice to numerically approach the problem using a nonstandard fast-converging optimization algorithm. By means of this algorithm, the Pareto frontiers for the different configurations were calculated, providing a powerful tool for the designer to achieve the desired preliminary operating conditions in terms of engineering current density and/or total current, depending on the specific application field, that is, power transmission cable and bus bar systems.

Pareto-Optimal Multi-objective Inversion of Geophysical Data

Science.gov (United States)

Schnaidt, Sebastian; Conway, Dennis; Krieger, Lars; Heinson, Graham

2018-01-01

In the process of modelling geophysical properties, jointly inverting different data sets can greatly improve model results, provided that the data sets are compatible, i.e., sensitive to similar features. Such a joint inversion requires a relationship between the different data sets, which can either be analytic or structural. Classically, the joint problem is expressed as a scalar objective function that combines the misfit functions of multiple data sets and a joint term which accounts for the assumed connection between the data sets. This approach suffers from two major disadvantages: first, it can be difficult to assess the compatibility of the data sets and second, the aggregation of misfit terms introduces a weighting of the data sets. We present a pareto-optimal multi-objective joint inversion approach based on an existing genetic algorithm. The algorithm treats each data set as a separate objective, avoiding forced weighting and generating curves of the trade-off between the different objectives. These curves are analysed by their shape and evolution to evaluate data set compatibility. Furthermore, the statistical analysis of the generated solution population provides valuable estimates of model uncertainty.

Design of a Circularly Polarized Galileo E6-Band Textile Antenna by Dedicated Multiobjective Constrained Pareto Optimization

Directory of Open Access Journals (Sweden)

Arnaut Dierck

2015-01-01

Full Text Available Designing textile antennas for real-life applications requires a design strategy that is able to produce antennas that are optimized over a wide bandwidth for often conflicting characteristics, such as impedance matching, axial ratio, efficiency, and gain, and, moreover, that is able to account for the variations that apply for the characteristics of the unconventional materials used in smart textile systems. In this paper, such a strategy, incorporating a multiobjective constrained Pareto optimization, is presented and applied to the design of a Galileo E6-band antenna with optimal return loss and wide-band axial ratio characteristics. Subsequently, different prototypes of the optimized antenna are fabricated and measured to validate the proposed design strategy.

Pareto utility

NARCIS (Netherlands)

Ikefuji, M.; Laeven, R.J.A.; Magnus, J.R.; Muris, C.H.M.

2013-01-01

In searching for an appropriate utility function in the expected utility framework, we formulate four properties that we want the utility function to satisfy. We conduct a search for such a function, and we identify Pareto utility as a function satisfying all four desired properties. Pareto utility

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

Directory of Open Access Journals (Sweden)

Carlos Pozo

Full Text Available Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters.

Science.gov (United States)

Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano

2012-01-01

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the

Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks.

Science.gov (United States)

Fernández Caballero, Juan Carlos; Martínez, Francisco José; Hervás, César; Gutiérrez, Pedro Antonio

2010-05-01

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.

Distributed approximation of Pareto surfaces in multicriteria radiation therapy treatment planning

International Nuclear Information System (INIS)

Bokrantz, Rasmus

2013-01-01

We consider multicriteria radiation therapy treatment planning by navigation over the Pareto surface, implemented by interpolation between discrete treatment plans. Current state of the art for calculation of a discrete representation of the Pareto surface is to sandwich this set between inner and outer approximations that are updated one point at a time. In this paper, we generalize this sequential method to an algorithm that permits parallelization. The principle of the generalization is to apply the sequential method to an approximation of an inexpensive model of the Pareto surface. The information gathered from the model is sub-sequently used for the calculation of points from the exact Pareto surface, which are processed in parallel. The model is constructed according to the current inner and outer approximations, and given a shape that is difficult to approximate, in order to avoid that parts of the Pareto surface are incorrectly disregarded. Approximations of comparable quality to those generated by the sequential method are demonstrated when the degree of parallelization is up to twice the number of dimensions of the objective space. For practical applications, the number of dimensions is typically at least five, so that a speed-up of one order of magnitude is obtained. (paper)

Distributed approximation of Pareto surfaces in multicriteria radiation therapy treatment planning.

Science.gov (United States)

Bokrantz, Rasmus

2013-06-07

We consider multicriteria radiation therapy treatment planning by navigation over the Pareto surface, implemented by interpolation between discrete treatment plans. Current state of the art for calculation of a discrete representation of the Pareto surface is to sandwich this set between inner and outer approximations that are updated one point at a time. In this paper, we generalize this sequential method to an algorithm that permits parallelization. The principle of the generalization is to apply the sequential method to an approximation of an inexpensive model of the Pareto surface. The information gathered from the model is sub-sequently used for the calculation of points from the exact Pareto surface, which are processed in parallel. The model is constructed according to the current inner and outer approximations, and given a shape that is difficult to approximate, in order to avoid that parts of the Pareto surface are incorrectly disregarded. Approximations of comparable quality to those generated by the sequential method are demonstrated when the degree of parallelization is up to twice the number of dimensions of the objective space. For practical applications, the number of dimensions is typically at least five, so that a speed-up of one order of magnitude is obtained.

Spatial redistribution of irregularly-spaced Pareto fronts for more intuitive navigation and solution selection

NARCIS (Netherlands)

A. Bouter (Anton); K. Pirpinia (Kleopatra); T. Alderliesten (Tanja); P.A.N. Bosman (Peter)

2017-01-01

textabstractA multi-objective optimization approach is o.en followed by an a posteriori decision-making process, during which the most appropriate solution of the Pareto set is selected by a professional in the .eld. Conventional visualization methods do not correct for Pareto fronts with

Generalized Pareto optimum and semi-classical spinors

Science.gov (United States)

Rouleux, M.

2018-02-01

In 1971, S. Smale presented a generalization of Pareto optimum he called the critical Pareto set. The underlying motivation was to extend Morse theory to several functions, i.e. to find a Morse theory for m differentiable functions defined on a manifold M of dimension ℓ. We use this framework to take a 2 × 2 Hamiltonian ℋ = ℋ(p) ∈ 2 C ∞(T * R 2) to its normal form near a singular point of the Fresnel surface. Namely we say that ℋ has the Pareto property if it decomposes, locally, up to a conjugation with regular matrices, as ℋ(p) = u ‧(p)C(p)(u ‧(p))*, where u : R 2 → R 2 has singularities of codimension 1 or 2, and C(p) is a regular Hermitian matrix (“integrating factor”). In particular this applies in certain cases to the matrix Hamiltonian of Elasticity theory and its (relative) perturbations of order 3 in momentum at the origin.

Beam configuration selection for robust intensity-modulated proton therapy in cervical cancer using Pareto front comparison.

Science.gov (United States)

van de Schoot, A J A J; Visser, J; van Kesteren, Z; Janssen, T M; Rasch, C R N; Bel, A

2016-02-21

The Pareto front reflects the optimal trade-offs between conflicting objectives and can be used to quantify the effect of different beam configurations on plan robustness and dose-volume histogram parameters. Therefore, our aim was to develop and implement a method to automatically approach the Pareto front in robust intensity-modulated proton therapy (IMPT) planning. Additionally, clinically relevant Pareto fronts based on different beam configurations will be derived and compared to enable beam configuration selection in cervical cancer proton therapy. A method to iteratively approach the Pareto front by automatically generating robustly optimized IMPT plans was developed. To verify plan quality, IMPT plans were evaluated on robustness by simulating range and position errors and recalculating the dose. For five retrospectively selected cervical cancer patients, this method was applied for IMPT plans with three different beam configurations using two, three and four beams. 3D Pareto fronts were optimized on target coverage (CTV D(99%)) and OAR doses (rectum V30Gy; bladder V40Gy). Per patient, proportions of non-approved IMPT plans were determined and differences between patient-specific Pareto fronts were quantified in terms of CTV D(99%), rectum V(30Gy) and bladder V(40Gy) to perform beam configuration selection. Per patient and beam configuration, Pareto fronts were successfully sampled based on 200 IMPT plans of which on average 29% were non-approved plans. In all patients, IMPT plans based on the 2-beam set-up were completely dominated by plans with the 3-beam and 4-beam configuration. Compared to the 3-beam set-up, the 4-beam set-up increased the median CTV D(99%) on average by 0.2 Gy and decreased the median rectum V(30Gy) and median bladder V(40Gy) on average by 3.6% and 1.3%, respectively. This study demonstrates a method to automatically derive Pareto fronts in robust IMPT planning. For all patients, the defined four-beam configuration was found optimal

Beam configuration selection for robust intensity-modulated proton therapy in cervical cancer using Pareto front comparison

International Nuclear Information System (INIS)

Van de Schoot, A J A J; Visser, J; Van Kesteren, Z; Rasch, C R N; Bel, A; Janssen, T M

2016-01-01

The Pareto front reflects the optimal trade-offs between conflicting objectives and can be used to quantify the effect of different beam configurations on plan robustness and dose-volume histogram parameters. Therefore, our aim was to develop and implement a method to automatically approach the Pareto front in robust intensity-modulated proton therapy (IMPT) planning. Additionally, clinically relevant Pareto fronts based on different beam configurations will be derived and compared to enable beam configuration selection in cervical cancer proton therapy. A method to iteratively approach the Pareto front by automatically generating robustly optimized IMPT plans was developed. To verify plan quality, IMPT plans were evaluated on robustness by simulating range and position errors and recalculating the dose. For five retrospectively selected cervical cancer patients, this method was applied for IMPT plans with three different beam configurations using two, three and four beams. 3D Pareto fronts were optimized on target coverage (CTV D 99% ) and OAR doses (rectum V 30Gy ; bladder V 40Gy ). Per patient, proportions of non-approved IMPT plans were determined and differences between patient-specific Pareto fronts were quantified in terms of CTV D 99% , rectum V 30Gy and bladder V 40Gy to perform beam configuration selection. Per patient and beam configuration, Pareto fronts were successfully sampled based on 200 IMPT plans of which on average 29% were non-approved plans. In all patients, IMPT plans based on the 2-beam set-up were completely dominated by plans with the 3-beam and 4-beam configuration. Compared to the 3-beam set-up, the 4-beam set-up increased the median CTV D 99% on average by 0.2 Gy and decreased the median rectum V 30Gy and median bladder V 40Gy on average by 3.6% and 1.3%, respectively. This study demonstrates a method to automatically derive Pareto fronts in robust IMPT planning. For all patients, the defined four-beam configuration was found optimal in

Evaluation of Preanalytical Quality Indicators by Six Sigma and Pareto`s Principle.

Science.gov (United States)

Kulkarni, Sweta; Ramesh, R; Srinivasan, A R; Silvia, C R Wilma Delphine

2018-01-01

Preanalytical steps are the major sources of error in clinical laboratory. The analytical errors can be corrected by quality control procedures but there is a need for stringent quality checks in preanalytical area as these processes are done outside the laboratory. Sigma value depicts the performance of laboratory and its quality measures. Hence in the present study six sigma and Pareto principle was applied to preanalytical quality indicators to evaluate the clinical biochemistry laboratory performance. This observational study was carried out for a period of 1 year from November 2015-2016. A total of 1,44,208 samples and 54,265 test requisition forms were screened for preanalytical errors like missing patient information, sample collection details in forms and hemolysed, lipemic, inappropriate, insufficient samples and total number of errors were calculated and converted into defects per million and sigma scale. Pareto`s chart was drawn using total number of errors and cumulative percentage. In 75% test requisition forms diagnosis was not mentioned and sigma value of 0.9 was obtained and for other errors like sample receiving time, stat and type of sample sigma values were 2.9, 2.6, and 2.8 respectively. For insufficient sample and improper ratio of blood to anticoagulant sigma value was 4.3. Pareto`s chart depicts out of 80% of errors in requisition forms, 20% is contributed by missing information like diagnosis. The development of quality indicators, application of six sigma and Pareto`s principle are quality measures by which not only preanalytical, the total testing process can be improved.

SU-F-J-105: Towards a Novel Treatment Planning Pipeline Delivering Pareto- Optimal Plans While Enabling Inter- and Intrafraction Plan Adaptation

Energy Technology Data Exchange (ETDEWEB)

Kontaxis, C; Bol, G; Lagendijk, J; Raaymakers, B [University Medical Center Utrecht, Utrecht (Netherlands); Breedveld, S; Sharfo, A; Heijmen, B [Erasmus University Medical Center Rotterdam, Rotterdam (Netherlands)

2016-06-15

Purpose: To develop a new IMRT treatment planning methodology suitable for the new generation of MR-linear accelerator machines. The pipeline is able to deliver Pareto-optimal plans and can be utilized for conventional treatments as well as for inter- and intrafraction plan adaptation based on real-time MR-data. Methods: A Pareto-optimal plan is generated using the automated multicriterial optimization approach Erasmus-iCycle. The resulting dose distribution is used as input to the second part of the pipeline, an iterative process which generates deliverable segments that target the latest anatomical state and gradually converges to the prescribed dose. This process continues until a certain percentage of the dose has been delivered. Under a conventional treatment, a Segment Weight Optimization (SWO) is then performed to ensure convergence to the prescribed dose. In the case of inter- and intrafraction adaptation, post-processing steps like SWO cannot be employed due to the changing anatomy. This is instead addressed by transferring the missing/excess dose to the input of the subsequent fraction. In this work, the resulting plans were delivered on a Delta4 phantom as a final Quality Assurance test. Results: A conventional static SWO IMRT plan was generated for two prostate cases. The sequencer faithfully reproduced the input dose for all volumes of interest. For the two cases the mean relative dose difference of the PTV between the ideal input and sequenced dose was 0.1% and −0.02% respectively. Both plans were delivered on a Delta4 phantom and passed the clinical Quality Assurance procedures by achieving 100% pass rate at a 3%/3mm gamma analysis. Conclusion: We have developed a new sequencing methodology capable of online plan adaptation. In this work, we extended the pipeline to support Pareto-optimal input and clinically validated that it can accurately achieve these ideal distributions, while its flexible design enables inter- and intrafraction plan

SU-F-J-105: Towards a Novel Treatment Planning Pipeline Delivering Pareto- Optimal Plans While Enabling Inter- and Intrafraction Plan Adaptation

International Nuclear Information System (INIS)

Kontaxis, C; Bol, G; Lagendijk, J; Raaymakers, B; Breedveld, S; Sharfo, A; Heijmen, B

2016-01-01

Purpose: To develop a new IMRT treatment planning methodology suitable for the new generation of MR-linear accelerator machines. The pipeline is able to deliver Pareto-optimal plans and can be utilized for conventional treatments as well as for inter- and intrafraction plan adaptation based on real-time MR-data. Methods: A Pareto-optimal plan is generated using the automated multicriterial optimization approach Erasmus-iCycle. The resulting dose distribution is used as input to the second part of the pipeline, an iterative process which generates deliverable segments that target the latest anatomical state and gradually converges to the prescribed dose. This process continues until a certain percentage of the dose has been delivered. Under a conventional treatment, a Segment Weight Optimization (SWO) is then performed to ensure convergence to the prescribed dose. In the case of inter- and intrafraction adaptation, post-processing steps like SWO cannot be employed due to the changing anatomy. This is instead addressed by transferring the missing/excess dose to the input of the subsequent fraction. In this work, the resulting plans were delivered on a Delta4 phantom as a final Quality Assurance test. Results: A conventional static SWO IMRT plan was generated for two prostate cases. The sequencer faithfully reproduced the input dose for all volumes of interest. For the two cases the mean relative dose difference of the PTV between the ideal input and sequenced dose was 0.1% and −0.02% respectively. Both plans were delivered on a Delta4 phantom and passed the clinical Quality Assurance procedures by achieving 100% pass rate at a 3%/3mm gamma analysis. Conclusion: We have developed a new sequencing methodology capable of online plan adaptation. In this work, we extended the pipeline to support Pareto-optimal input and clinically validated that it can accurately achieve these ideal distributions, while its flexible design enables inter- and intrafraction plan

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors

Science.gov (United States)

McInerney, David; Thyer, Mark; Kavetski, Dmitri; Lerat, Julien; Kuczera, George

2017-03-01

Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. This study focuses on approaches for representing error heteroscedasticity with respect to simulated streamflow, i.e., the pattern of larger errors in higher streamflow predictions. We evaluate eight common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter λ) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and the United States, and two lumped hydrological models. Performance is quantified using predictive reliability, precision, and volumetric bias metrics. We find the choice of heteroscedastic error modeling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with λ of 0.2 and 0.5, and the log scheme (λ = 0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Paradoxically, calibration of λ is often counterproductive: in perennial catchments, it tends to overfit low flows at the expense of abysmal precision in high flows. The log-sinh transformation is dominated by the simpler Pareto optimal schemes listed above. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

Science.gov (United States)

Jiang, Shouyong; Yang, Shengxiang

2016-02-01

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

Evaluation of treatment plan quality of IMRT and VMAT with and without flattening filter using Pareto optimal fronts.

Science.gov (United States)

Lechner, Wolfgang; Kragl, Gabriele; Georg, Dietmar

2013-12-01

To investigate the differences in treatment plan quality of IMRT and VMAT with and without flattening filter using Pareto optimal fronts, for two treatment sites of different anatomic complexity. Pareto optimal fronts (POFs) were generated for six prostate and head-and-neck cancer patients by stepwise reduction of the constraint (during the optimization process) of the primary organ-at-risk (OAR). 9-static field IMRT and 360°-single-arc VMAT plans with flattening filter (FF) and without flattening filter (FFF) were compared. The volume receiving 5 Gy or more (V5 Gy) was used to estimate the low dose exposure. Furthermore, the number of monitor units (MUs) and measurements of the delivery time (T) were used to assess the efficiency of the treatment plans. A significant increase in MUs was found when using FFF-beams while the treatment plan quality was at least equivalent to the FF-beams. T was decreased by 18% for prostate for IMRT with FFF-beams and by 4% for head-and-neck cases, but increased by 22% and 16% for VMAT. A reduction of up to 5% of V5 Gy was found for IMRT prostate cases with FFF-beams. The evaluation of the POFs showed an at least comparable treatment plan quality of FFF-beams compared to FF-beams for both treatment sites and modalities. For smaller targets the advantageous characteristics of FFF-beams could be better exploited. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

Science.gov (United States)

Xu, Gongxian; Liu, Ying; Gao, Qunwang

2016-02-10

This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimal design and management of chlorination in drinking water networks: a multi-objective approach using Genetic Algorithms and the Pareto optimality concept

Science.gov (United States)

Nouiri, Issam

2017-11-01

This paper presents the development of multi-objective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objectives have been considered: the improvement of the chlorination uniformity (healthy objective), the minimization of chlorine booster stations number, and the injected chlorine mass (economic objectives). The problem has been dissociated in medium and short terms ones. The proposed methodology was tested on hypothetical and real DWN. Results proved the ability of the developed optimization tool to identify relationships between the healthy and economic objectives as Pareto fronts. The proposed approach was efficient in computing solutions ensuring better chlorination uniformity while requiring the weakest injected chlorine mass when compared to other approaches. For the real DWN studied, chlorination optimization has been crowned by great improvement of free-chlorine-dosing uniformity and by a meaningful chlorine mass reduction, in comparison with the conventional chlorination.

Pareto-Optimal Evaluation of Ultimate Limit States in Offshore Wind Turbine Structural Analysis

Directory of Open Access Journals (Sweden)

Michael Muskulus

2015-12-01

Full Text Available The ultimate capacity of support structures is checked with extreme loads. This is straightforward when the limit state equations depend on a single load component, and it has become common to report maxima for each load component. However, if more than one load component is influential, e.g., both axial force and bending moments, it is not straightforward how to define an extreme load. The combination of univariate maxima can be too conservative, and many different combinations of load components can result in the worst value of the limit state equations. The use of contemporaneous load vectors is typically non-conservative. Therefore, in practice, limit state checks are done for each possible load vector, from each time step of a simulation. This is not feasible when performing reliability assessments and structural optimization, where additional, time-consuming computations are involved for each load vector. We therefore propose to use Pareto-optimal loads, which are a small set of loads that together represent all possible worst case scenarios. Simulations with two reference wind turbines show that this approach can be very useful for jacket structures, whereas the design of monopiles is often governed by the bending moment only. Even in this case, the approach might be useful when approaching the structural limits during optimization.

Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm

International Nuclear Information System (INIS)

Shojaeefard, Mohammad Hasan; Behnagh, Reza Abdi; Akbari, Mostafa; Givi, Mohammad Kazem Besharati; Farhani, Foad

2013-01-01

Highlights: ► Defect-free friction stir welds have been produced for AA5083-O/AA7075-O. ► Back-propagation was sufficient for predicting hardness and tensile strength. ► A hybrid multi-objective algorithm is proposed to deal with this MOP. ► Multi-objective particle swarm optimization was used to find the Pareto solutions. ► TOPSIS is used to rank the given alternatives of the Pareto solutions. -- Abstract: Friction Stir Welding (FSW) has been successfully used to weld similar and dissimilar cast and wrought aluminium alloys, especially for aircraft aluminium alloys, that generally present with low weldability by the traditional fusion welding process. This paper focuses on the microstructural and mechanical properties of the Friction Stir Welding (FSW) of AA7075-O to AA5083-O aluminium alloys. Weld microstructures, hardness and tensile properties were evaluated in as-welded condition. Tensile tests indicated that mechanical properties of the joint were better than in the base metals. An Artificial Neural Network (ANN) model was developed to simulate the correlation between the Friction Stir Welding parameters and mechanical properties. Performance of the ANN model was excellent and the model was employed to predict the ultimate tensile strength and hardness of butt joint of AA7075–AA5083 as functions of weld and rotational speeds. The multi-objective particle swarm optimization was used to obtain the Pareto-optimal set. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was applied to determine the best compromised solution.

Numerical investigation of a dual-loop EGR split strategy using a split index and multi-objective Pareto optimization

International Nuclear Information System (INIS)

Park, Jungsoo; Song, Soonho; Lee, Kyo Seung

2015-01-01

Highlights: • Model-based control of dual-loop EGR system is performed. • EGR split index is developed to provide non-dimensional index for optimization. • EGR rates are calibrated using EGR split index at specific operating conditions. • Multi-objective Pareto optimization is performed to minimize NO X and BSFC. • Optimum split strategies are suggested with LP-rich dual-loop EGR at high load. - Abstract: A proposed dual-loop exhaust-gas recirculation (EGR) system that combines the features of high-pressure (HP) and low-pressure (LP) systems is considered a key technology for improving the combustion behavior of diesel engines. The fraction of HP and LP flows, known as the EGR split, for a given dual-loop EGR rate play an important role in determining the engine performance and emission characteristics. Therefore, identifying the proper EGR split is important for the engine optimization and calibration processes, which affect the EGR response and deNO X efficiencies. The objective of this research was to develop a dual-loop EGR split strategy using numerical analysis and one-dimensional (1D) cycle simulation. A control system was modeled by coupling the 1D cycle simulation and the control logic. An EGR split index was developed to investigate the HP/LP split effects on the engine performance and emissions. Using the model-based control system, a multi-objective Pareto (MOP) analysis was used to minimize the NO X formation and fuel consumption through optimized engine operating parameters. The MOP analysis was performed using a response surface model extracted from Latin hypercube sampling as a fractional factorial design of experiment. By using an LP rich dual-loop EGR, a high EGR rate was attained at low, medium, and high engine speeds, increasing the applicable load ranges compared to base conditions

Tapped density optimisation for four agricultural wastes - Part II: Performance analysis and Taguchi-Pareto

Directory of Open Access Journals (Sweden)

Ajibade Oluwaseyi Ayodele

2016-01-01

Full Text Available In this attempt, which is a second part of discussions on tapped density optimisation for four agricultural wastes (particles of coconut, periwinkle, palm kernel and egg shells, performance analysis for comparative basis is made. This paper pioneers a study direction in which optimisation of process variables are pursued using Taguchi method integrated with the Pareto 80-20 rule. Negative percentage improvements resulted when the optimal tapped density was compared with the average tapped density. However, the performance analysis between optimal tapped density and the peak tapped density values yielded positive percentage improvements for the four filler particles. The performance analysis results validate the effectiveness of using the Taguchi method in improving the tapped density properties of the filler particles. The application of the Pareto 80-20 rule to the table of parameters and levels produced revised tables of parameters and levels which helped to identify the factor-levels position of each parameter that is economical to optimality. The Pareto 80-20 rule also produced revised S/N response tables which were used to know the relevant S/N ratios that are relevant to optimality.

Efficiently approximating the Pareto frontier: Hydropower dam placement in the Amazon basin

Science.gov (United States)

Wu, Xiaojian; Gomes-Selman, Jonathan; Shi, Qinru; Xue, Yexiang; Garcia-Villacorta, Roosevelt; Anderson, Elizabeth; Sethi, Suresh; Steinschneider, Scott; Flecker, Alexander; Gomes, Carla P.

2018-01-01

Real–world problems are often not fully characterized by a single optimal solution, as they frequently involve multiple competing objectives; it is therefore important to identify the so-called Pareto frontier, which captures solution trade-offs. We propose a fully polynomial-time approximation scheme based on Dynamic Programming (DP) for computing a polynomially succinct curve that approximates the Pareto frontier to within an arbitrarily small > 0 on treestructured networks. Given a set of objectives, our approximation scheme runs in time polynomial in the size of the instance and 1/. We also propose a Mixed Integer Programming (MIP) scheme to approximate the Pareto frontier. The DP and MIP Pareto frontier approaches have complementary strengths and are surprisingly effective. We provide empirical results showing that our methods outperform other approaches in efficiency and accuracy. Our work is motivated by a problem in computational sustainability concerning the proliferation of hydropower dams throughout the Amazon basin. Our goal is to support decision-makers in evaluating impacted ecosystem services on the full scale of the Amazon basin. Our work is general and can be applied to approximate the Pareto frontier of a variety of multiobjective problems on tree-structured networks.

Determining decoupling points in a supply chain networks using NSGA II algorithm

Energy Technology Data Exchange (ETDEWEB)

Ebrahimiarjestan, M.; Wang, G.

2017-07-01

Purpose: In the model, we used the concepts of Lee and Amaral (2002) and Tang and Zhou (2009) and offer a multi-criteria decision-making model that identify the decoupling points to aim to minimize production costs, minimize the product delivery time to customer and maximize their satisfaction. Design/methodology/approach: We encounter with a triple-objective model that meta-heuristic method (NSGA II) is used to solve the model and to identify the Pareto optimal points. The max (min) method was used. Findings: Our results of using NSGA II to find Pareto optimal solutions demonstrate good performance of NSGA II to extract Pareto solutions in proposed model that considers determining of decoupling point in a supply network. Originality/value: So far, several approaches to model the future have been proposed, of course, each of them modeled a part of this concept. This concept has been considered more general in the model that defined in follow. In this model, we face with a multi-criteria decision problem that includes minimization of the production costs and product delivery time to customers as well as customer consistency maximization.

Determining decoupling points in a supply chain networks using NSGA II algorithm

International Nuclear Information System (INIS)

Ebrahimiarjestan, M.; Wang, G.

2017-01-01

Purpose: In the model, we used the concepts of Lee and Amaral (2002) and Tang and Zhou (2009) and offer a multi-criteria decision-making model that identify the decoupling points to aim to minimize production costs, minimize the product delivery time to customer and maximize their satisfaction. Design/methodology/approach: We encounter with a triple-objective model that meta-heuristic method (NSGA II) is used to solve the model and to identify the Pareto optimal points. The max (min) method was used. Findings: Our results of using NSGA II to find Pareto optimal solutions demonstrate good performance of NSGA II to extract Pareto solutions in proposed model that considers determining of decoupling point in a supply network. Originality/value: So far, several approaches to model the future have been proposed, of course, each of them modeled a part of this concept. This concept has been considered more general in the model that defined in follow. In this model, we face with a multi-criteria decision problem that includes minimization of the production costs and product delivery time to customers as well as customer consistency maximization.

A Pareto-Improving Minimum Wage

OpenAIRE

Eliav Danziger; Leif Danziger

2014-01-01

This paper shows that a graduated minimum wage, in contrast to a constant minimum wage, can provide a strict Pareto improvement over what can be achieved with an optimal income tax. The reason is that a graduated minimum wage requires high-productivity workers to work more to earn the same income as low-productivity workers, which makes it more difficult for the former to mimic the latter. In effect, a graduated minimum wage allows the low-productivity workers to benefit from second-degree pr...

Efficient approximation of black-box functions and Pareto sets

NARCIS (Netherlands)

Rennen, G.

2009-01-01

In the case of time-consuming simulation models or other so-called black-box functions, we determine a metamodel which approximates the relation between the input- and output-variables of the simulation model. To solve multi-objective optimization problems, we approximate the Pareto set, i.e. the

Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.

Science.gov (United States)

Enns, Eva A; Cipriano, Lauren E; Simons, Cyrena T; Kong, Chung Yin

2015-02-01

To identify best-fitting input sets using model calibration, individual calibration target fits are often combined into a single goodness-of-fit (GOF) measure using a set of weights. Decisions in the calibration process, such as which weights to use, influence which sets of model inputs are identified as best-fitting, potentially leading to different health economic conclusions. We present an alternative approach to identifying best-fitting input sets based on the concept of Pareto-optimality. A set of model inputs is on the Pareto frontier if no other input set simultaneously fits all calibration targets as well or better. We demonstrate the Pareto frontier approach in the calibration of 2 models: a simple, illustrative Markov model and a previously published cost-effectiveness model of transcatheter aortic valve replacement (TAVR). For each model, we compare the input sets on the Pareto frontier to an equal number of best-fitting input sets according to 2 possible weighted-sum GOF scoring systems, and we compare the health economic conclusions arising from these different definitions of best-fitting. For the simple model, outcomes evaluated over the best-fitting input sets according to the 2 weighted-sum GOF schemes were virtually nonoverlapping on the cost-effectiveness plane and resulted in very different incremental cost-effectiveness ratios ($79,300 [95% CI 72,500-87,600] v. $139,700 [95% CI 79,900-182,800] per quality-adjusted life-year [QALY] gained). Input sets on the Pareto frontier spanned both regions ($79,000 [95% CI 64,900-156,200] per QALY gained). The TAVR model yielded similar results. Choices in generating a summary GOF score may result in different health economic conclusions. The Pareto frontier approach eliminates the need to make these choices by using an intuitive and transparent notion of optimality as the basis for identifying best-fitting input sets. © The Author(s) 2014.

Estimation of the shape parameter of a generalized Pareto distribution based on a transformation to Pareto distributed variables

OpenAIRE

van Zyl, J. Martin

2012-01-01

Random variables of the generalized Pareto distribution, can be transformed to that of the Pareto distribution. Explicit expressions exist for the maximum likelihood estimators of the parameters of the Pareto distribution. The performance of the estimation of the shape parameter of generalized Pareto distributed using transformed observations, based on the probability weighted method is tested. It was found to improve the performance of the probability weighted estimator and performs good wit...

Record Values of a Pareto Distribution.

Science.gov (United States)

Ahsanullah, M.

The record values of the Pareto distribution, labelled Pareto (II) (alpha, beta, nu), are reviewed. The best linear unbiased estimates of the parameters in terms of the record values are provided. The prediction of the sth record value based on the first m (s>m) record values are obtained. A classical Pareto distribution provides reasonably…

Monopoly, Pareto and Ramsey mark-ups

OpenAIRE

Ten Raa, T.

2009-01-01

Monopoly prices are too high. It is a price level problem, in the sense that the relative mark-ups have Ramsey optimal proportions, at least for independent constant elasticity demands. I show that this feature of monopoly prices breaks down the moment one demand is replaced by the textbook linear demand or, even within the constant elasticity framework, dependence is introduced. The analysis provides a single Generalized Inverse Elasticity Rule for the problems of monopoly, Pareto and Ramsey.

Solving multi-objective job shop problem using nature-based algorithms: new Pareto approximation features

Directory of Open Access Journals (Sweden)

Jarosław Rudy

2015-01-01

Full Text Available In this paper the job shop scheduling problem (JSP with minimizing two criteria simultaneously is considered. JSP is frequently used model in real world applications of combinatorial optimization. Multi-objective job shop problems (MOJSP were rarely studied. We implement and compare two multi-agent nature-based methods, namely ant colony optimization (ACO and genetic algorithm (GA for MOJSP. Both of those methods employ certain technique, taken from the multi-criteria decision analysis in order to establish ranking of solutions. ACO and GA differ in a method of keeping information about previously found solutions and their quality, which affects the course of the search. In result, new features of Pareto approximations provided by said algorithms are observed: aside from the slight superiority of the ACO method the Pareto frontier approximations provided by both methods are disjoint sets. Thus, both methods can be used to search mutually exclusive areas of the Pareto frontier.

Finding a pareto-optimal solution for multi-region models subject to capital trade and spillover externalities

Energy Technology Data Exchange (ETDEWEB)

Leimbach, Marian [Potsdam-Institut fuer Klimafolgenforschung e.V., Potsdam (Germany); Eisenack, Klaus [Oldenburg Univ. (Germany). Dept. of Economics and Statistics

2008-11-15

In this paper we present an algorithm that deals with trade interactions within a multi-region model. In contrast to traditional approaches this algorithm is able to handle spillover externalities. Technological spillovers are expected to foster the diffusion of new technologies, which helps to lower the cost of climate change mitigation. We focus on technological spillovers which are due to capital trade. The algorithm of finding a pareto-optimal solution in an intertemporal framework is embedded in a decomposed optimization process. The paper analyzes convergence and equilibrium properties of this algorithm. In the final part of the paper, we apply the algorithm to investigate possible impacts of technological spillovers. While benefits of technological spillovers are significant for the capital-importing region, benefits for the capital-exporting region depend on the type of regional disparities and the resulting specialization and terms-of-trade effects. (orig.)

Pareto joint inversion of 2D magnetotelluric and gravity data

Science.gov (United States)

Miernik, Katarzyna; Bogacz, Adrian; Kozubal, Adam; Danek, Tomasz; Wojdyła, Marek

2015-04-01

In this contribution, the first results of the "Innovative technology of petrophysical parameters estimation of geological media using joint inversion algorithms" project were described. At this stage of the development, Pareto joint inversion scheme for 2D MT and gravity data was used. Additionally, seismic data were provided to set some constrains for the inversion. Sharp Boundary Interface(SBI) approach and description model with set of polygons were used to limit the dimensionality of the solution space. The main engine was based on modified Particle Swarm Optimization(PSO). This algorithm was properly adapted to handle two or more target function at once. Additional algorithm was used to eliminate non- realistic solution proposals. Because PSO is a method of stochastic global optimization, it requires a lot of proposals to be evaluated to find a single Pareto solution and then compose a Pareto front. To optimize this stage parallel computing was used for both inversion engine and 2D MT forward solver. There are many advantages of proposed solution of joint inversion problems. First of all, Pareto scheme eliminates cumbersome rescaling of the target functions, that can highly affect the final solution. Secondly, the whole set of solution is created in one optimization run, providing a choice of the final solution. This choice can be based off qualitative data, that are usually very hard to be incorporated into the regular inversion schema. SBI parameterisation not only limits the problem of dimensionality, but also makes constraining of the solution easier. At this stage of work, decision to test the approach using MT and gravity data was made, because this combination is often used in practice. It is important to mention, that the general solution is not limited to this two methods and it is flexible enough to be used with more than two sources of data. Presented results were obtained for synthetic models, imitating real geological conditions, where

Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1

Science.gov (United States)

Langenbrunner, B.; Neelin, J. D.

2017-09-01

Global climate models (GCMs) are examples of high-dimensional input-output systems, where model output is a function of many variables, and an update in model physics commonly improves performance in one objective function (i.e., measure of model performance) at the expense of degrading another. Here concepts from multiobjective optimization in the engineering literature are used to investigate parameter sensitivity and optimization in the face of such trade-offs. A metamodeling technique called cut high-dimensional model representation (cut-HDMR) is leveraged in the context of multiobjective optimization to improve GCM simulation of the tropical Pacific climate, focusing on seasonal precipitation, column water vapor, and skin temperature. An evolutionary algorithm is used to solve for Pareto fronts, which are surfaces in objective function space along which trade-offs in GCM performance occur. This approach allows the modeler to visualize trade-offs quickly and identify the physics at play. In some cases, Pareto fronts are small, implying that trade-offs are minimal, optimal parameter value choices are more straightforward, and the GCM is well-functioning. In all cases considered here, the control run was found not to be Pareto-optimal (i.e., not on the front), highlighting an opportunity for model improvement through objectively informed parameter selection. Taylor diagrams illustrate that these improvements occur primarily in field magnitude, not spatial correlation, and they show that specific parameter updates can improve fields fundamental to tropical moist processes—namely precipitation and skin temperature—without significantly impacting others. These results provide an example of how basic elements of multiobjective optimization can facilitate pragmatic GCM tuning processes.

Pareto-Efficiency, Hayek’s Marvel, and the Invisible Executor

OpenAIRE

Kakarot-Handtke, Egmont

2014-01-01

This non-technical contribution to the RWER-Blog deals with the interrelations of market clearing, efficient information processing through the price system, and distribution. The point of entry is a transparent example of Pareto-efficiency taken from the popular book How Markets Fail.

Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems

International Nuclear Information System (INIS)

Cao, Dingzhou; Murat, Alper; Chinnam, Ratna Babu

2013-01-01

This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliability or minimizing the cost given certain constraints. The few studies that treated redundancy allocation problem as a multi-objective optimization problem relied on meta-heuristic solution approaches. However, meta-heuristic approaches have significant limitations: they do not guarantee that Pareto points are optimal and, more importantly, they may not identify all the Pareto-optimal points. In this paper, we treat redundancy allocation problem as a multi-objective problem, as is typical in practice. We decompose the original problem into several multi-objective sub-problems, efficiently and exactly solve sub-problems, and then systematically combine the solutions. The decomposition-based approach can efficiently generate all the Pareto-optimal solutions for redundancy allocation problems. Experimental results demonstrate the effectiveness and efficiency of the proposed method over meta-heuristic methods on a numerical example taken from the literature.

Pareto-optimal estimates that constrain mean California precipitation change

Science.gov (United States)

Langenbrunner, B.; Neelin, J. D.

2017-12-01

Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

On the Truncated Pareto Distribution with applications

OpenAIRE

Zaninetti, Lorenzo; Ferraro, Mario

2008-01-01

The Pareto probability distribution is widely applied in different fields such us finance, physics, hydrology, geology and astronomy. This note deals with an application of the Pareto distribution to astrophysics and more precisely to the statistical analysis of mass of stars and of diameters of asteroids. In particular a comparison between the usual Pareto distribution and its truncated version is presented. Finally a possible physical mechanism that produces Pareto tails for the distributio...

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

Science.gov (United States)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

Multi-objective optimization of a series–parallel system using GPSIA

International Nuclear Information System (INIS)

Okafor, Ekene Gabriel; Sun Youchao

2012-01-01

The optimal solution of a multi-objective optimization problem (MOP) corresponds to a Pareto set that is characterized by a tradeoff between objectives. Genetic Pareto Set Identification Algorithm (GPSIA) proposed for reliability-redundant MOPs is a hybrid technique which combines genetic and heuristic principles to generate non-dominated solutions. Series–parallel system with active redundancy is studied in this paper. Reliability and cost were the research objective functions subject to cost and weight constraints. The results reveal an evenly distributed non-dominated front. The distances between successive Pareto points were used to evaluate the general performance of the method. Plots were also used to show the computational results for the type of system studied and the robustness of the technique is discussed in comparison with NSGA-II and SPEA-2.

Existence of pareto equilibria for multiobjective games without compactness

OpenAIRE

Shiraishi, Yuya; Kuroiwa, Daishi

2013-01-01

In this paper, we investigate the existence of Pareto and weak Pareto equilibria for multiobjective games without compactness. By employing an existence theorem of Pareto equilibria due to Yu and Yuan([10]), several existence theorems of Pareto and weak Pareto equilibria for the multiobjective games are established in a similar way to Flores-B´azan.

The Incompatibility of Pareto Optimality and Dominant-Strategy Incentive Compatibility in Sufficiently-Anonymous Budget-Constrained Quasilinear Settings

Directory of Open Access Journals (Sweden)

Rica Gonen

2013-11-01

Full Text Available We analyze the space of deterministic, dominant-strategy incentive compatible, individually rational and Pareto optimal combinatorial auctions. We examine a model with multidimensional types, nonidentical items, private values and quasilinear preferences for the players with one relaxation; the players are subject to publicly-known budget constraints. We show that the space includes dictatorial mechanisms and that if dictatorial mechanisms are ruled out by a natural anonymity property, then an impossibility of design is revealed. The same impossibility naturally extends to other abstract mechanisms with an arbitrary outcome set if one maintains the original assumptions of players with quasilinear utilities, public budgets and nonnegative prices.

The Primary Experiments of an Analysis of Pareto Solutions for Conceptual Design Optimization Problem of Hybrid Rocket Engine

Science.gov (United States)

Kudo, Fumiya; Yoshikawa, Tomohiro; Furuhashi, Takeshi

Recentry, Multi-objective Genetic Algorithm, which is the application of Genetic Algorithm to Multi-objective Optimization Problems is focused on in the engineering design field. In this field, the analysis of design variables in the acquired Pareto solutions, which gives the designers useful knowledge in the applied problem, is important as well as the acquisition of advanced solutions. This paper proposes a new visualization method using Isomap which visualizes the geometric distances of solutions in the design variable space considering their distances in the objective space. The proposed method enables a user to analyze the design variables of the acquired solutions considering their relationship in the objective space. This paper applies the proposed method to the conceptual design optimization problem of hybrid rocket engine and studies the effectiveness of the proposed method.

Studies on generalized kinetic model and Pareto optimization of a product-driven self-cycling bioprocess.

Science.gov (United States)

Sun, Kaibiao; Kasperski, Andrzej; Tian, Yuan

2014-10-01

The aim of this study is the optimization of a product-driven self-cycling bioprocess and presentation of a way to determine the best possible decision variables out of a set of alternatives based on the designed model. Initially, a product-driven generalized kinetic model, which allows a flexible choice of the most appropriate kinetics is designed and analysed. The optimization problem is given as the bi-objective one, where maximization of biomass productivity and minimization of unproductive loss of substrate are the objective functions. Then, the Pareto fronts are calculated for exemplary kinetics. It is found that in the designed bioprocess, a decrease of emptying/refilling fraction and an increase of substrate feeding concentration cause an increase of the biomass productivity. An increase of emptying/refilling fraction and a decrease of substrate feeding concentration cause a decrease of unproductive loss of substrate. The preferred solutions are calculated using the minimum distance from an ideal solution method, while giving proposals of their modifications derived from a decision maker's reactions to the generated solutions.

[Origination of Pareto distribution in complex dynamic systems].

Science.gov (United States)

Chernavskiĭ, D S; Nikitin, A P; Chernavskaia, O D

2008-01-01

The Pareto distribution, whose probability density function can be approximated at sufficiently great chi as rho(chi) - chi(-alpha), where alpha > or = 2, is of crucial importance from both the theoretical and practical point of view. The main reason is its qualitative distinction from the normal (Gaussian) distribution. Namely, the probability of high deviations appears to be significantly higher. The conception of the universal applicability of the Gauss law remains to be widely distributed despite the lack of objective confirmation of this notion in a variety of application areas. The origin of the Pareto distribution in dynamic systems located in the gaussian noise field is considered. A simple one-dimensional model is discussed where the system response in a rather wide interval of the variable can be quite precisely approximated by this distribution.

MO-G-304-04: Generating Well-Dispersed Representations of the Pareto Front for Multi-Criteria Optimization in Radiation Treatment Planning

Energy Technology Data Exchange (ETDEWEB)

Kirlik, G; Zhang, H [University of Maryland School of Medicine, Baltimore, MD (United States)

2015-06-15

Purpose: To present a novel multi-criteria optimization (MCO) solution approach that generates well-dispersed representation of the Pareto front for radiation treatment planning. Methods: Different algorithms have been proposed and implemented in commercial planning software to generate MCO plans for external-beam radiation therapy. These algorithms consider convex optimization problems. We propose a grid-based algorithm to generate well-dispersed treatment plans over Pareto front. Our method is able to handle nonconvexity in the problem to deal with dose-volume objectives/constraints, biological objectives, such as equivalent uniform dose (EUD), tumor control probability (TCP), normal tissue complication probability (NTCP), etc. In addition, our algorithm is able to provide single MCO plan when clinicians are targeting narrow bounds of objectives for patients. In this situation, usually none of the generated plans were within the bounds and a solution is difficult to identify via manual navigation. We use the subproblem formulation utilized in the grid-based algorithm to obtain a plan within the specified bounds. The subproblem aims to generate a solution that maps into the rectangle defined by the bounds. If such a solution does not exist, it generates the solution closest to the rectangle. We tested our method with 10 locally advanced head and neck cancer cases. Results: 8 objectives were used including 3 different objectives for primary target volume, high-risk and low-risk target volumes, and 5 objectives for each of the organs-at-risk (OARs) (two parotids, spinal cord, brain stem and oral cavity). Given tight bounds, uniform dose was achieved for all targets while as much as 26% improvement was achieved in OAR sparing comparing to clinical plans without MCO and previously proposed MCO method. Conclusion: Our method is able to obtain well-dispersed treatment plans to attain better approximation for convex and nonconvex Pareto fronts. Single treatment plan can

TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

Science.gov (United States)

Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

2018-01-01

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

A clinical distance measure for evaluating treatment plan quality difference with Pareto fronts in radiotherapy

Directory of Open Access Journals (Sweden)

Kristoffer Petersson

2017-07-01

Full Text Available We present a clinical distance measure for Pareto front evaluation studies in radiotherapy, which we show strongly correlates (r = 0.74 and 0.90 with clinical plan quality evaluation. For five prostate cases, sub-optimal treatment plans located at a clinical distance value of >0.32 (0.28–0.35 from fronts of Pareto optimal plans, were assessed to be of lower plan quality by our (12 observers (p < .05. In conclusion, the clinical distance measure can be used to determine if the difference between a front and a given plan (or between different fronts corresponds to a clinically significant plan quality difference.

On quasistability radius of a vector trajectorial problem with a principle of optimality generalizing Pareto and lexicographic principles

Directory of Open Access Journals (Sweden)

Sergey E. Bukhtoyarov

2005-05-01

Full Text Available A multicriterion linear combinatorial problem with a parametric principle of optimality is considered. This principle is defined by a partitioning of partial criteria onto Pareto preference relation groups within each group and the lexicographic preference relation between them. Quasistability of the problem is investigated. This type of stability is a discrete analog of Hausdorff lower semi-continuity of the multiple-valued mapping that defines the choice function. A formula of quasistability radius is derived for the case of the metric l∞. Some known results are stated as corollaries. Mathematics Subject Classification 2000: 90C05, 90C10, 90C29, 90C31.

Visualising Pareto-optimal trade-offs helps move beyond monetary-only criteria for water management decisions

Science.gov (United States)

Hurford, Anthony; Harou, Julien

2014-05-01

Water related eco-system services are important to the livelihoods of the poorest sectors of society in developing countries. Degradation or loss of these services can increase the vulnerability of people decreasing their capacity to support themselves. New approaches to help guide water resources management decisions are needed which account for the non-market value of ecosystem goods and services. In case studies from Brazil and Kenya we demonstrate the capability of many objective Pareto-optimal trade-off analysis to help decision makers balance economic and non-market benefits from the management of existing multi-reservoir systems. A multi-criteria search algorithm is coupled to a water resources management simulator of each basin to generate a set of Pareto-approximate trade-offs representing the best case management decisions. In both cases, volume dependent reservoir release rules are the management decisions being optimised. In the Kenyan case we further assess the impacts of proposed irrigation investments, and how the possibility of new investments impacts the system's trade-offs. During the multi-criteria search (optimisation), performance of different sets of management decisions (policies) is assessed against case-specific objective functions representing provision of water supply and irrigation, hydropower generation and maintenance of ecosystem services. Results are visualised as trade-off surfaces to help decision makers understand the impacts of different policies on a broad range of stakeholders and to assist in decision-making. These case studies show how the approach can reveal unexpected opportunities for win-win solutions, and quantify the trade-offs between investing to increase agricultural revenue and negative impacts on protected ecosystems which support rural livelihoods.

Global shape optimization of airfoil using multi-objective genetic algorithm

International Nuclear Information System (INIS)

Lee, Ju Hee; Lee, Sang Hwan; Park, Kyoung Woo

2005-01-01

The shape optimization of an airfoil has been performed for an incompressible viscous flow. In this study, Pareto frontier sets, which are global and non-dominated solutions, can be obtained without various weighting factors by using the multi-objective genetic algorithm. An NACA0012 airfoil is considered as a baseline model, and the profile of the airfoil is parameterized and rebuilt with four Bezier curves. Two curves, from leading to maximum thickness, are composed of five control points and the rest, from maximum thickness to tailing edge, are composed of four control points. There are eighteen design variables and two objective functions such as the lift and drag coefficients. A generation is made up of forty-five individuals. After fifteenth evolutions, the Pareto individuals of twenty can be achieved. One Pareto, which is the best of the reduction of the drag force, improves its drag to 13% and lift-drag ratio to 2%. Another Pareto, however, which is focused on increasing the lift force, can improve its lift force to 61%, while sustaining its drag force, compared to those of the baseline model

Global shape optimization of airfoil using multi-objective genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Lee, Ju Hee; Lee, Sang Hwan [Hanyang Univ., Seoul (Korea, Republic of); Park, Kyoung Woo [Hoseo Univ., Asan (Korea, Republic of)

2005-10-01

The shape optimization of an airfoil has been performed for an incompressible viscous flow. In this study, Pareto frontier sets, which are global and non-dominated solutions, can be obtained without various weighting factors by using the multi-objective genetic algorithm. An NACA0012 airfoil is considered as a baseline model, and the profile of the airfoil is parameterized and rebuilt with four Bezier curves. Two curves, from leading to maximum thickness, are composed of five control points and the rest, from maximum thickness to tailing edge, are composed of four control points. There are eighteen design variables and two objective functions such as the lift and drag coefficients. A generation is made up of forty-five individuals. After fifteenth evolutions, the Pareto individuals of twenty can be achieved. One Pareto, which is the best of the reduction of the drag force, improves its drag to 13% and lift-drag ratio to 2%. Another Pareto, however, which is focused on increasing the lift force, can improve its lift force to 61%, while sustaining its drag force, compared to those of the baseline model.

A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.

Science.gov (United States)

Yang, Shaofu; Liu, Qingshan; Wang, Jun

2018-04-01

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

Implementation of strength pareto evolutionary algorithm II in the multiobjective burnable poison placement optimization of KWU pressurized water reactor

International Nuclear Information System (INIS)

Gharari, Rahman; Poursalehi, Navid; Abbasi, Mohmmadreza; Aghale, Mahdi

2016-01-01

In this research, for the first time, a new optimization method, i.e., strength Pareto evolutionary algorithm II (SPEA-II), is developed for the burnable poison placement (BPP) optimization of a nuclear reactor core. In the BPP problem, an optimized placement map of fuel assemblies with burnable poison is searched for a given core loading pattern according to defined objectives. In this work, SPEA-II coupled with a nodal expansion code is used for solving the BPP problem of Kraftwerk Union AG (KWU) pressurized water reactor. Our optimization goal for the BPP is to achieve a greater multiplication factor (K-e-f-f) for gaining possible longer operation cycles along with more flattening of fuel assembly relative power distribution, considering a safety constraint on the radial power peaking factor. For appraising the proposed methodology, the basic approach, i.e., SPEA, is also developed in order to compare obtained results. In general, results reveal the acceptance performance and high strength of SPEA, particularly its new version, i.e., SPEA-II, in achieving a semioptimized loading pattern for the BPP optimization of KWU pressurized water reactor

Implementation of strength pareto evolutionary algorithm II in the multiobjective burnable poison placement optimization of KWU pressurized water reactor

Energy Technology Data Exchange (ETDEWEB)

Gharari, Rahman [Nuclear Science and Technology Research Institute (NSTRI), Tehran (Iran, Islamic Republic of); Poursalehi, Navid; Abbasi, Mohmmadreza; Aghale, Mahdi [Nuclear Engineering Dept, Shahid Beheshti University, Tehran (Iran, Islamic Republic of)

2016-10-15

In this research, for the first time, a new optimization method, i.e., strength Pareto evolutionary algorithm II (SPEA-II), is developed for the burnable poison placement (BPP) optimization of a nuclear reactor core. In the BPP problem, an optimized placement map of fuel assemblies with burnable poison is searched for a given core loading pattern according to defined objectives. In this work, SPEA-II coupled with a nodal expansion code is used for solving the BPP problem of Kraftwerk Union AG (KWU) pressurized water reactor. Our optimization goal for the BPP is to achieve a greater multiplication factor (K-e-f-f) for gaining possible longer operation cycles along with more flattening of fuel assembly relative power distribution, considering a safety constraint on the radial power peaking factor. For appraising the proposed methodology, the basic approach, i.e., SPEA, is also developed in order to compare obtained results. In general, results reveal the acceptance performance and high strength of SPEA, particularly its new version, i.e., SPEA-II, in achieving a semioptimized loading pattern for the BPP optimization of KWU pressurized water reactor.

Aerodynamic multi-objective integrated optimization based on principal component analysis

Directory of Open Access Journals (Sweden)

Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis.

Science.gov (United States)

Hsiao, Ko-Jen; Xu, Kevin S; Calder, Jeff; Hero, Alfred O

2016-06-01

We consider the problem of identifying patterns in a data set that exhibits anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g., as measured by the nearest neighbor Euclidean distances between a test sample and the training samples. In many application domains, there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such cases, multiple dissimilarity measures can be defined, including nonmetric measures, and one can test for anomalies by scalarizing using a nonnegative linear combination of them. If the relative importance of the different dissimilarity measures are not known in advance, as in many anomaly detection applications, the anomaly detection algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we propose a method for similarity-based anomaly detection using a novel multicriteria dissimilarity measure, the Pareto depth. The proposed Pareto depth analysis (PDA) anomaly detection algorithm uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach is provably better than using linear combinations of the criteria, and shows superior performance on experiments with synthetic and real data sets.

An extension of the directed search domain algorithm to bilevel optimization

Science.gov (United States)

Wang, Kaiqiang; Utyuzhnikov, Sergey V.

2017-08-01

A method is developed for generating a well-distributed Pareto set for the upper level in bilevel multiobjective optimization. The approach is based on the Directed Search Domain (DSD) algorithm, which is a classical approach for generation of a quasi-evenly distributed Pareto set in multiobjective optimization. The approach contains a double-layer optimizer designed in a specific way under the framework of the DSD method. The double-layer optimizer is based on bilevel single-objective optimization and aims to find a unique optimal Pareto solution rather than generate the whole Pareto frontier on the lower level in order to improve the optimization efficiency. The proposed bilevel DSD approach is verified on several test cases, and a relevant comparison against another classical approach is made. It is shown that the approach can generate a quasi-evenly distributed Pareto set for the upper level with relatively low time consumption.

The exponentiated generalized Pareto distribution | Adeyemi | Ife ...

African Journals Online (AJOL)

Recently Gupta et al. (1998) introduced the exponentiated exponential distribution as a generalization of the standard exponential distribution. In this paper, we introduce a three-parameter generalized Pareto distribution, the exponentiated generalized Pareto distribution (EGP). We present a comprehensive treatment of the ...

Optimal Power Flow by Interior Point and Non Interior Point Modern Optimization Algorithms

Directory of Open Access Journals (Sweden)

Marcin Połomski

2013-03-01

Full Text Available The idea of optimal power flow (OPF is to determine the optimal settings for control variables while respecting various constraints, and in general it is related to power system operational and planning optimization problems. A vast number of optimization methods have been applied to solve the OPF problem, but their performance is highly dependent on the size of a power system being optimized. The development of the OPF recently has tracked significant progress both in numerical optimization techniques and computer techniques application. In recent years, application of interior point methods to solve OPF problem has been paid great attention. This is due to the fact that IP methods are among the fastest algorithms, well suited to solve large-scale nonlinear optimization problems. This paper presents the primal-dual interior point method based optimal power flow algorithm and new variant of the non interior point method algorithm with application to optimal power flow problem. Described algorithms were implemented in custom software. The experiments show the usefulness of computational software and implemented algorithms for solving the optimal power flow problem, including the system model sizes comparable to the size of the National Power System.

Multi-objective optimization of Stirling engine using Finite Physical Dimensions Thermodynamics (FPDT) method

International Nuclear Information System (INIS)

Li, Ruijie; Grosu, Lavinia; Queiros-Conde, Diogo

2016-01-01

Highlights: • A gamma Stirling engine has been optimized using FPDT method by multi-objective criteria. • Genetic algorithm and decision making methods were used to get Pareto frontier and optimum points. • It shows: total thermal conductance, hot temperature, stroke and diameter ratios can be improved. - Abstract: In this paper, a solar energy powered gamma type SE has been optimized using Finite Physical Dimensions Thermodynamics (FPDT) method by multi-objective criteria. Genetic algorithm was used to get the Pareto frontier, and optimum points were obtained using the decision making methods of LINMAP and TOPSIS. The optimization results have been compared with those obtained using the ecological method. It was shown that the multi-objective optimization in this paper has a better balance among the optimizing criteria (maximum mechanical power, maximum thermal efficiency and minimum entropy generation flow). The effects of the hot source temperature and the total thermal conductance of the engine on the Pareto frontier have been also studied. This sensibility study shows that an increase in the hot reservoir temperature can increase the output mechanical power, the thermal efficiency of the engine, but also the entropy generation rate. In addition to this, an increase of the total thermal conductance of the engine can strongly increase the output mechanical power and only slightly increase the thermal efficiency. These results allow us to improve the engine performance after some modifications as geometrical dimensions (diameter, stroke, heat exchange surface, etc.) and physical parameters (temperature, thermal conductivity).

Investigating multi-objective fluence and beam orientation IMRT optimization

Science.gov (United States)

Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

2017-07-01

Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters

A Pareto scale-inflated outlier model and its Bayesian analysis

OpenAIRE

Scollnik, David P. M.

2016-01-01

This paper develops a Pareto scale-inflated outlier model. This model is intended for use when data from some standard Pareto distribution of interest is suspected to have been contaminated with a relatively small number of outliers from a Pareto distribution with the same shape parameter but with an inflated scale parameter. The Bayesian analysis of this Pareto scale-inflated outlier model is considered and its implementation using the Gibbs sampler is discussed. The paper contains three wor...

Optimal transmitter power of an intersatellite optical communication system with reciprocal Pareto fading.

Science.gov (United States)

Liu, Xian

2010-02-10

This paper shows that optical signal transmission over intersatellite links with swaying transmitters can be described as an equivalent fading model. In this model, the instantaneous signal-to-noise ratio is stochastic and follows the reciprocal Pareto distribution. With this model, we show that the transmitter power can be minimized, subject to a specified outage probability, by appropriately adjusting some system parameters, such as the transmitter gain.

Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.

Science.gov (United States)

Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon

2017-01-01

In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.

A note on the estimation of the Pareto efficient set for multiobjective matrix permutation problems.

Science.gov (United States)

Brusco, Michael J; Steinley, Douglas

2012-02-01

There are a number of important problems in quantitative psychology that require the identification of a permutation of the n rows and columns of an n × n proximity matrix. These problems encompass applications such as unidimensional scaling, paired-comparison ranking, and anti-Robinson forms. The importance of simultaneously incorporating multiple objective criteria in matrix permutation applications is well recognized in the literature; however, to date, there has been a reliance on weighted-sum approaches that transform the multiobjective problem into a single-objective optimization problem. Although exact solutions to these single-objective problems produce supported Pareto efficient solutions to the multiobjective problem, many interesting unsupported Pareto efficient solutions may be missed. We illustrate the limitation of the weighted-sum approach with an example from the psychological literature and devise an effective heuristic algorithm for estimating both the supported and unsupported solutions of the Pareto efficient set. © 2011 The British Psychological Society.

Trade-off bounds for the Pareto surface approximation in multi-criteria IMRT planning

International Nuclear Information System (INIS)

Serna, J I; Monz, M; Kuefer, K H; Thieke, C

2009-01-01

One approach to multi-criteria IMRT planning is to automatically calculate a data set of Pareto-optimal plans for a given planning problem in a first phase, and then interactively explore the solution space and decide on the clinically best treatment plan in a second phase. The challenge of computing the plan data set is to ensure that all clinically meaningful plans are covered and that as many clinically irrelevant plans as possible are excluded to keep computation times within reasonable limits. In this work, we focus on the approximation of the clinically relevant part of the Pareto surface, the process that constitutes the first phase. It is possible that two plans on the Pareto surface have a small, clinically insignificant difference in one criterion and a significant difference in another criterion. For such cases, only the plan that is clinically clearly superior should be included into the data set. To achieve this during the Pareto surface approximation, we propose to introduce bounds that restrict the relative quality between plans, the so-called trade-off bounds. We show how to integrate these trade-off bounds into the approximation scheme and study their effects. The proposed scheme is applied to two artificial cases and one clinical case of a paraspinal tumor. For all cases, the quality of the Pareto surface approximation is measured with respect to the number of computed plans, and the range of values occurring in the approximation for different criteria is compared. Through enforcing trade-off bounds, the scheme disregards clinically irrelevant plans during the approximation. Thereby, the number of plans necessary to achieve a good approximation quality can be significantly reduced. Thus, trade-off bounds are an effective tool to focus the planning and to reduce computation time.

Trade-off bounds for the Pareto surface approximation in multi-criteria IMRT planning.

Science.gov (United States)

Serna, J I; Monz, M; Küfer, K H; Thieke, C

2009-10-21

One approach to multi-criteria IMRT planning is to automatically calculate a data set of Pareto-optimal plans for a given planning problem in a first phase, and then interactively explore the solution space and decide on the clinically best treatment plan in a second phase. The challenge of computing the plan data set is to ensure that all clinically meaningful plans are covered and that as many clinically irrelevant plans as possible are excluded to keep computation times within reasonable limits. In this work, we focus on the approximation of the clinically relevant part of the Pareto surface, the process that constitutes the first phase. It is possible that two plans on the Pareto surface have a small, clinically insignificant difference in one criterion and a significant difference in another criterion. For such cases, only the plan that is clinically clearly superior should be included into the data set. To achieve this during the Pareto surface approximation, we propose to introduce bounds that restrict the relative quality between plans, the so-called trade-off bounds. We show how to integrate these trade-off bounds into the approximation scheme and study their effects. The proposed scheme is applied to two artificial cases and one clinical case of a paraspinal tumor. For all cases, the quality of the Pareto surface approximation is measured with respect to the number of computed plans, and the range of values occurring in the approximation for different criteria is compared. Through enforcing trade-off bounds, the scheme disregards clinically irrelevant plans during the approximation. Thereby, the number of plans necessary to achieve a good approximation quality can be significantly reduced. Thus, trade-off bounds are an effective tool to focus the planning and to reduce computation time.

Multi-objective optimization of a joule cycle for re-liquefaction of the Liquefied Natural Gas

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Babaelahi, M.

2011-01-01

Highlights: → A typical LNG boil off gas re-liquefaction plant system is optimized. → Objective functions based on thermodynamic and thermoeconomic analysis are obtained. → The cost of the system product and the exergetic efficiency are optimized, simultaneously. → A decision-making process for selection of the final optimal design is introduced. → Results obtained using various optimization scenarios are compared and discussed. - Abstract: A LNG re-liquefaction plant is optimized with a multi-objective approach which simultaneously considers exergetic and exergoeconomic objectives. In this regard, optimization is performed in order to maximize the exergetic efficiency of plant and minimize the unit cost of the system product (refrigeration effect), simultaneously. Thermodynamic modeling is performed based on energy and exergy analyses, while an exergoeconomic model based on the total revenue requirement (TRR) are developed. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms namely NSGA-II. This approach which is based on the Genetic Algorithm is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained and a final optimal solution is selected in a decision-making process. An example of decision-making process for selection of the final solution from the available optimal points of the Pareto frontier is presented here. The feature of selected final optimal system is compared with corresponding features of the base case and exergoeconomic single-objective optimized systems and discussed.

Pareto-Lognormal Modeling of Known and Unknown Metal Resources. II. Method Refinement and Further Applications

International Nuclear Information System (INIS)

Agterberg, Frits

2017-01-01

Pareto-lognormal modeling of worldwide metal deposit size–frequency distributions was proposed in an earlier paper (Agterberg in Nat Resour 26:3–20, 2017). In the current paper, the approach is applied to four metals (Cu, Zn, Au and Ag) and a number of model improvements are described and illustrated in detail for copper and gold. The new approach has become possible because of the very large inventory of worldwide metal deposit data recently published by Patiño Douce (Nat Resour 25:97–124, 2016c). Worldwide metal deposits for Cu, Zn and Ag follow basic lognormal size–frequency distributions that form straight lines on lognormal Q–Q plots. Au deposits show a departure from the straight-line model in the vicinity of their median size. Both largest and smallest deposits for the four metals taken as examples exhibit hyperbolic size–frequency relations and their Pareto coefficients are determined by fitting straight lines on log rank–log size plots. As originally pointed out by Patiño Douce (Nat Resour Res 25:365–387, 2016d), the upper Pareto tail cannot be distinguished clearly from the tail of what would be a secondary lognormal distribution. The method previously used in Agterberg (2017) for fitting the bridge function separating the largest deposit size–frequency Pareto tail from the basic lognormal is significantly improved in this paper. A new method is presented for estimating the approximate deposit size value at which the upper tail Pareto comes into effect. Although a theoretical explanation of the proposed Pareto-lognormal distribution model is not a required condition for its applicability, it is shown that existing double Pareto-lognormal models based on Brownian motion generalizations of the multiplicative central limit theorem are not applicable to worldwide metal deposits. Neither are various upper tail frequency amplification models in their present form. Although a physicochemical explanation remains possible, it is argued that

Pareto-Lognormal Modeling of Known and Unknown Metal Resources. II. Method Refinement and Further Applications

Energy Technology Data Exchange (ETDEWEB)

Agterberg, Frits, E-mail: agterber@nrcan.gc.ca [Geological Survey of Canada (Canada)

2017-07-01

Pareto-lognormal modeling of worldwide metal deposit size–frequency distributions was proposed in an earlier paper (Agterberg in Nat Resour 26:3–20, 2017). In the current paper, the approach is applied to four metals (Cu, Zn, Au and Ag) and a number of model improvements are described and illustrated in detail for copper and gold. The new approach has become possible because of the very large inventory of worldwide metal deposit data recently published by Patiño Douce (Nat Resour 25:97–124, 2016c). Worldwide metal deposits for Cu, Zn and Ag follow basic lognormal size–frequency distributions that form straight lines on lognormal Q–Q plots. Au deposits show a departure from the straight-line model in the vicinity of their median size. Both largest and smallest deposits for the four metals taken as examples exhibit hyperbolic size–frequency relations and their Pareto coefficients are determined by fitting straight lines on log rank–log size plots. As originally pointed out by Patiño Douce (Nat Resour Res 25:365–387, 2016d), the upper Pareto tail cannot be distinguished clearly from the tail of what would be a secondary lognormal distribution. The method previously used in Agterberg (2017) for fitting the bridge function separating the largest deposit size–frequency Pareto tail from the basic lognormal is significantly improved in this paper. A new method is presented for estimating the approximate deposit size value at which the upper tail Pareto comes into effect. Although a theoretical explanation of the proposed Pareto-lognormal distribution model is not a required condition for its applicability, it is shown that existing double Pareto-lognormal models based on Brownian motion generalizations of the multiplicative central limit theorem are not applicable to worldwide metal deposits. Neither are various upper tail frequency amplification models in their present form. Although a physicochemical explanation remains possible, it is argued that

Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

Science.gov (United States)

Penrod, Nadia M; Greene, Casey S; Moore, Jason H

2014-01-01

Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination

Pareto versus lognormal: a maximum entropy test.

Science.gov (United States)

Bee, Marco; Riccaboni, Massimo; Schiavo, Stefano

2011-08-01

It is commonly found that distributions that seem to be lognormal over a broad range change to a power-law (Pareto) distribution for the last few percentiles. The distributions of many physical, natural, and social events (earthquake size, species abundance, income and wealth, as well as file, city, and firm sizes) display this structure. We present a test for the occurrence of power-law tails in statistical distributions based on maximum entropy. This methodology allows one to identify the true data-generating processes even in the case when it is neither lognormal nor Pareto. The maximum entropy approach is then compared with other widely used methods and applied to different levels of aggregation of complex systems. Our results provide support for the theory that distributions with lognormal body and Pareto tail can be generated as mixtures of lognormally distributed units.

Pareto 80/20 Law: Derivation via Random Partitioning

Science.gov (United States)

Lipovetsky, Stan

2009-01-01

The Pareto 80/20 Rule, also known as the Pareto principle or law, states that a small number of causes (20%) is responsible for a large percentage (80%) of the effect. Although widely recognized as a heuristic rule, this proportion has not been theoretically based. The article considers derivation of this 80/20 rule and some other standard…

A multicriteria framework with voxel-dependent parameters for radiotherapy treatment plan optimization

International Nuclear Information System (INIS)

Zarepisheh, Masoud; Uribe-Sanchez, Andres F.; Li, Nan; Jia, Xun; Jiang, Steve B.

2014-01-01

Purpose: To establish a new mathematical framework for radiotherapy treatment optimization with voxel-dependent optimization parameters. Methods: In the treatment plan optimization problem for radiotherapy, a clinically acceptable plan is usually generated by an optimization process with weighting factors or reference doses adjusted for a set of the objective functions associated to the organs. Recent discoveries indicate that adjusting parameters associated with each voxel may lead to better plan quality. However, it is still unclear regarding the mathematical reasons behind it. Furthermore, questions about the objective function selection and parameter adjustment to assure Pareto optimality as well as the relationship between the optimal solutions obtained from the organ-based and voxel-based models remain unanswered. To answer these questions, the authors establish in this work a new mathematical framework equipped with two theorems. Results: The new framework clarifies the different consequences of adjusting organ-dependent and voxel-dependent parameters for the treatment plan optimization of radiation therapy, as well as the impact of using different objective functions on plan qualities and Pareto surfaces. The main discoveries are threefold: (1) While in the organ-based model the selection of the objective function has an impact on the quality of the optimized plans, this is no longer an issue for the voxel-based model since the Pareto surface is independent of the objective function selection and the entire Pareto surface could be generated as long as the objective function satisfies certain mathematical conditions; (2) All Pareto solutions generated by the organ-based model with different objective functions are parts of a unique Pareto surface generated by the voxel-based model with any appropriate objective function; (3) A much larger Pareto surface is explored by adjusting voxel-dependent parameters than by adjusting organ-dependent parameters, possibly

Coordinated Pitch & Torque Control of Large-Scale Wind Turbine Based on Pareto Eciency Analysis

DEFF Research Database (Denmark)

Lin, Zhongwei; Chen, Zhenyu; Wu, Qiuwei

2018-01-01

For the existing pitch and torque control of the wind turbine generator system (WTGS), further development on coordinated control is necessary to improve effectiveness for practical applications. In this paper, the WTGS is modeled as a coupling combination of two subsystems: the generator torque...... control subsystem and blade pitch control subsystem. Then, the pole positions in each control subsystem are adjusted coordinately to evaluate the controller participation and used as the objective of optimization. A two-level parameters-controllers coordinated optimization scheme is proposed and applied...... to optimize the controller coordination based on the Pareto optimization theory. Three solutions are obtained through optimization, which includes the optimal torque solution, optimal power solution, and satisfactory solution. Detailed comparisons evaluate the performance of the three selected solutions...

Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

Science.gov (United States)

Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

2016-01-01

Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

A Pareto Algorithm for Efficient De Novo Design of Multi-functional Molecules.

Science.gov (United States)

Daeyaert, Frits; Deem, Micheal W

2017-01-01

We have introduced a Pareto sorting algorithm into Synopsis, a de novo design program that generates synthesizable molecules with desirable properties. We give a detailed description of the algorithm and illustrate its working in 2 different de novo design settings: the design of putative dual and selective FGFR and VEGFR inhibitors, and the successful design of organic structure determining agents (OSDAs) for the synthesis of zeolites. We show that the introduction of Pareto sorting not only enables the simultaneous optimization of multiple properties but also greatly improves the performance of the algorithm to generate molecules with hard-to-meet constraints. This in turn allows us to suggest approaches to address the problem of false positive hits in de novo structure based drug design by introducing structural and physicochemical constraints in the designed molecules, and by forcing essential interactions between these molecules and their target receptor. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

Science.gov (United States)

Yu, Xiang; Zhang, Xueqing

2017-01-01

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

Evaluation of plan quality assurance models for prostate cancer patients based on fully automatically generated Pareto-optimal treatment plans.

Science.gov (United States)

Wang, Yibing; Breedveld, Sebastiaan; Heijmen, Ben; Petit, Steven F

2016-06-07

IMRT planning with commercial Treatment Planning Systems (TPSs) is a trial-and-error process. Consequently, the quality of treatment plans may not be consistent among patients, planners and institutions. Recently, different plan quality assurance (QA) models have been proposed, that could flag and guide improvement of suboptimal treatment plans. However, the performance of these models was validated using plans that were created using the conventional trail-and-error treatment planning process. Consequently, it is challenging to assess and compare quantitatively the accuracy of different treatment planning QA models. Therefore, we created a golden standard dataset of consistently planned Pareto-optimal IMRT plans for 115 prostate patients. Next, the dataset was used to assess the performance of a treatment planning QA model that uses the overlap volume histogram (OVH). 115 prostate IMRT plans were fully automatically planned using our in-house developed TPS Erasmus-iCycle. An existing OVH model was trained on the plans of 58 of the patients. Next it was applied to predict DVHs of the rectum, bladder and anus of the remaining 57 patients. The predictions were compared with the achieved values of the golden standard plans for the rectum D mean, V 65, and V 75, and D mean of the anus and the bladder. For the rectum, the prediction errors (predicted-achieved) were only  -0.2  ±  0.9 Gy (mean  ±  1 SD) for D mean,-1.0  ±  1.6% for V 65, and  -0.4  ±  1.1% for V 75. For D mean of the anus and the bladder, the prediction error was 0.1  ±  1.6 Gy and 4.8  ±  4.1 Gy, respectively. Increasing the training cohort to 114 patients only led to minor improvements. A dataset of consistently planned Pareto-optimal prostate IMRT plans was generated. This dataset can be used to train new, and validate and compare existing treatment planning QA models, and has been made publicly available. The OVH model was highly accurate

Prediction and optimization of friction welding parameters for super duplex stainless steel (UNS S32760) joints

International Nuclear Information System (INIS)

Udayakumar, T.; Raja, K.; Afsal Husain, T.M.; Sathiya, P.

2014-01-01

Highlights: • Corrosion resistance and impact strength – predicted by response surface methodology. • Burn off length has highest significance on corrosion resistance. • Friction force is a strong determinant in changing impact strength. • Pareto front points generated by genetic algorithm aid to fix input control variable. • Pareto front will be a trade-off between corrosion resistance and impact strength. - Abstract: Friction welding finds widespread industrial use as a mass production process for joining materials. Friction welding process allows welding of several materials that are extremely difficult to fusion weld. Friction welding process parameters play a significant role in making good quality joints. To produce a good quality joint it is important to set up proper welding process parameters. This can be done by employing optimization techniques. This paper presents a multi objective optimization method for optimizing the process parameters during friction welding process. The proposed method combines the response surface methodology (RSM) with an intelligent optimization algorithm, i.e. genetic algorithm (GA). Corrosion resistance and impact strength of friction welded super duplex stainless steel (SDSS) (UNS S32760) joints were investigated considering three process parameters: friction force (F), upset force (U) and burn off length (B). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. Burn off length has high significance on corrosion current followed by upset force and friction force. In the case of impact strength, friction force has high significance followed by upset force and burn off length. Multi objective optimization for maximizing the impact strength and minimizing the corrosion current (maximizing corrosion resistance) was carried out using GA with the RSM model. The optimization procedure resulted in

Multi-objective Optimization Strategies Using Adjoint Method and Game Theory in Aerodynamics

Science.gov (United States)

Tang, Zhili

2006-08-01

There are currently three different game strategies originated in economics: (1) Cooperative games (Pareto front), (2) Competitive games (Nash game) and (3) Hierarchical games (Stackelberg game). Each game achieves different equilibria with different performance, and their players play different roles in the games. Here, we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multi-criteria aerodynamic optimization problems. The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments. We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method. The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front. Non-dominated Pareto front solutions are obtained, however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

Multi-objective optimization strategies using adjoint method and game theory in aerodynamics

Institute of Scientific and Technical Information of China (English)

Zhili Tang

2006-01-01

There are currently three different game strategies originated in economics:(1) Cooperative games (Pareto front),(2)Competitive games (Nash game) and (3)Hierarchical games (Stackelberg game).Each game achieves different equilibria with different performance,and their players play different roles in the games.Here,we introduced game concept into aerodynamic design, and combined it with adjoint method to solve multicriteria aerodynamic optimization problems.The performance distinction of the equilibria of these three game strategies was investigated by numerical experiments.We computed Pareto front, Nash and Stackelberg equilibria of the same optimization problem with two conflicting and hierarchical targets under different parameterizations by using the deterministic optimization method.The numerical results show clearly that all the equilibria solutions are inferior to the Pareto front.Non-dominated Pareto front solutions are obtained,however the CPU cost to capture a set of solutions makes the Pareto front an expensive tool to the designer.

Analysis of a Pareto Mixture Distribution for Maritime Surveillance Radar

Directory of Open Access Journals (Sweden)

Graham V. Weinberg

2012-01-01

Full Text Available The Pareto distribution has been shown to be an excellent model for X-band high-resolution maritime surveillance radar clutter returns. Given the success of mixture distributions in radar, it is thus of interest to consider the effect of Pareto mixture models. This paper introduces a formulation of a Pareto intensity mixture distribution and investigates coherent multilook radar detector performance using this new clutter model. Clutter parameter estimates are derived from data sets produced by the Defence Science and Technology Organisation's Ingara maritime surveillance radar.

Multi-objective optimal strategy for generating and bidding in the power market

International Nuclear Information System (INIS)

Peng Chunhua; Sun Huijuan; Guo Jianfeng; Liu Gang

2012-01-01

Highlights: ► A new benefit/risk/emission comprehensive generation optimization model is established. ► A hybrid multi-objective differential evolution optimization algorithm is designed. ► Fuzzy set theory and entropy weighting method are employed to extract the general best solution. ► The proposed approach of generating and bidding is efficient for maximizing profit and minimizing both risk and emissions. - Abstract: Based on the coordinated interaction between units output and electricity market prices, the benefit/risk/emission comprehensive generation optimization model with objectives of maximal profit and minimal bidding risk and emissions is established. A hybrid multi-objective differential evolution optimization algorithm, which successfully integrates Pareto non-dominated sorting with differential evolution algorithm and improves individual crowding distance mechanism and mutation strategy to avoid premature and unevenly search, is designed to achieve Pareto optimal set of this model. Moreover, fuzzy set theory and entropy weighting method are employed to extract one of the Pareto optimal solutions as the general best solution. Several optimization runs have been carried out on different cases of generation bidding and scheduling. The results confirm the potential and effectiveness of the proposed approach in solving the multi-objective optimization problem of generation bidding and scheduling. In addition, the comparison with the classical optimization algorithms demonstrates the superiorities of the proposed algorithm such as integrality of Pareto front, well-distributed Pareto-optimal solutions, high search speed.

The exponential age distribution and the Pareto firm size distribution

OpenAIRE

Coad, Alex

2008-01-01

Recent work drawing on data for large and small firms has shown a Pareto distribution of firm size. We mix a Gibrat-type growth process among incumbents with an exponential distribution of firm’s age, to obtain the empirical Pareto distribution.

Tsallis-Pareto like distributions in hadron-hadron collisions

International Nuclear Information System (INIS)

Barnafoeldi, G G; Uermoessy, K; Biro, T S

2011-01-01

Non-extensive thermodynamics is a novel approach in high energy physics. In high-energy heavy-ion, and especially in proton-proton collisions we are far from a canonical thermal state, described by the Boltzmann-Gibbs statistic. In these reactions low and intermediate transverse momentum spectra are extremely well reproduced by the Tsallis-Pareto distribution, but the physical origin of Tsallis parameters is still an unsettled question. Here, we analyze whether Tsallis-Pareto energy distribution do overlap with hadron spectra at high-pT. We fitted data, measured in proton-proton (proton-antiproton) collisions in wide center of mass energy range from 200 GeV RHIC up to 7 TeV LHC energies. Furthermore, our test is extended to an investigation of a possible √s-dependence of the power in the Tsallis-Pareto distribution, motivated by QCD evolution equations. We found that Tsallis-Pareto distributions fit well high-pT data, in the wide center of mass energy range. Deviance from the fits appears at p T > 20-30 GeV/c, especially on CDF data. Introducing a pT-scaling ansatz, the fits at low and intermediate transverse momenta still remain good, and the deviations tend to disappear at the highest-pT data.

Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective

Directory of Open Access Journals (Sweden)

Yoichi Hayashi

2016-01-01

Full Text Available Historically, the assessment of credit risk has proved to be both highly important and extremely difficult. Currently, financial institutions rely on the use of computer-generated credit scores for risk assessment. However, automated risk evaluations are currently imperfect, and the loss of vast amounts of capital could be prevented by improving the performance of computerized credit assessments. A number of approaches have been developed for the computation of credit scores over the last several decades, but these methods have been considered too complex without good interpretability and have therefore not been widely adopted. Therefore, in this study, we provide the first comprehensive comparison of results regarding the assessment of credit risk obtained using 10 runs of 10-fold cross validation of the Re-RX algorithm family, including the Re-RX algorithm, the Re-RX algorithm with both discrete and continuous attributes (Continuous Re-RX, the Re-RX algorithm with J48graft, the Re-RX algorithm with a trained neural network (Sampling Re-RX, NeuroLinear, NeuroLinear+GRG, and three unique rule extraction techniques involving support vector machines and Minerva from four real-life, two-class mixed credit-risk datasets. We also discuss the roles of various newly-extended types of the Re-RX algorithm and high performance classifiers from a Pareto optimal perspective. Our findings suggest that Continuous Re-RX, Re-RX with J48graft, and Sampling Re-RX comprise a powerful management tool that allows the creation of advanced, accurate, concise and interpretable decision support systems for credit risk evaluation. In addition, from a Pareto optimal perspective, the Re-RX algorithm family has superior features in relation to the comprehensibility of extracted rules and the potential for credit scoring with Big Data.

Pareto Efficient Solutions of Attack-Defence Trees

DEFF Research Database (Denmark)

Aslanyan, Zaruhi; Nielson, Flemming

2015-01-01

Attack-defence trees are a promising approach for representing threat scenarios and possible countermeasures in a concise and intuitive manner. An attack-defence tree describes the interaction between an attacker and a defender, and is evaluated by assigning parameters to the nodes, such as proba......Attack-defence trees are a promising approach for representing threat scenarios and possible countermeasures in a concise and intuitive manner. An attack-defence tree describes the interaction between an attacker and a defender, and is evaluated by assigning parameters to the nodes......, such as probability or cost of attacks and defences. In case of multiple parameters most analytical methods optimise one parameter at a time, e.g., minimise cost or maximise probability of an attack. Such methods may lead to sub-optimal solutions when optimising conflicting parameters, e.g., minimising cost while...... maximising probability. In order to tackle this challenge, we devise automated techniques that optimise all parameters at once. Moreover, in the case of conflicting parameters our techniques compute the set of all optimal solutions, defined in terms of Pareto efficiency. The developments are carried out...

An Investigation of the Pareto Distribution as a Model for High Grazing Angle Clutter

Science.gov (United States)

2011-03-01

radar detection schemes under controlled conditions. Complicated clutter models result in mathematical difficulties in the determination of optimal and...a population [7]. It has been used in the modelling of actuarial data; an example is in excess of loss quotations in insurance [8]. Its usefulness as...UNCLASSIFIED modified Bessel functions, making it difficult to employ in radar detection schemes. The Pareto Distribution is amenable to mathematical

Pareto vs Simmel: residui ed emozioni

Directory of Open Access Journals (Sweden)

Silvia Fornari

2017-08-01

Full Text Available A cento anni dalla pubblicazione del Trattato di sociologia generale (Pareto 1988 siamo a mantenere vivo ed attuale lo studio paretiano con una rilettura contemporanea del suo pensiero. Ricordato per la grande versatilità intellettuale dagli economisti, rimane lo scienziato rigoroso ed analitico i cui contributi sono ancora discussi a livello internazionale. Noi ne analizzeremo gli aspetti che l’hanno portato ad avvicinarsi all’approccio sociologico, con l’introduzione della nota distinzione dell’azione sociale: logica e non-logica. Una dicotomia utilizzata per dare conto dei cambiamenti sociali riguardanti le modalità d’azione degli uomini e delle donne. Com’è noto le azioni logiche sono quelle che riguardano comportamenti mossi da logicità e raziocinio, in cui vi è una diretta relazione causa-effetto, azioni oggetto di studio degli economisti, e di cui non si occupano i sociologi. Le azioni non-logiche riguardano tutte le tipologie di agire umano che rientrano nel novero delle scienze sociali, e che rappresentano la parte più ampia dell’agire sociale. Sono le azioni guidate dai sentimenti, dall’emotività, dalla superstizione, ecc., illustrate da Pareto nel Trattato di sociologia generale e in saggi successivi, dove riprende anche il concetto di eterogenesi dei fini, formulato per la prima volta da Giambattista Vico. Concetto secondo il quale la storia umana, pur conservando in potenza la realizzazione di certi fini, non è lineare e lungo il suo percorso evolutivo può accadere che l’uomo nel tentativo di raggiungere una finalità arrivi a conclusioni opposte. Pareto collega la definizione del filosofo napoletano alle tipologie di azione sociale e alla loro distinzione (logiche, non-logiche. L’eterogenesi dei fini per Pareto è dunque l’esito di un particolare tipo di azione non-logica dell’essere umano e della collettività.

Pareto Improving Price Regulation when the Asset Market is Incomplete

NARCIS (Netherlands)

Herings, P.J.J.; Polemarchakis, H.M.

1999-01-01

When the asset market is incomplete, competitive equilibria are constrained suboptimal, which provides a scope for pareto improving interventions. Price regulation can be such a pareto improving policy, even when the welfare effects of rationing are taken into account. An appealing aspect of price

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

Science.gov (United States)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Multiobjective Optimization of a Counterrotating Type Pump-Turbine Unit Operated at Turbine Mode

Directory of Open Access Journals (Sweden)

Jin-Hyuk Kim

2014-05-01

Full Text Available A multiobjective optimization for improving the turbine output and efficiency of a counterrotating type pump-turbine unit operated at turbine mode was carried out in this work. The blade geometry of both the runners was optimized using a hybrid multiobjective evolutionary algorithm coupled with a surrogate model. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model were discretized by finite volume approximations and solved on hexahedral grids to analyze the flow in the pump-turbine unit. As major hydrodynamic performance parameters, the turbine output and efficiency were selected as objective functions with two design variables related to the hub profiles of both the runner blades. These objectives were numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. Response surface approximation models for the objectives were constructed based on the objective function values at the design points. A fast nondominated sorting genetic algorithm for the local search coupled with the response surface approximation models was applied to determine the global Pareto-optimal solutions. The trade-off between the two objectives was determined and described with respect to the Pareto-optimal solutions. The results of this work showed that the turbine outputs and efficiencies of optimized pump-turbine units were simultaneously improved in comparison to the reference unit.

On the size distribution of cities: an economic interpretation of the Pareto coefficient.

Science.gov (United States)

Suh, S H

1987-01-01

"Both the hierarchy and the stochastic models of size distribution of cities are analyzed in order to explain the Pareto coefficient by economic variables. In hierarchy models, it is found that the rate of variation in the productivity of cities and that in the probability of emergence of cities can explain the Pareto coefficient. In stochastic models, the productivity of cities is found to explain the Pareto coefficient. New city-size distribution functions, in which the Pareto coefficient is decomposed by economic variables, are estimated." excerpt

The Reduction of Modal Sensor Channels through a Pareto Chart Methodology

Directory of Open Access Journals (Sweden)

Kaci J. Lemler

2015-01-01

Full Text Available Presented herein is a new experimental sensor placement procedure developed to assist in placing sensors in key locations in an efficient method to reduce the number of channels for a full modal analysis. It is a fast, noncontact method that uses a laser vibrometer to gather a candidate set of sensor locations. These locations are then evaluated using a Pareto chart to obtain a reduced set of sensor locations that still captures the motion of the structure. The Pareto chart is employed to identify the points on a structure that have the largest reaction to an input excitation and thus reduce the number of channels while capturing the most significant data. This method enhances the correct and efficient placement of sensors which is crucial in modal testing. Previously this required the development and/or use of a complicated model or set of equations. This new technique is applied in a case study on a small unmanned aerial system. The test procedure is presented and the results are discussed.

Optimizing the dynamic response of the H.B. Robinson nuclear plant using multiobjective particle swarm optimization

International Nuclear Information System (INIS)

Elsays, Mostafa A.; Naguib Aly, M.; Badawi, Alya A.

2009-01-01

In this paper, the Particle Swarm Optimization (PSO) algorithm is modified to deal with Multiobjective Optimization Problems (MOPs). A mathematical model for predicting the dynamic response of the H. B. Robinson nuclear power plant, which represents an Initial Value Problem (IVP) of Ordinary Differential Equations (ODEs), is solved using Runge-Kutta formula. The resulted data values are represented as a system of nonlinear algebraic equations by interpolation schemes for data fitting. This system of fitted nonlinear algebraic equations represents a nonlinear multiobjective optimization problem. A Multiobjective Particle Swarm Optimizer (MOPSO) which is based on the Pareto optimality concept is developed and applied to maximize the above mentioned problem. Results show that MOPSO efficiently cope with the problem and finds multiple Pareto optimal solutions. (orig.)

Flow area optimization in point to area or area to point flows

International Nuclear Information System (INIS)

Ghodoossi, Lotfollah; Egrican, Niluefer

2003-01-01

This paper deals with the constructal theory of generation of shape and structure in flow systems connecting one point to a finite size area. The flow direction may be either from the point to the area or the area to the point. The formulation of the problem remains the same if the flow direction is reversed. Two models are used in optimization of the point to area or area to point flow problem: cost minimization and revenue maximization. The cost minimization model enables one to predict the shape of the optimized flow areas, but the geometric sizes of the flow areas are not predictable. That is, as an example, if the area of flow is a rectangle with a fixed area size, optimization of the point to area or area to point flow problem by using the cost minimization model will only predict the height/length ratio of the rectangle not the height and length itself. By using the revenue maximization model in optimization of the flow problems, all optimized geometric aspects of the interested flow areas will be derived as well. The aim of this paper is to optimize the point to area or area to point flow problems in various elemental flow area shapes and various structures of the flow system (various combinations of elemental flow areas) by using the revenue maximization model. The elemental flow area shapes used in this paper are either rectangular or triangular. The forms of the flow area structure, made up of an assembly of optimized elemental flow areas to obtain bigger flow areas, are rectangle-in-rectangle, rectangle-in-triangle, triangle-in-triangle and triangle-in-rectangle. The global maximum revenue, revenue collected per unit flow area and the shape and sizes of each flow area structure have been derived in optimized conditions. The results for each flow area structure have been compared with the results of the other structures to determine the structure that provides better performance. The conclusion is that the rectangle-in-triangle flow area structure

Application of Pareto optimization method for ontology matching in nuclear reactor domain

International Nuclear Information System (INIS)

Meenachi, N. Madurai; Baba, M. Sai

2017-01-01

This article describes the need for ontology matching and describes the methods to achieve the same. Efforts are put in the implementation of the semantic web based knowledge management system for nuclear domain which necessitated use of the methods for development of ontology matching. In order to exchange information in a distributed environment, ontology mapping has been used. The constraints in matching the ontology are also discussed. Pareto based ontology matching algorithm is used to find the similarity between two ontologies in the nuclear reactor domain. Algorithms like Jaro Winkler distance, Needleman Wunsch algorithm, Bigram, Kull Back and Cosine divergence are employed to demonstrate ontology matching. A case study was carried out to analysis the ontology matching in diversity in the nuclear reactor domain and same was illustrated.

Application of Pareto optimization method for ontology matching in nuclear reactor domain

Energy Technology Data Exchange (ETDEWEB)

Meenachi, N. Madurai [Indira Gandhi Centre for Atomic Research, HBNI, Tamil Nadu (India). Planning and Human Resource Management Div.; Baba, M. Sai [Indira Gandhi Centre for Atomic Research, HBNI, Tamil Nadu (India). Resources Management Group

2017-12-15

This article describes the need for ontology matching and describes the methods to achieve the same. Efforts are put in the implementation of the semantic web based knowledge management system for nuclear domain which necessitated use of the methods for development of ontology matching. In order to exchange information in a distributed environment, ontology mapping has been used. The constraints in matching the ontology are also discussed. Pareto based ontology matching algorithm is used to find the similarity between two ontologies in the nuclear reactor domain. Algorithms like Jaro Winkler distance, Needleman Wunsch algorithm, Bigram, Kull Back and Cosine divergence are employed to demonstrate ontology matching. A case study was carried out to analysis the ontology matching in diversity in the nuclear reactor domain and same was illustrated.

Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis

KAUST Repository

Chikalov, Igor

2017-10-19

This paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms.

Practical solutions for multi-objective optimization: An application to system reliability design problems

International Nuclear Information System (INIS)

Taboada, Heidi A.; Baheranwala, Fatema; Coit, David W.; Wattanapongsakorn, Naruemon

2007-01-01

For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set. Unfortunately, these sets are often large and can become difficult to comprehend and consider. Two methods are presented as practical approaches to reduce the size of the Pareto optimal set for multiple-objective system reliability design problems. The first method is a pseudo-ranking scheme that helps the decision maker select solutions that reflect his/her objective function priorities. In the second approach, we used data mining clustering techniques to group the data by using the k-means algorithm to find clusters of similar solutions. This provides the decision maker with just k general solutions to choose from. With this second method, from the clustered Pareto optimal set, we attempted to find solutions which are likely to be more relevant to the decision maker. These are solutions where a small improvement in one objective would lead to a large deterioration in at least one other objective. To demonstrate how these methods work, the well-known redundancy allocation problem was solved as a multiple objective problem by using the NSGA genetic algorithm to initially find the Pareto optimal solutions, and then, the two proposed methods are applied to prune the Pareto set

Designing Pareto-superior demand-response rate options

International Nuclear Information System (INIS)

Horowitz, I.; Woo, C.K.

2006-01-01

We explore three voluntary service options-real-time pricing, time-of-use pricing, and curtailable/interruptible service-that a local distribution company might offer its customers in order to encourage them to alter their electricity usage in response to changes in the electricity-spot-market price. These options are simple and practical, and make minimal information demands. We show that each of the options is Pareto-superior ex ante, in that it benefits both the participants and the company offering it, while not affecting the non-participants. The options are shown to be Pareto-superior ex post as well, except under certain exceptional circumstances. (author)

Pareto-depth for multiple-query image retrieval.

Science.gov (United States)

Hsiao, Ko-Jen; Calder, Jeff; Hero, Alfred O

2015-02-01

Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper, we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a novel multiple-query information retrieval algorithm that combines the Pareto front method with efficient manifold ranking. We show that our proposed algorithm outperforms state of the art multiple-query retrieval algorithms on real-world image databases. We attribute this performance improvement to concavity properties of the Pareto fronts, and prove a theoretical result that characterizes the asymptotic concavity of the fronts.

Rayleigh Pareto Distribution

Directory of Open Access Journals (Sweden)

Kareema ‎ Abed Al-Kadim

2017-12-01

Full Text Available In this paper Rayleigh Pareto distribution have  introduced denote by( R_PD. We stated some  useful functions. Therefor  we  give some of its properties like the entropy function, mean, mode, median , variance , the r-th moment about the mean, the rth moment about the origin, reliability, hazard functions, coefficients of variation, of sekeness and of kurtosis. Finally, we estimate the parameters  so the aim of this search  is to introduce a new distribution

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

International Nuclear Information System (INIS)

Zhou, Z; Folkert, M; Wang, J

2016-01-01

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Energy Technology Data Exchange (ETDEWEB)

Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

A Pareto upper tail for capital income distribution

Science.gov (United States)

Oancea, Bogdan; Pirjol, Dan; Andrei, Tudorel

2018-02-01

We present a study of the capital income distribution and of its contribution to the total income (capital income share) using individual tax income data in Romania, for 2013 and 2014. Using a parametric representation we show that the capital income is Pareto distributed in the upper tail, with a Pareto coefficient α ∼ 1 . 44 which is much smaller than the corresponding coefficient for wage- and non-wage-income (excluding capital income), of α ∼ 2 . 53. Including the capital income contribution has the effect of increasing the overall inequality measures.

Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modelling heteroscedastic residual errors

Science.gov (United States)

David, McInerney; Mark, Thyer; Dmitri, Kavetski; George, Kuczera

2017-04-01

This study provides guidance to hydrological researchers which enables them to provide probabilistic predictions of daily streamflow with the best reliability and precision for different catchment types (e.g. high/low degree of ephemerality). Reliable and precise probabilistic prediction of daily catchment-scale streamflow requires statistical characterization of residual errors of hydrological models. It is commonly known that hydrological model residual errors are heteroscedastic, i.e. there is a pattern of larger errors in higher streamflow predictions. Although multiple approaches exist for representing this heteroscedasticity, few studies have undertaken a comprehensive evaluation and comparison of these approaches. This study fills this research gap by evaluating 8 common residual error schemes, including standard and weighted least squares, the Box-Cox transformation (with fixed and calibrated power parameter, lambda) and the log-sinh transformation. Case studies include 17 perennial and 6 ephemeral catchments in Australia and USA, and two lumped hydrological models. We find the choice of heteroscedastic error modelling approach significantly impacts on predictive performance, though no single scheme simultaneously optimizes all performance metrics. The set of Pareto optimal schemes, reflecting performance trade-offs, comprises Box-Cox schemes with lambda of 0.2 and 0.5, and the log scheme (lambda=0, perennial catchments only). These schemes significantly outperform even the average-performing remaining schemes (e.g., across ephemeral catchments, median precision tightens from 105% to 40% of observed streamflow, and median biases decrease from 25% to 4%). Theoretical interpretations of empirical results highlight the importance of capturing the skew/kurtosis of raw residuals and reproducing zero flows. Recommendations for researchers and practitioners seeking robust residual error schemes for practical work are provided.

Vilfredo Pareto. L'economista alla luce delle lettere a Maffeo Pantaleoni. (Vilfredo Pareto. The economist in the light of his letters to Maffeo Pantaleoni

Directory of Open Access Journals (Sweden)

E. SCHNEIDER

2014-07-01

Full Text Available The article is part of a special issue on occasion of the publication of the entire scientific correspondence of Vilfredo Pareto with Maffeo Pantaleoni. The author reconstructs the beginning of their correspondence, the debate in pure mathematical economics and draws main conclusions on the different views of Pareto with respect to Marshal, Edgeworth and Fisher.JEL: B16, B31, C02, C60

Pareto Distribution of Firm Size and Knowledge Spillover Process as a Network

OpenAIRE

Tomohiko Konno

2013-01-01

The firm size distribution is considered as Pareto distribution. In the present paper, we show that the Pareto distribution of firm size results from the spillover network model which was introduced in Konno (2010).

Optimized positioning of autonomous surgical lamps

Science.gov (United States)

Teuber, Jörn; Weller, Rene; Kikinis, Ron; Oldhafer, Karl-Jürgen; Lipp, Michael J.; Zachmann, Gabriel

2017-03-01

We consider the problem of finding automatically optimal positions of surgical lamps throughout the whole surgical procedure, where we assume that future lamps could be robotized. We propose a two-tiered optimization technique for the real-time autonomous positioning of those robotized surgical lamps. Typically, finding optimal positions for surgical lamps is a multi-dimensional problem with several, in part conflicting, objectives, such as optimal lighting conditions at every point in time while minimizing the movement of the lamps in order to avoid distractions of the surgeon. Consequently, we use multi-objective optimization (MOO) to find optimal positions in real-time during the entire surgery. Due to the conflicting objectives, there is usually not a single optimal solution for such kinds of problems, but a set of solutions that realizes a Pareto-front. When our algorithm selects a solution from this set it additionally has to consider the individual preferences of the surgeon. This is a highly non-trivial task because the relationship between the solution and the parameters is not obvious. We have developed a novel meta-optimization that considers exactly this challenge. It delivers an easy to understand set of presets for the parameters and allows a balance between the lamp movement and lamp obstruction. This metaoptimization can be pre-computed for different kinds of operations and it then used by our online optimization for the selection of the appropriate Pareto solution. Both optimization approaches use data obtained by a depth camera that captures the surgical site but also the environment around the operating table. We have evaluated our algorithms with data recorded during a real open abdominal surgery. It is available for use for scientific purposes. The results show that our meta-optimization produces viable parameter sets for different parts of an intervention even when trained on a small portion of it.

Pareto design of state feedback tracking control of a biped robot via multiobjective PSO in comparison with sigma method and genetic algorithms: modified NSGAII and MATLAB's toolbox.

Science.gov (United States)

Mahmoodabadi, M J; Taherkhorsandi, M; Bagheri, A

2014-01-01

An optimal robust state feedback tracking controller is introduced to control a biped robot. In the literature, the parameters of the controller are usually determined by a tedious trial and error process. To eliminate this process and design the parameters of the proposed controller, the multiobjective evolutionary algorithms, that is, the proposed method, modified NSGAII, Sigma method, and MATLAB's Toolbox MOGA, are employed in this study. Among the used evolutionary optimization algorithms to design the controller for biped robots, the proposed method operates better in the aspect of designing the controller since it provides ample opportunities for designers to choose the most appropriate point based upon the design criteria. Three points are chosen from the nondominated solutions of the obtained Pareto front based on two conflicting objective functions, that is, the normalized summation of angle errors and normalized summation of control effort. Obtained results elucidate the efficiency of the proposed controller in order to control a biped robot.

Multicriteria Optimization in Intensity-Modulated Radiation Therapy Treatment Planning for Locally Advanced Cancer of the Pancreatic Head

International Nuclear Information System (INIS)

Hong, Theodore S.; Craft, David L.; Carlsson, Fredrik; Bortfeld, Thomas R.

2008-01-01

Purpose: Intensity-modulated radiation therapy (IMRT) affords the potential to decrease radiation therapy-associated toxicity by creating highly conformal dose distributions. However, the inverse planning process can create a suboptimal plan despite meeting all constraints. Multicriteria optimization (MCO) may reduce the time-consuming iteration loop necessary to develop a satisfactory plan while providing information regarding trade-offs between different treatment planning goals. In this exploratory study, we examine the feasibility and utility of MCO in physician plan selection in patients with locally advanced pancreatic cancer (LAPC). Methods and Materials: The first 10 consecutive patients with LAPC treated with IMRT were evaluated. A database of plans (Pareto surface) was created that met the inverse planning goals. The physician then navigated to an 'optimal' plan from the point on the Pareto surface at which kidney dose was minimized. Results: Pareto surfaces were created for all 10 patients. A physician was able to select a plan from the Pareto surface within 10 minutes for all cases. Compared with the original (treated) IMRT plans, the plan selected from the Pareto surface had a lower stomach mean dose in 9 of 10 patients, although often at the expense of higher kidney dose than with the treated plan. Conclusion: The MCO is feasible in patients with LAPC and allows the physician to choose a satisfactory plan quickly. Generally, when given the opportunity, the physician will choose a plan with a lower stomach dose. The MCO enables a physician to provide greater active clinical input into the IMRT planning process

Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method

International Nuclear Information System (INIS)

Azizipanah-Abarghooee, Rasoul; Niknam, Taher; Roosta, Alireza; Malekpour, Ahmad Reza; Zare, Mohsen

2012-01-01

In this paper, wind power generators are being incorporated in the multiobjective economic emission dispatch problem which minimizes wind-thermal electrical energy cost and emissions produced by fossil-fueled power plants, simultaneously. Large integration of wind energy sources necessitates an efficient model to cope with uncertainty arising from random wind variation. Hence, a multiobjective stochastic search algorithm based on 2m point estimated method is implemented to analyze the probabilistic wind-thermal economic emission dispatch problem considering both overestimation and underestimation of available wind power. 2m point estimated method handles the system uncertainties and renders the probability density function of desired variables efficiently. Moreover, a new population-based optimization algorithm called modified teaching-learning algorithm is proposed to determine the set of non-dominated optimal solutions. During the simulation, the set of non-dominated solutions are kept in an external memory (repository). Also, a fuzzy-based clustering technique is implemented to control the size of the repository. In order to select the best compromise solution from the repository, a niching mechanism is utilized such that the population will move toward a smaller search space in the Pareto-optimal front. In order to show the efficiency and feasibility of the proposed framework, three different test systems are represented as case studies. -- Highlights: ► WPGs are being incorporated in the multiobjective economic emission dispatch problem. ► 2m PEM handles the system uncertainties. ► A MTLBO is proposed to determine the set of non-dominated (Pareto) optimal solutions. ► A fuzzy-based clustering technique is implemented to control the size of the repository.

Using Coevolution Genetic Algorithm with Pareto Principles to Solve Project Scheduling Problem under Duration and Cost Constraints

Directory of Open Access Journals (Sweden)

Alexandr Victorovich Budylskiy

2014-06-01

Full Text Available This article considers the multicriteria optimization approach using the modified genetic algorithm to solve the project-scheduling problem under duration and cost constraints. The work contains the list of choices for solving this problem. The multicriteria optimization approach is justified here. The study describes the Pareto principles, which are used in the modified genetic algorithm. We identify the mathematical model of the project-scheduling problem. We introduced the modified genetic algorithm, the ranking strategies, the elitism approaches. The article includes the example.

A buffer material optimal design in the radioactive wastes geological disposal using the satisficing trade-off method and the self-organizing map

International Nuclear Information System (INIS)

Okamoto, Takashi; Hanaoka, Yuya; Aiyoshi, Eitaro; Kobayashi, Yoko

2012-01-01

In this paper, we consider a multi-objective optimization method in order to obtain a preferred solution for the buffer material optimal design problem in the high-level radioactive wastes geological disposal. The buffer material optimal design problem is formulated as a constrained multi-objective optimization problem. Its Pareto optimal solutions are distributed evenly on whole bounds of the feasible region. Hence, we develop a search method to find a preferred solution easily for a decision maker from the Pareto optimal solutions which are distributed evenly and vastly. In the preferred solution search method, the visualization technique of a Pareto optimal solution set using the self-organizing map is introduced into the satisficing trade-off method which is the interactive method to obtain a Pareto optimal solution that satisfies a decision maker. We confirm the effectiveness of the preferred solution search method in the buffer material optimal design problem. (author)

Prediction in Partial Duration Series With Generalized Pareto-Distributed Exceedances

DEFF Research Database (Denmark)

Rosbjerg, Dan; Madsen, Henrik; Rasmussen, Peter Funder

1992-01-01

As a generalization of the common assumption of exponential distribution of the exceedances in Partial duration series the generalized Pareto distribution has been adopted. Estimators for the parameters are presented using estimation by both method of moments and probability-weighted moments......-weighted moments. Maintaining the generalized Pareto distribution as the parent exceedance distribution the T-year event is estimated assuming the exceedances to be exponentially distributed. For moderately long-tailed exceedance distributions and small to moderate sample sizes it is found, by comparing mean...... square errors of the T-year event estimators, that the exponential distribution is preferable to the correct generalized Pareto distribution despite the introduced model error and despite a possible rejection of the exponential hypothesis by a test of significance. For moderately short-tailed exceedance...

Strong Convergence Bound of the Pareto Index Estimator under Right Censoring

Directory of Open Access Journals (Sweden)

Peng Zuoxiang

2010-01-01

Full Text Available Let be a sequence of positive independent and identically distributed random variables with common Pareto-type distribution function as , where represents a slowly varying function at infinity. In this note we study the strong convergence bound of a kind of right censored Pareto index estimator under second-order regularly varying conditions.

Multiclass gene selection using Pareto-fronts.

Science.gov (United States)

Rajapakse, Jagath C; Mundra, Piyushkumar A

2013-01-01

Filter methods are often used for selection of genes in multiclass sample classification by using microarray data. Such techniques usually tend to bias toward a few classes that are easily distinguishable from other classes due to imbalances of strong features and sample sizes of different classes. It could therefore lead to selection of redundant genes while missing the relevant genes, leading to poor classification of tissue samples. In this manuscript, we propose to decompose multiclass ranking statistics into class-specific statistics and then use Pareto-front analysis for selection of genes. This alleviates the bias induced by class intrinsic characteristics of dominating classes. The use of Pareto-front analysis is demonstrated on two filter criteria commonly used for gene selection: F-score and KW-score. A significant improvement in classification performance and reduction in redundancy among top-ranked genes were achieved in experiments with both synthetic and real-benchmark data sets.

The Burr X Pareto Distribution: Properties, Applications and VaR Estimation

Directory of Open Access Journals (Sweden)

Mustafa Ç. Korkmaz

2017-12-01

Full Text Available In this paper, a new three-parameter Pareto distribution is introduced and studied. We discuss various mathematical and statistical properties of the new model. Some estimation methods of the model parameters are performed. Moreover, the peaks-over-threshold method is used to estimate Value-at-Risk (VaR by means of the proposed distribution. We compare the distribution with a few other models to show its versatility in modelling data with heavy tails. VaR estimation with the Burr X Pareto distribution is presented using time series data, and the new model could be considered as an alternative VaR model against the generalized Pareto model for financial institutions.

Pareto-Zipf law in growing systems with multiplicative interactions

Science.gov (United States)

Ohtsuki, Toshiya; Tanimoto, Satoshi; Sekiyama, Makoto; Fujihara, Akihiro; Yamamoto, Hiroshi

2018-06-01

Numerical simulations of multiplicatively interacting stochastic processes with weighted selections were conducted. A feedback mechanism to control the weight w of selections was proposed. It becomes evident that when w is moderately controlled around 0, such systems spontaneously exhibit the Pareto-Zipf distribution. The simulation results are universal in the sense that microscopic details, such as parameter values and the type of control and weight, are irrelevant. The central ingredient of the Pareto-Zipf law is argued to be the mild control of interactions.

A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

OpenAIRE

Filatovas, Ernestas; Podkopaev, Dmitry; Kurasova, Olga

2015-01-01

Interactive methods of multiobjective optimization repetitively derive Pareto optimal solutions based on decision maker’s preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the Pareto optimal set and learning about the op...

A Note on Parameter Estimation in the Composite Weibull–Pareto Distribution

Directory of Open Access Journals (Sweden)

Enrique Calderín-Ojeda

2018-02-01

Full Text Available Composite models have received much attention in the recent actuarial literature to describe heavy-tailed insurance loss data. One of the models that presents a good performance to describe this kind of data is the composite Weibull–Pareto (CWL distribution. On this note, this distribution is revisited to carry out estimation of parameters via mle and mle2 optimization functions in R. The results are compared with those obtained in a previous paper by using the nlm function, in terms of analytical and graphical methods of model selection. In addition, the consistency of the parameter estimation is examined via a simulation study.

Hybridization of Sensing Methods of the Search Domain and Adaptive Weighted Sum in the Pareto Approximation Problem

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2015-01-01

Full Text Available We consider the relatively new and rapidly developing class of methods to solve a problem of multi-objective optimization, based on the preliminary built finite-dimensional approximation of the set, and thereby, the Pareto front of this problem as well. The work investigates the efficiency of several modifications of the method of adaptive weighted sum (AWS. This method proposed in the paper of Ryu and Kim Van (JH. Ryu, S. Kim, H. Wan is intended to build Pareto approximation of the multi-objective optimization problem.The AWS method uses quadratic approximation of the objective functions in the current sub-domain of the search space (the area of trust based on the gradient and Hessian matrix of the objective functions. To build the (quadratic meta objective functions this work uses methods of the experimental design theory, which involves calculating the values of these functions in the grid nodes covering the area of trust (a sensing method of the search domain. There are two groups of the sensing methods under consideration: hypercube- and hyper-sphere-based methods. For each of these groups, a number of test multi-objective optimization tasks has been used to study the efficiency of the following grids: "Latin Hypercube"; grid, which is uniformly random for each measurement; grid, based on the LP  sequences.

A hybrid pareto mixture for conditional asymmetric fat-tailed distributions.

Science.gov (United States)

Carreau, Julie; Bengio, Yoshua

2009-07-01

In many cases, we observe some variables X that contain predictive information over a scalar variable of interest Y , with (X,Y) pairs observed in a training set. We can take advantage of this information to estimate the conditional density p(Y|X = x). In this paper, we propose a conditional mixture model with hybrid Pareto components to estimate p(Y|X = x). The hybrid Pareto is a Gaussian whose upper tail has been replaced by a generalized Pareto tail. A third parameter, in addition to the location and spread parameters of the Gaussian, controls the heaviness of the upper tail. Using the hybrid Pareto in a mixture model results in a nonparametric estimator that can adapt to multimodality, asymmetry, and heavy tails. A conditional density estimator is built by modeling the parameters of the mixture estimator as functions of X. We use a neural network to implement these functions. Such conditional density estimators have important applications in many domains such as finance and insurance. We show experimentally that this novel approach better models the conditional density in terms of likelihood, compared to competing algorithms: conditional mixture models with other types of components and a classical kernel-based nonparametric model.

Robustness analysis of bogie suspension components Pareto optimised values

Science.gov (United States)

Mousavi Bideleh, Seyed Milad

2017-08-01

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.

Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

Directory of Open Access Journals (Sweden)

José Raúl Machado-Fernández

2016-12-01

Full Text Available The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE. The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.

Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining

KAUST Repository

Hussain, Shahid

2016-01-01

This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.

Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining

KAUST Repository

Hussain, Shahid

2016-07-10

This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.

A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Wanxing Sheng

2013-01-01

Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

Optimal Set-Point Synthesis in HVAC Systems

DEFF Research Database (Denmark)

Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik

2007-01-01

This paper presents optimal set-point synthesis for a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger and a water-to-air heat exchanger. The objective function is composed of the electrical power for different...... components, encompassing fans, primary/secondary pump, tertiary pump, and air-to-air heat exchanger wheel; and a fraction of thermal power used by the HVAC system. The goals that have to be achieved by the HVAC system appear as constraints in the optimization problem. To solve the optimization problem......, a steady state model of the HVAC system is derived while different supplying hydronic circuits are studied for the water-to-air heat exchanger. Finally, the optimal set-points and the optimal supplying hydronic circuit are resulted....

Multi-objective optimal power flow with FACTS devices

International Nuclear Information System (INIS)

Basu, M.

2011-01-01

This paper presents multi-objective differential evolution to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FACTS devices considered include thyristor controlled series capacitor (TCSC) and thyristor controlled phase shifter (TCPS). The proposed approach has been examined and tested on the modified IEEE 30-bus and 57-bus test systems. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, strength pareto evolutionary algorithm 2 and pareto differential evolution.

The equivalence of multi-criteria methods for radiotherapy plan optimization

International Nuclear Information System (INIS)

Breedveld, Sebastiaan; Storchi, Pascal R M; Heijmen, Ben J M

2009-01-01

Several methods can be used to achieve multi-criteria optimization of radiation therapy treatment planning, which strive for Pareto-optimality. The property of the solution being Pareto optimal is desired, because it guarantees that no criteria can be improved without deteriorating another criteria. The most widely used methods are the weighted-sum method, in which the different treatment objectives are weighted, and constrained optimization methods, in which treatment goals are set and the algorithm has to find the best plan fulfilling these goals. The constrained method used in this paper, the 2pεc (2-phase ε-constraint) method is based on the ε-constraint method, which generates Pareto-optimal solutions. Both approaches are uniquely related to each other. In this paper, we will show that it is possible to switch from the constrained method to the weighted-sum method by using the Lagrange multipliers from the constrained optimization problem, and vice versa by setting the appropriate constraints. In general, the theory presented in this paper can be useful in cases where a new situation is slightly different from the original situation, e.g. in online treatment planning, with deformations of the volumes of interest, or in automated treatment planning, where changes to the automated plan have to be made. An example of the latter is given where the planner is not satisfied with the result from the constrained method and wishes to decrease the dose in a structure. By using the Lagrange multipliers, a weighted-sum optimization problem is constructed, which generates a Pareto-optimal solution in the neighbourhood of the original plan, but fulfills the new treatment objectives.

Kantian Optimization, Social Ethos, and Pareto Efficiency

OpenAIRE

John E. Roemer

2012-01-01

Although evidence accrues in biology, anthropology and experimental economics that homo sapiens is a cooperative species, the reigning assumption in economic theory is that individuals optimize in an autarkic manner (as in Nash and Walrasian equilibrium). I here postulate an interdependent kind of optimizing behavior, called Kantian. It is shown that in simple economic models, when there are negative externalities (such as congestion effects from use of a commonly owned resource) or positive ...

An asymptotically unbiased minimum density power divergence estimator for the Pareto-tail index

DEFF Research Database (Denmark)

Dierckx, Goedele; Goegebeur, Yuri; Guillou, Armelle

2013-01-01

We introduce a robust and asymptotically unbiased estimator for the tail index of Pareto-type distributions. The estimator is obtained by fitting the extended Pareto distribution to the relative excesses over a high threshold with the minimum density power divergence criterion. Consistency...

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

International Nuclear Information System (INIS)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong; Choi, Jae Ho

2009-01-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of); Choi, Jae Ho [Samsung Techwin Co., Ltd., Changwon (Korea, Republic of)

2009-07-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with {epsilon}-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Performance comparison of low-grade ORCs (organic Rankine cycles) using R245fa, pentane and their mixtures based on the thermoeconomic multi-objective optimization and decision makings

International Nuclear Information System (INIS)

Feng, Yongqiang; Hung, TzuChen; Zhang, Yaning; Li, Bingxi; Yang, Jinfu; Shi, Yang

2015-01-01

Based on the thermoeconomic multi-objective optimization and decision makings, considering both exergy efficiency and LEC (levelized energy cost), the performance comparison of low-grade ORCs (organic Rankine cycles) using R245fa, pentane and their mixtures has been investigated. The effects of mass fraction of R245fa and four key parameters on the exergy efficiency and LEC are examined. The Pareto-optimal solutions are selected from the Pareto optimal frontier obtained by NSGA-II algorithm using three decision makings, including Shannon Entropy, LINMAP and TOPSIS. The deviation index is introduced to evaluate different decision makings. Research demonstrates that as the mass fraction of R245fa increasing, the exergy efficiency decreases first and then increases, while LEC presents a reverse trend. The optimum values from TOPSIS decision making are selected as the preferred Pareto-optimal solution for its lowest deviation index. The Pareto-optimal solutions for pentane, R245fa, and 0.5pentane/0.5R245fa in pairs of (exergy efficiency, LEC) are (0.5425, 0.104), (0.5502, 0.111), and (0.5212, 0.108), respectively. The mixture working fluids present lower thermodynamic performance and moderate economic performance than the pure working fluids under the Pareto optimization. - Highlights: • The thermoeconomic comparison between pure and mixture working fluids is investigated. • The Pareto-optimal solutions with bi-objective function using three decision makings are obtained. • The optimum values from TOPSIS decision making are selected as the preferred Pareto-optimal solution. • The mixture yields lower thermodynamic performance and moderate economic performance.

Zipf's law and influential factors of the Pareto exponent of the city size distribution: Evidence from China

OpenAIRE

GAO Hongying; WU Kangping

2007-01-01

This paper estimates the Pareto exponent of the city size (population size and economy size) distribution, all provinces, and three regions in China in 1997, 2000 and 2003 by OLS, comparatively analyzes the Pareto exponent cross section and times, and empirically analyzes the factors which impacts on the Pareto exponents of provinces. Our analyses show that the size distributions of cities in China follow the Pareto distribution and are of structural features. Variations in the value of the P...

Comparison between regenerative organic Rankine cycle (RORC) and basic organic Rankine cycle (BORC) based on thermoeconomic multi-objective optimization considering exergy efficiency and levelized energy cost (LEC)

International Nuclear Information System (INIS)

Feng, Yongqiang; Zhang, Yaning; Li, Bingxi; Yang, Jinfu; Shi, Yang

2015-01-01

Highlights: • The thermoeconomic comparison of regenerative RORC and BORC is investigated. • The Pareto frontier solution with bi-objective compares with the corresponding single-objective solutions. • The three-objective optimization of the RORC and BORC is studied. • The RORC owns 8.1% higher exergy efficiency and 21.1% more LEC than the BORC under the Pareto-optimal solution. - Abstract: Based on the thermoeconomic multi-objective optimization by using non-dominated sorting genetic algorithm (NSGA-II), considering both thermodynamic performance and economic factors, the thermoeconomic comparison of regenerative organic Rankine cycles (RORC) and basic organic Rankine cycles (BORC) are investigated. The effects of five key parameters including evaporator outlet temperature, condenser temperature, degree of superheat, pinch point temperature difference and degree of supercooling on the exergy efficiency and levelized energy cost (LEC) are examined. Meanwhile, the Pareto frontier solution with bi-objective for maximizing exergy efficiency and minimizing LEC is obtained and compared with the corresponding single-objective solutions. Research demonstrates that there is a significant negative correlation between thermodynamic performance and economic factors. And the optimum exergy efficiency and LEC for the Pareto-optimal solution of the RORC are 55.97% and 0.142 $/kW h, respectively, which are 8.1% higher exergy efficiency and 21.1% more LEC than that of the BORC under considered condition. Highest exergy and thermal efficiencies are accompanied with lowest net power output and worst economic performance. Furthermore, taking the net power output into account, detailed investigation on the three-objective optimization for maximizing exergy efficiency, maximizing net power output and minimizing LEC is discussed

Robust bayesian inference of generalized Pareto distribution ...

African Journals Online (AJOL)

En utilisant une etude exhaustive de Monte Carlo, nous prouvons que, moyennant une fonction perte generalisee adequate, on peut construire un estimateur Bayesien robuste du modele. Key words: Bayesian estimation; Extreme value; Generalized Fisher information; Gener- alized Pareto distribution; Monte Carlo; ...

Strong Convergence Bound of the Pareto Index Estimator under Right Censoring

Directory of Open Access Journals (Sweden)

Bao Tao

2010-01-01

Full Text Available Let {Xn,n≥1} be a sequence of positive independent and identically distributed random variables with common Pareto-type distribution function F(x=1−x−1/γlF(x as γ>0, where lF(x represents a slowly varying function at infinity. In this note we study the strong convergence bound of a kind of right censored Pareto index estimator under second-order regularly varying conditions.

A Regionalization Approach to select the final watershed parameter set among the Pareto solutions

Science.gov (United States)

Park, G. H.; Micheletty, P. D.; Carney, S.; Quebbeman, J.; Day, G. N.

2017-12-01

The calibration of hydrological models often results in model parameters that are inconsistent with those from neighboring basins. Considering that physical similarity exists within neighboring basins some of the physically related parameters should be consistent among them. Traditional manual calibration techniques require an iterative process to make the parameters consistent, which takes additional effort in model calibration. We developed a multi-objective optimization procedure to calibrate the National Weather Service (NWS) Research Distributed Hydrological Model (RDHM), using the Nondominant Sorting Genetic Algorithm (NSGA-II) with expert knowledge of the model parameter interrelationships one objective function. The multi-objective algorithm enables us to obtain diverse parameter sets that are equally acceptable with respect to the objective functions and to choose one from the pool of the parameter sets during a subsequent regionalization step. Although all Pareto solutions are non-inferior, we exclude some of the parameter sets that show extremely values for any of the objective functions to expedite the selection process. We use an apriori model parameter set derived from the physical properties of the watershed (Koren et al., 2000) to assess the similarity for a given parameter across basins. Each parameter is assigned a weight based on its assumed similarity, such that parameters that are similar across basins are given higher weights. The parameter weights are useful to compute a closeness measure between Pareto sets of nearby basins. The regionalization approach chooses the Pareto parameter sets that minimize the closeness measure of the basin being regionalized. The presentation will describe the results of applying the regionalization approach to a set of pilot basins in the Upper Colorado basin as part of a NASA-funded project.

Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

International Nuclear Information System (INIS)

Pang, X.; Rybarcyk, L.J.

2014-01-01

Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster

Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

Energy Technology Data Exchange (ETDEWEB)

Pang, X., E-mail: xpang@lanl.gov; Rybarcyk, L.J.

2014-03-21

Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster.

Multiobjective optimization for design of multifunctional sandwich panel heat pipes with micro-architected truss cores

International Nuclear Information System (INIS)

Roper, Christopher S.

2011-01-01

A micro-architected multifunctional structure, a sandwich panel heat pipe with a micro-scale truss core and arterial wick, is modeled and optimized. To characterize multiple functionalities, objective equations are formulated for density, compressive modulus, compressive strength, and maximum heat flux. Multiobjective optimization is used to determine the Pareto-optimal design surfaces, which consist of hundreds of individually optimized designs. The Pareto-optimal surfaces for different working fluids (water, ethanol, and perfluoro(methylcyclohexane)) as well as different micro-scale truss core materials (metal, ceramic, and polymer) are determined and compared. Examination of the Pareto fronts allows comparison of the trade-offs between density, compressive stiffness, compressive strength, and maximum heat flux in the design of multifunctional sandwich panel heat pipes with micro-scale truss cores. Heat fluxes up to 3.0 MW/m 2 are predicted for silicon carbide truss core heat pipes with water as the working fluid.

Multiobjective anatomy-based dose optimization for HDR-brachytherapy with constraint free deterministic algorithms

International Nuclear Information System (INIS)

Milickovic, N.; Lahanas, M.; Papagiannopoulou, M.; Zamboglou, N.; Baltas, D.

2002-01-01

In high dose rate (HDR) brachytherapy, conventional dose optimization algorithms consider multiple objectives in the form of an aggregate function that transforms the multiobjective problem into a single-objective problem. As a result, there is a loss of information on the available alternative possible solutions. This method assumes that the treatment planner exactly understands the correlation between competing objectives and knows the physical constraints. This knowledge is provided by the Pareto trade-off set obtained by single-objective optimization algorithms with a repeated optimization with different importance vectors. A mapping technique avoids non-feasible solutions with negative dwell weights and allows the use of constraint free gradient-based deterministic algorithms. We compare various such algorithms and methods which could improve their performance. This finally allows us to generate a large number of solutions in a few minutes. We use objectives expressed in terms of dose variances obtained from a few hundred sampling points in the planning target volume (PTV) and in organs at risk (OAR). We compare two- to four-dimensional Pareto fronts obtained with the deterministic algorithms and with a fast-simulated annealing algorithm. For PTV-based objectives, due to the convex objective functions, the obtained solutions are global optimal. If OARs are included, then the solutions found are also global optimal, although local minima may be present as suggested. (author)

Axiomatizations of Pareto Equilibria in Multicriteria Games

NARCIS (Netherlands)

Voorneveld, M.; Vermeulen, D.; Borm, P.E.M.

1997-01-01

We focus on axiomatizations of the Pareto equilibrium concept in multicriteria games based on consistency.Axiomatizations of the Nash equilibrium concept by Peleg and Tijs (1996) and Peleg, Potters, and Tijs (1996) have immediate generalizations.The axiomatization of Norde et al.(1996) cannot be

Ranking of microRNA target prediction scores by Pareto front analysis.

Science.gov (United States)

Sahoo, Sudhakar; Albrecht, Andreas A

2010-12-01

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure, which encourages further research towards a higher-dimensional analysis of Pareto fronts. Copyright © 2010 Elsevier Ltd. All rights reserved.

Optimal configuration of power grid sources based on optimal particle swarm algorithm

Science.gov (United States)

Wen, Yuanhua

2018-04-01

In order to optimize the distribution problem of power grid sources, an optimized particle swarm optimization algorithm is proposed. First, the concept of multi-objective optimization and the Pareto solution set are enumerated. Then, the performance of the classical genetic algorithm, the classical particle swarm optimization algorithm and the improved particle swarm optimization algorithm are analyzed. The three algorithms are simulated respectively. Compared with the test results of each algorithm, the superiority of the algorithm in convergence and optimization performance is proved, which lays the foundation for subsequent micro-grid power optimization configuration solution.

Income inequality in Romania: The exponential-Pareto distribution

Science.gov (United States)

Oancea, Bogdan; Andrei, Tudorel; Pirjol, Dan

2017-03-01

We present a study of the distribution of the gross personal income and income inequality in Romania, using individual tax income data, and both non-parametric and parametric methods. Comparing with official results based on household budget surveys (the Family Budgets Survey and the EU-SILC data), we find that the latter underestimate the income share of the high income region, and the overall income inequality. A parametric study shows that the income distribution is well described by an exponential distribution in the low and middle incomes region, and by a Pareto distribution in the high income region with Pareto coefficient α = 2.53. We note an anomaly in the distribution in the low incomes region (∼9,250 RON), and present a model which explains it in terms of partial income reporting.

Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Babaie, Meisam; Farmani, Mohammad Reza

2011-01-01

Multi-objective optimization for design of a benchmark cogeneration system namely as the CGAM cogeneration system is performed. In optimization approach, Exergetic, Exergoeconomic and Environmental objectives are considered, simultaneously. In this regard, the set of Pareto optimal solutions known as the Pareto frontier is obtained using the MOPSO (multi-objective particle swarm optimizer). The exergetic efficiency as an exergetic objective is maximized while the unit cost of the system product and the cost of the environmental impact respectively as exergoeconomic and environmental objectives are minimized. Economic model which is utilized in the exergoeconomic analysis is built based on both simple model (used in original researches of the CGAM system) and the comprehensive modeling namely as TTR (total revenue requirement) method (used in sophisticated exergoeconomic analysis). Finally, a final optimal solution from optimal set of the Pareto frontier is selected using a fuzzy decision-making process based on the Bellman-Zadeh approach and results are compared with corresponding results obtained in a traditional decision-making process. Further, results are compared with the corresponding performance of the base case CGAM system and optimal designs of previous works and discussed. -- Highlights: → A multi-objective optimization approach has been implemented in optimization of a benchmark cogeneration system. → Objective functions based on the environmental impact evaluation, thermodynamic and economic analysis are obtained and optimized. → Particle swarm optimizer implemented and its robustness is compared with NSGA-II. → A final optimal configuration is found using various decision-making approaches. → Results compared with previous works in the field.

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

Science.gov (United States)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

Science.gov (United States)

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Decision and Inhibitory Rule Optimization for Decision Tables with Many-valued Decisions

KAUST Repository

Alsolami, Fawaz

2016-04-25

‘If-then’ rule sets are one of the most expressive and human-readable knowledge representations. This thesis deals with optimization and analysis of decision and inhibitory rules for decision tables with many-valued decisions. The most important areas of applications are knowledge extraction and representation. The benefit of considering inhibitory rules is connected with the fact that in some situations they can describe more knowledge than the decision ones. Decision tables with many-valued decisions arise in combinatorial optimization, computational geometry, fault diagnosis, and especially under the processing of data sets. In this thesis, various examples of real-life problems are considered which help to understand the motivation of the investigation. We extend relatively simple results obtained earlier for decision rules over decision tables with many-valued decisions to the case of inhibitory rules. The behavior of Shannon functions (which characterize complexity of rule systems) is studied for finite and infinite information systems, for global and local approaches, and for decision and inhibitory rules. The extensions of dynamic programming for the study of decision rules over decision tables with single-valued decisions are generalized to the case of decision tables with many-valued decisions. These results are also extended to the case of inhibitory rules. As a result, we have algorithms (i) for multi-stage optimization of rules relative to such criteria as length or coverage, (ii) for counting the number of optimal rules, (iii) for construction of Pareto optimal points for bi-criteria optimization problems, (iv) for construction of graphs describing relationships between two cost functions, and (v) for construction of graphs describing relationships between cost and accuracy of rules. The applications of created tools include comparison (based on information about Pareto optimal points) of greedy heuristics for bi-criteria optimization of rules

Application of Failure Mode and Effect Analysis (FMEA), cause and effect analysis, and Pareto diagram in conjunction with HACCP to a corn curl manufacturing plant.

Science.gov (United States)

Varzakas, Theodoros H; Arvanitoyannis, Ioannis S

2007-01-01

The Failure Mode and Effect Analysis (FMEA) model has been applied for the risk assessment of corn curl manufacturing. A tentative approach of FMEA application to the snacks industry was attempted in an effort to exclude the presence of GMOs in the final product. This is of crucial importance both from the ethics and the legislation (Regulations EC 1829/2003; EC 1830/2003; Directive EC 18/2001) point of view. The Preliminary Hazard Analysis and the Fault Tree Analysis were used to analyze and predict the occurring failure modes in a food chain system (corn curls processing plant), based on the functions, characteristics, and/or interactions of the ingredients or the processes, upon which the system depends. Critical Control points have been identified and implemented in the cause and effect diagram (also known as Ishikawa, tree diagram, and the fishbone diagram). Finally, Pareto diagrams were employed towards the optimization of GMOs detection potential of FMEA.

A heuristic ranking approach on capacity benefit margin determination using Pareto-based evolutionary programming technique.

Science.gov (United States)

Othman, Muhammad Murtadha; Abd Rahman, Nurulazmi; Musirin, Ismail; Fotuhi-Firuzabad, Mahmud; Rajabi-Ghahnavieh, Abbas

2015-01-01

This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.

A Heuristic Ranking Approach on Capacity Benefit Margin Determination Using Pareto-Based Evolutionary Programming Technique

Directory of Open Access Journals (Sweden)

Muhammad Murtadha Othman

2015-01-01

Full Text Available This paper introduces a novel multiobjective approach for capacity benefit margin (CBM assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE in various conditions. Eventually, the power transfer based available transfer capability (ATC is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.

A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

Directory of Open Access Journals (Sweden)

J. S. Sadaghiani

2014-04-01

Full Text Available Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions.

Comparison of Two Methods Used to Model Shape Parameters of Pareto Distributions

Science.gov (United States)

Liu, C.; Charpentier, R.R.; Su, J.

2011-01-01

Two methods are compared for estimating the shape parameters of Pareto field-size (or pool-size) distributions for petroleum resource assessment. Both methods assume mature exploration in which most of the larger fields have been discovered. Both methods use the sizes of larger discovered fields to estimate the numbers and sizes of smaller fields: (1) the tail-truncated method uses a plot of field size versus size rank, and (2) the log-geometric method uses data binned in field-size classes and the ratios of adjacent bin counts. Simulation experiments were conducted using discovered oil and gas pool-size distributions from four petroleum systems in Alberta, Canada and using Pareto distributions generated by Monte Carlo simulation. The estimates of the shape parameters of the Pareto distributions, calculated by both the tail-truncated and log-geometric methods, generally stabilize where discovered pool numbers are greater than 100. However, with fewer than 100 discoveries, these estimates can vary greatly with each new discovery. The estimated shape parameters of the tail-truncated method are more stable and larger than those of the log-geometric method where the number of discovered pools is more than 100. Both methods, however, tend to underestimate the shape parameter. Monte Carlo simulation was also used to create sequences of discovered pool sizes by sampling from a Pareto distribution with a discovery process model using a defined exploration efficiency (in order to show how biased the sampling was in favor of larger fields being discovered first). A higher (more biased) exploration efficiency gives better estimates of the Pareto shape parameters. ?? 2011 International Association for Mathematical Geosciences.

An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

Directory of Open Access Journals (Sweden)

Tao Zhang

2012-01-01

Full Text Available An improved particle swarm optimization (PSO algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP. For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms

International Nuclear Information System (INIS)

Tahani, Mojtaba; Babayan, Narek; Astaraei, Fatemeh Razi; Moghadam, Ali

2015-01-01

Highlights: • The performance of four different Meta heuristic optimization algorithms was studied. • Power coefficient and produced torque on stationary blade were selected as objective functions. • Chord and twist distributions were selected as decision variables. • All optimization algorithms were combined with blade element momentum theory. • The best Pareto front was obtained by multi objective flower pollination algorithm for HATCTs. - Abstract: The performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry. According to this fact, the optimum performance will be achieved by optimized geometry. In this research study, the multi objective optimization of the HATCT is carried out by using four different multi objective optimization algorithms and their performance is evaluated in combination with blade element momentum theory (BEM). The second version of non-dominated sorting genetic algorithm (NSGA-II), multi objective particle swarm optimization algorithm (MOPSO), multi objective cuckoo search algorithm (MOCS) and multi objective flower pollination algorithm (MOFPA) are the selected algorithms. The power coefficient and the produced torque on stationary blade are selected as objective functions and chord and twist distributions along the blade span are selected as decision variables. These algorithms are combined with the blade element momentum (BEM) theory for the purpose of achieving the best Pareto front. The obtained Pareto fronts are compared with each other. Different sets of experiments are carried out by considering different numbers of iterations, population size and tip speed ratios. The Pareto fronts which are achieved by MOFPA and NSGA-II have better quality in comparison to MOCS and MOPSO, but on the other hand a detail comparison between the first fronts of MOFPA and NSGA-II indicated that MOFPA algorithm can obtain the best Pareto front and can maximize the power coefficient up to 4.3% and the

Multi-objective Design Optimization of a Parallel Schönflies-motion Robot

DEFF Research Database (Denmark)

Wu, Guanglei; Bai, Shaoping; Hjørnet, Preben

2016-01-01

. The dynamic performance is concerned mainly the capability of force transmission in the parallel kinematic chain, for which transmission indices are defined. The Pareto-front is obtained to investigate the influence of the design variables to the robot performance. Dynamic characteristics for three Pareto......This paper introduces a parallel Schoenflies-motion robot with rectangular workspace, which is suitable for pick-and-place operations. A multi-objective optimization problem is formulated to optimize the robot's geometric parameters with consideration of kinematic and dynamic performances...

Computing the Pareto-Nash equilibrium set in finite multi-objective mixed-strategy games

Directory of Open Access Journals (Sweden)

Victoria Lozan

2013-10-01

Full Text Available The Pareto-Nash equilibrium set (PNES is described as intersection of graphs of efficient response mappings. The problem of PNES computing in finite multi-objective mixed-strategy games (Pareto-Nash games is considered. A method for PNES computing is studied. Mathematics Subject Classification 2010: 91A05, 91A06, 91A10, 91A43, 91A44.

A two-stage approach for multi-objective decision making with applications to system reliability optimization

International Nuclear Information System (INIS)

Li Zhaojun; Liao Haitao; Coit, David W.

2009-01-01

This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.

Optimization of the RF cavity heat load and trip rates for CEBAF at 12 GeV

Energy Technology Data Exchange (ETDEWEB)

Zhang, He [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Roblin, Yves R. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Freyberger, Arne P. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Krafft, Geoffrey A. [Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States); Terzic, Balsa P. [Old Dominion Univ., Norfolk, VA (United States)

2017-05-01

The Continuous Electron Beam Accelerator Facility at JLab has 200 RF cavities in the north linac and the south linac respectively after the 12 GeV upgrade. The purpose of this work is to simultaneously optimize the heat load and the trip rate for the cavities and to reconstruct the pareto-optimal front in a timely manner when some of the cavities are turned down. By choosing an efficient optimizer and strategically creating the initial gradients, the pareto-optimal front for no more than 15 cavities down can be re-established within 20 seconds.

Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors

International Nuclear Information System (INIS)

Hedayat, Afshin; Davilu, Hadi; Barfrosh, Ahmad Abdollahzadeh; Sepanloo, Kamran

2009-01-01

To successfully carry out material irradiation experiments and radioisotope productions, a high thermal neutron flux at irradiation box over a desired life time of a core configuration is needed. On the other hand, reactor safety and operational constraints must be preserved during core configuration selection. Two main objectives and two safety and operational constraints are suggested to optimize reactor core configuration design. Suggested parameters and conditions are considered as two separate fitness functions composed of two main objectives and two penalty functions. This is a constrained and combinatorial type of a multi-objective optimization problem. In this paper, a fast and effective hybrid artificial intelligence algorithm is introduced and developed to reach a Pareto optimal set. The hybrid algorithm is composed of a fast and elitist multi-objective genetic algorithm (GA) and a fast fitness function evaluating system based on the cascade feed forward artificial neural networks (ANNs). A specific GA representation of core configuration and also special GA operators are introduced and used to overcome the combinatorial constraints of this optimization problem. A software package (Core Pattern Calculator 1) is developed to prepare and reform required data for ANNs training and also to revise the optimization results. Some practical test parameters and conditions are suggested to adjust main parameters of the hybrid algorithm. Results show that introduced ANNs can be trained and estimate selected core parameters of a research reactor very quickly. It improves effectively optimization process. Final optimization results show that a uniform and dense diversity of Pareto fronts are gained over a wide range of fitness function values. To take a more careful selection of Pareto optimal solutions, a revision system is introduced and used. The revision of gained Pareto optimal set is performed by using developed software package. Also some secondary operational

Optimization of the core configuration design using a hybrid artificial intelligence algorithm for research reactors

Energy Technology Data Exchange (ETDEWEB)

Hedayat, Afshin, E-mail: ahedayat@aut.ac.i [Department of Nuclear Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, P.O. Box 15875-4413, Tehran (Iran, Islamic Republic of); Reactor Research and Development School, Nuclear Science and Technology Research Institute (NSTRI), End of North Karegar Street, P.O. Box 14395-836, Tehran (Iran, Islamic Republic of); Davilu, Hadi [Department of Nuclear Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, P.O. Box 15875-4413, Tehran (Iran, Islamic Republic of); Barfrosh, Ahmad Abdollahzadeh [Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, P.O. Box 15875-4413, Tehran (Iran, Islamic Republic of); Sepanloo, Kamran [Reactor Research and Development School, Nuclear Science and Technology Research Institute (NSTRI), End of North Karegar Street, P.O. Box 14395-836, Tehran (Iran, Islamic Republic of)

2009-12-15

To successfully carry out material irradiation experiments and radioisotope productions, a high thermal neutron flux at irradiation box over a desired life time of a core configuration is needed. On the other hand, reactor safety and operational constraints must be preserved during core configuration selection. Two main objectives and two safety and operational constraints are suggested to optimize reactor core configuration design. Suggested parameters and conditions are considered as two separate fitness functions composed of two main objectives and two penalty functions. This is a constrained and combinatorial type of a multi-objective optimization problem. In this paper, a fast and effective hybrid artificial intelligence algorithm is introduced and developed to reach a Pareto optimal set. The hybrid algorithm is composed of a fast and elitist multi-objective genetic algorithm (GA) and a fast fitness function evaluating system based on the cascade feed forward artificial neural networks (ANNs). A specific GA representation of core configuration and also special GA operators are introduced and used to overcome the combinatorial constraints of this optimization problem. A software package (Core Pattern Calculator 1) is developed to prepare and reform required data for ANNs training and also to revise the optimization results. Some practical test parameters and conditions are suggested to adjust main parameters of the hybrid algorithm. Results show that introduced ANNs can be trained and estimate selected core parameters of a research reactor very quickly. It improves effectively optimization process. Final optimization results show that a uniform and dense diversity of Pareto fronts are gained over a wide range of fitness function values. To take a more careful selection of Pareto optimal solutions, a revision system is introduced and used. The revision of gained Pareto optimal set is performed by using developed software package. Also some secondary operational

Multi-objective optimization of a cascade refrigeration system: Exergetic, economic, environmental, and inherent safety analysis

International Nuclear Information System (INIS)

Eini, Saeed; Shahhosseini, Hamidreza; Delgarm, Navid; Lee, Moonyong; Bahadori, Alireza

2016-01-01

Highlights: • A multi-objective optimization is performed for a cascade refrigeration cycle. • The optimization problem considers inherently safe design as well as 3E analysis. • As a measure of inherent safety level a quantitative risk analysis is utilized. • A CO 2 /NH 3 cascade refrigeration system is compared with a CO 2 /C 3 H 8 system. - Abstract: Inherently safer design is the new approach to maximize the overall safety of a process plant. This approach suggests some risk reduction strategies to be implemented in the early stages of design. In this paper a multi-objective optimization was performed considering economic, exergetic, and environmental aspects besides evaluation of the inherent safety level of a cascade refrigeration system. The capital costs, the processing costs, and the social cost due to CO 2 emission were considered to be included in the economic objective function. Exergetic efficiency of the plant was considered as the second objective function. As a measure of inherent safety level, Quantitative Risk Assessment (QRA) was performed to calculate total risk level of the cascade as the third objective function. Two cases (ammonia and propane) were considered to be compared as the refrigerant of the high temperature circuit. The achieved optimum solutions from the multi–objective optimization process were given as Pareto frontier. The ultimate optimal solution from available solutions on the Pareto optimal curve was selected using Decision-Makings approaches. NSGA-II algorithm was used to obtain Pareto optimal frontiers. Also, three decision-making approaches (TOPSIS, LINMAP, and Shannon’s entropy methods) were utilized to select the final optimum point. Considering continuous material release from the major equipment in the plant, flash and jet fire scenarios were considered for the CO 2 /C 3 H 8 cycle and toxic hazards were considered for the CO 2 /NH 3 cycle. The results showed no significant differences between CO 2 /NH 3 and

Kinetics of wealth and the Pareto law.

Science.gov (United States)

Boghosian, Bruce M

2014-04-01

An important class of economic models involve agents whose wealth changes due to transactions with other agents. Several authors have pointed out an analogy with kinetic theory, which describes molecules whose momentum and energy change due to interactions with other molecules. We pursue this analogy and derive a Boltzmann equation for the time evolution of the wealth distribution of a population of agents for the so-called Yard-Sale Model of wealth exchange. We examine the solutions to this equation by a combination of analytical and numerical methods and investigate its long-time limit. We study an important limit of this equation for small transaction sizes and derive a partial integrodifferential equation governing the evolution of the wealth distribution in a closed economy. We then describe how this model can be extended to include features such as inflation, production, and taxation. In particular, we show that the model with taxation exhibits the basic features of the Pareto law, namely, a lower cutoff to the wealth density at small values of wealth, and approximate power-law behavior at large values of wealth.

Research and Setting the Modified Algorithm "Predator-Prey" in the Problem of the Multi-Objective Optimization

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2016-01-01

Full Text Available We consider a class of algorithms for multi-objective optimization - Pareto-approximation algorithms, which suppose a preliminary building of finite-dimensional approximation of a Pareto set, thereby also a Pareto front of the problem. The article gives an overview of population and non-population algorithms of the Pareto-approximation, identifies their strengths and weaknesses, and presents a canonical algorithm "predator-prey", showing its shortcomings. We offer a number of modifications of the canonical algorithm "predator-prey" with the aim to overcome the drawbacks of this algorithm, present the results of a broad study of the efficiency of these modifications of the algorithm. The peculiarity of the study is the use of the quality indicators of the Pareto-approximation, which previous publications have not used. In addition, we present the results of the meta-optimization of the modified algorithm, i.e. determining the optimal values of some free parameters of the algorithm. The study of efficiency of the modified algorithm "predator-prey" has shown that the proposed modifications allow us to improve the following indicators of the basic algorithm: cardinality of a set of the archive solutions, uniformity of archive solutions, and computation time. By and large, the research results have shown that the modified and meta-optimized algorithm enables achieving exactly the same approximation as the basic algorithm, but with the number of preys being one order less. Computational costs are proportionally reduced.

Multi-objective Reactive Power Optimization Based on Improved Particle Swarm Algorithm

Science.gov (United States)

Cui, Xue; Gao, Jian; Feng, Yunbin; Zou, Chenlu; Liu, Huanlei

2018-01-01

In this paper, an optimization model with the minimum active power loss and minimum voltage deviation of node and maximum static voltage stability margin as the optimization objective is proposed for the reactive power optimization problems. By defining the index value of reactive power compensation, the optimal reactive power compensation node was selected. The particle swarm optimization algorithm was improved, and the selection pool of global optimal and the global optimal of probability (p-gbest) were introduced. A set of Pareto optimal solution sets is obtained by this algorithm. And by calculating the fuzzy membership value of the pareto optimal solution sets, individuals with the smallest fuzzy membership value were selected as the final optimization results. The above improved algorithm is used to optimize the reactive power of IEEE14 standard node system. Through the comparison and analysis of the results, it has been proven that the optimization effect of this algorithm was very good.

Global stability-based design optimization of truss structures using ...

Indian Academy of Sciences (India)

Furthermore, a pure pareto-ranking based multi-objective optimization model is employed for the design optimization of the truss structure with multiple objectives. The computational performance of the optimization model is increased by implementing an island model into its evolutionary search mechanism. The proposed ...

Optimal design and planning of glycerol-based biorefinery supply chains under uncertainty

DEFF Research Database (Denmark)

Loureiro da Costa Lira Gargalo, Carina; Carvalho, Ana; Gernaey, Krist V.

2017-01-01

-echelon mixed integer linear programming problem is proposed based upon a previous model, GlyThink. In the new formulation, market uncertainties are taken into account at the strategic planning level. The robustness of the supply chain structures is analyzed based on statistical data provided...... by the implementation of the Monte Carlo method, where a deterministic optimization problem is solved for each scenario. Furthermore, the solution of the stochastic multi-objective optimization model, points to the Pareto set of trade-off solutions obtained when maximizing the NPV and minimizing environmental......The optimal design and planning of glycerol-based biorefinery supply chains is critical for the development and implementation of this concept in a sustainable manner. To achieve this, a decision-making framework is proposed in this work, to holistically optimize the design and planning...

Thermodynamic analysis and multi-objective optimization of various ORC (organic Rankine cycle) configurations using zeotropic mixtures

International Nuclear Information System (INIS)

Sadeghi, Mohsen; Nemati, Arash; Ghavimi, Alireza; Yari, Mortaza

2016-01-01

In this paper, the performance of the ORC (organic Rankine cycle) powered by geothermal water, in three different configurations, including the simple ORC, PTORC (parallel two-stage ORC) and STORC (series two-stage ORC), using zeotrpoic working fluids is investigated from the viewpoints of the energy and exergy. In addition, considering the net power output and TSP (turbine size parameter) as the two objective functions, the multi-objective optimization with the aim of maximizing the first function and minimizing the second one, is performed to determine the optimal values of decision variables including evaporators 1 and 2 pressure, the pinch point temperature difference and the superheating degree. The results show that using zeotropic mixtures as the working fluid instead of a pure fluid such as R245fa, leads to 27.76%, 24.98% and 24.79% improvement in power generation in the simple ORC, PTORC and STORC, respectively and also lower values of TSP. Moreover, it is observed that STORC has the highest amount of net power output and R407A can be selected as the most appropriate working fluid. The optimization results demonstrate that at the final optimum point achieved by Pareto frontier, the values of the objective functions are gained 877 kW and 0.08218 m, respectively. - Highlights: • Three different configurations of ORC powered by geothermal water are analyzed. • The thermodynamic performance of these systems using zeotrpoic mixtures is investigated. • Multi-objective optimization is performed to obtain optimum performance. • The Pareto-frontier is used to automatically select the most promising solutions.

Optimal Point-to-Point Trajectory Tracking of Redundant Manipulators using Generalized Pattern Search

Directory of Open Access Journals (Sweden)

Thi Rein Myo

2008-11-01

Full Text Available Optimal point-to-point trajectory planning for planar redundant manipulator is considered in this study. The main objective is to minimize the sum of the position error of the end-effector at each intermediate point along the trajectory so that the end-effector can track the prescribed trajectory accurately. An algorithm combining Genetic Algorithm and Pattern Search as a Generalized Pattern Search GPS is introduced to design the optimal trajectory. To verify the proposed algorithm, simulations for a 3-D-O-F planar manipulator with different end-effector trajectories have been carried out. A comparison between the Genetic Algorithm and the Generalized Pattern Search shows that The GPS gives excellent tracking performance.

Genetic algorithm based separation cascade optimization

International Nuclear Information System (INIS)

Mahendra, A.K.; Sanyal, A.; Gouthaman, G.; Bera, T.K.

2008-01-01

The conventional separation cascade design procedure does not give an optimum design because of squaring-off, variation of flow rates and separation factor of the element with respect to stage location. Multi-component isotope separation further complicates the design procedure. Cascade design can be stated as a constrained multi-objective optimization. Cascade's expectation from the separating element is multi-objective i.e. overall separation factor, cut, optimum feed and separative power. Decision maker may aspire for more comprehensive multi-objective goals where optimization of cascade is coupled with the exploration of separating element optimization vector space. In real life there are many issues which make it important to understand the decision maker's perception of cost-quality-speed trade-off and consistency of preferences. Genetic algorithm (GA) is one such evolutionary technique that can be used for cascade design optimization. This paper addresses various issues involved in the GA based multi-objective optimization of the separation cascade. Reference point based optimization methodology with GA based Pareto optimality concept for separation cascade was found pragmatic and promising. This method should be explored, tested, examined and further developed for binary as well as multi-component separations. (author)

Achieving Optimal Privacy in Trust-Aware Social Recommender Systems

Science.gov (United States)

Dokoohaki, Nima; Kaleli, Cihan; Polat, Huseyin; Matskin, Mihhail

Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommender's accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.

Optimal allocation and adaptive VAR control of PV-DG in distribution networks

International Nuclear Information System (INIS)

Fu, Xueqian; Chen, Haoyong; Cai, Runqing; Yang, Ping

2015-01-01

Highlights: • A methodology for optimal PV-DG allocation based on a combination of algorithms. • Dealing with the randomicity of solar power energy using CCSP. • Presenting a VAR control strategy to balance the technical demands. • Finding the Pareto solutions using MOPSO and SVM. • Evaluating the Pareto solutions using WRSR. - Abstract: The development of distributed generation (DG) has brought new challenges to power networks. One of them that catches extensive attention is the voltage regulation problem of distribution networks caused by DG. Optimal allocation of DG in distribution networks is another well-known problem being widely investigated. This paper proposes a new method for the optimal allocation of photovoltaic distributed generation (PV-DG) considering the non-dispatchable characteristics of PV units. An adaptive reactive power control model is introduced in PV-DG allocation as to balance the trade-off between the improvement of voltage quality and the minimization of power loss in a distribution network integrated with PV-DG units. The optimal allocation problem is formulated as a chance-constrained stochastic programming (CCSP) model for dealing with the randomness of solar power energy. A novel algorithm combining the multi-objective particle swarm optimization (MOPSO) with support vector machines (SVM) is proposed to find the Pareto front consisting of a set of possible solutions. The Pareto solutions are further evaluated using the weighted rank sum ratio (WRSR) method to help the decision-maker obtain the desired solution. Simulation results on a 33-bus radial distribution system show that the optimal allocation method can fully take into account the time-variant characteristics and probability distribution of PV-DG, and obtain the best allocation scheme

Multi-objective Optimization of Pulsed Gas Metal Arc Welding Process Using Neuro NSGA-II

Science.gov (United States)

Pal, Kamal; Pal, Surjya K.

2018-05-01

Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

The feasibility of using Pareto fronts for comparison of treatment planning systems and delivery techniques

DEFF Research Database (Denmark)

Ottosson, Rickard O; Engstrom, Per E; Sjöström, David

2008-01-01

constitute the Pareto front. The Pareto concept applies well to the inverse planning process, which involves inherently contradictory objectives, high and uniform target dose on one hand, and sparing of surrounding tissue and nearby organs at risk (OAR) on the other. Due to the specific characteristics...

Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model

Directory of Open Access Journals (Sweden)

Yi Liu

2016-01-01

Full Text Available Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II on a daily streamflow forecasting model based on support vector machine (SVM. The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.

Coordinated Voltage Control in Distribution Network with the Presence of DGs and Variable Loads Using Pareto and Fuzzy Logic

Directory of Open Access Journals (Sweden)

José Raúl Castro

2016-02-01

Full Text Available This paper presents an efficient algorithm to solve the multi-objective (MO voltage control problem in distribution networks. The proposed algorithm minimizes the following three objectives: voltage variation on pilot buses, reactive power production ratio deviation, and generator voltage deviation. This work leverages two optimization techniques: fuzzy logic to find the optimum value of the reactive power of the distributed generation (DG and Pareto optimization to find the optimal value of the pilot bus voltage so that this produces lower losses under the constraints that the voltage remains within established limits. Variable loads and DGs are taken into account in this paper. The algorithm is tested on an IEEE 13-node test feeder and the results show the effectiveness of the proposed model.

Statistical inferences with jointly type-II censored samples from two Pareto distributions

Science.gov (United States)

Abu-Zinadah, Hanaa H.

2017-08-01

In the several fields of industries the product comes from more than one production line, which is required to work the comparative life tests. This problem requires sampling of the different production lines, then the joint censoring scheme is appeared. In this article we consider the life time Pareto distribution with jointly type-II censoring scheme. The maximum likelihood estimators (MLE) and the corresponding approximate confidence intervals as well as the bootstrap confidence intervals of the model parameters are obtained. Also Bayesian point and credible intervals of the model parameters are presented. The life time data set is analyzed for illustrative purposes. Monte Carlo results from simulation studies are presented to assess the performance of our proposed method.

Global Search of a Three-dimensional Low Solidity Circular Cascade Diffuser for Centrifugal Blowers by Meta-model Assisted Optimization

Science.gov (United States)

Sakaguchi, Daisaku; Sakue, Daiki; Tun, Min Thaw

2018-04-01

A three-dimensional blade of a low solidity circular cascade diffuser in centrifugal blowers is designed by means of a multi-point optimization technique. The optimization aims at improving static pressure coefficient at a design point and at a small flow rate condition. Moreover, a clear definition of secondary flow expressed by positive radial velocity at hub side is taken into consideration in constraints. The number of design parameters for three-dimensional blade reaches to 10 in this study, such as a radial gap, a radial chord length and mean camber angle distribution of the LSD blade with five control points, control point between hub and shroud with two design freedom. Optimization results show clear Pareto front and selected optimum design shows good improvement of pressure rise in diffuser at small flow rate conditions. It is found that three-dimensional blade has advantage to stabilize the secondary flow effect with improving pressure recovery of the low solidity circular cascade diffuser.

Dual-mode nested search method for categorical uncertain multi-objective optimization

Science.gov (United States)

Tang, Long; Wang, Hu

2016-10-01

Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

Sci-Thur AM: Planning - 04: Evaluation of the fluence complexity, solution quality, and run efficiency produced by five fluence parameterizations implemented in PARETO multiobjective radiotherapy treatment planning software.

Science.gov (United States)

Champion, H; Fiege, J; McCurdy, B; Potrebko, P; Cull, A

2012-07-01

PARETO (Pareto-Aware Radiotherapy Evolutionary Treatment Optimization) is a novel multiobjective treatment planning system that performs beam orientation and fluence optimization simultaneously using an advanced evolutionary algorithm. In order to reduce the number of parameters involved in this enormous search space, we present several methods for modeling the beam fluence. The parameterizations are compared using innovative tools that evaluate fluence complexity, solution quality, and run efficiency. A PARETO run is performed using the basic weight (BW), linear gradient (LG), cosine transform (CT), beam group (BG), and isodose-projection (IP) methods for applying fluence modulation over the projection of the Planning Target Volume in the beam's-eye-view plane. The solutions of each run are non-dominated with respect to other trial solutions encountered during the run. However, to compare the solution quality of independent runs, each run competes against every other run in a round robin fashion. Score is assigned based on the fraction of solutions that survive when a tournament selection operator is applied to the solutions of the two competitors. To compare fluence complexity, a modulation index, fractal dimension, and image gradient entropy are calculated for the fluence maps of each optimal plan. We have found that the LG method results in superior solution quality for a spine phantom, lung patient, and cauda equina patient. The BG method produces solutions with the highest degree of fluence complexity. Most methods result in comparable run times. The LG method produces superior solution quality using a moderate degree of fluence modulation. © 2012 American Association of Physicists in Medicine.

Optimal harvesting for a predator-prey agent-based model using difference equations.

Science.gov (United States)

Oremland, Matthew; Laubenbacher, Reinhard

2015-03-01

In this paper, a method known as Pareto optimization is applied in the solution of a multi-objective optimization problem. The system in question is an agent-based model (ABM) wherein global dynamics emerge from local interactions. A system of discrete mathematical equations is formulated in order to capture the dynamics of the ABM; while the original model is built up analytically from the rules of the model, the paper shows how minor changes to the ABM rule set can have a substantial effect on model dynamics. To address this issue, we introduce parameters into the equation model that track such changes. The equation model is amenable to mathematical theory—we show how stability analysis can be performed and validated using ABM data. We then reduce the equation model to a simpler version and implement changes to allow controls from the ABM to be tested using the equations. Cohen's weighted κ is proposed as a measure of similarity between the equation model and the ABM, particularly with respect to the optimization problem. The reduced equation model is used to solve a multi-objective optimization problem via a technique known as Pareto optimization, a heuristic evolutionary algorithm. Results show that the equation model is a good fit for ABM data; Pareto optimization provides a suite of solutions to the multi-objective optimization problem that can be implemented directly in the ABM.

Genetic Algorithm Optimizes Q-LAW Control Parameters

Science.gov (United States)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Contribution to the evaluation and to the improvement of multi-objective optimization methods: application to the optimization of nuclear fuel reloading pattern

International Nuclear Information System (INIS)

Collette, Y.

2002-01-01

In this thesis, we study the general problem of the selection of a multi-objective optimization method, then we study the improvement so as to efficiently solve a problem. The pertinent selection of a method presume the existence of a methodology: we have built tools to perform evaluation of performances and we propose an original method dedicated to the classification of know optimization methods. Our step has been applied to the elaboration of new methods for solving a very difficult problem: the nuclear core reload pattern optimization. First, we looked for a non usual approach of performances measurement: we have 'measured' the behavior of a method. To reach this goal, we have introduced several metrics. We have proposed to evaluate the 'aesthetic' of a distribution of solutions by defining two new metrics: a 'spacing metric' and a metric that allow us to measure the size of the biggest hole in the distribution of solutions. Then, we studied the convergence of multi-objective optimization methods by using some metrics defined in scientific literature and by proposing some more metrics: the 'Pareto ratio' which computes a ratio of solution production. Lastly, we have defined new metrics intended to better apprehend the behavior of optimization methods: the 'speed metric', which allows to compute the speed profile and a 'distribution metric' which allows to compute statistical distribution of solutions along the Pareto frontier. Next, we have studied transformations of a multi-objective problem and defined news methods: the modified Tchebychev method, or the penalized weighted sum of objective functions. We have elaborated new techniques to choose the initial point. These techniques allow to produce new initial points closer and closer to the Pareto frontier and, thanks to the 'proximal optimality concept', allowing dramatic improvements in the convergence of a multi-objective optimization method. Lastly, we have defined new vectorial multi-objective optimization

GENERALIZED DOUBLE PARETO SHRINKAGE.

Science.gov (United States)

Armagan, Artin; Dunson, David B; Lee, Jaeyong

2013-01-01

We propose a generalized double Pareto prior for Bayesian shrinkage estimation and inferences in linear models. The prior can be obtained via a scale mixture of Laplace or normal distributions, forming a bridge between the Laplace and Normal-Jeffreys' priors. While it has a spike at zero like the Laplace density, it also has a Student's t -like tail behavior. Bayesian computation is straightforward via a simple Gibbs sampling algorithm. We investigate the properties of the maximum a posteriori estimator, as sparse estimation plays an important role in many problems, reveal connections with some well-established regularization procedures, and show some asymptotic results. The performance of the prior is tested through simulations and an application.

Decomposition and Simplification of Multivariate Data using Pareto Sets.

Science.gov (United States)

Huettenberger, Lars; Heine, Christian; Garth, Christoph

2014-12-01

Topological and structural analysis of multivariate data is aimed at improving the understanding and usage of such data through identification of intrinsic features and structural relationships among multiple variables. We present two novel methods for simplifying so-called Pareto sets that describe such structural relationships. Such simplification is a precondition for meaningful visualization of structurally rich or noisy data. As a framework for simplification operations, we introduce a decomposition of the data domain into regions of equivalent structural behavior and the reachability graph that describes global connectivity of Pareto extrema. Simplification is then performed as a sequence of edge collapses in this graph; to determine a suitable sequence of such operations, we describe and utilize a comparison measure that reflects the changes to the data that each operation represents. We demonstrate and evaluate our methods on synthetic and real-world examples.

A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

International Nuclear Information System (INIS)

Zarepisheh, Masoud; Li, Nan; Long, Troy; Romeijn, H. Edwin; Tian, Zhen; Jia, Xun; Jiang, Steve B.

2014-01-01

Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment

Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein-small molecule docking.

Science.gov (United States)

Wang, Hongrui; Liu, Hongwei; Cai, Leixin; Wang, Caixia; Lv, Qiang

2017-07-10

In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange. The Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies. Our findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein-small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand.

A least squares approach for efficient and reliable short-term versus long-term optimization

DEFF Research Database (Denmark)

Christiansen, Lasse Hjuler; Capolei, Andrea; Jørgensen, John Bagterp

2017-01-01

The uncertainties related to long-term forecasts of oil prices impose significant financial risk on ventures of oil production. To minimize risk, oil companies are inclined to maximize profit over short-term horizons ranging from months to a few years. In contrast, conventional production...... optimization maximizes long-term profits over horizons that span more than a decade. To address this challenge, the oil literature has introduced short-term versus long-term optimization. Ideally, this problem is solved by a posteriori multi-objective optimization methods that generate an approximation...... the balance between the objectives, leaving an unfulfilled potential to increase profits. To promote efficient and reliable short-term versus long-term optimization, this paper introduces a natural way to characterize desirable Pareto points and proposes a novel least squares (LS) method. Unlike hierarchical...

Exergoeconomic analysis and multi-objective optimization of an ejector refrigeration cycle powered by an internal combustion (HCCI) engine

International Nuclear Information System (INIS)

Sadeghi, Mohsen; Mahmoudi, S.M.S.; Khoshbakhti Saray, R.

2015-01-01

Highlights: • Ejector refrigeration systems powered by HCCI engine is proposed. • A new two-dimensional model is developed for the ejector. • Multi-objective optimization is performed for the proposed system. • Pareto frontier is plotted for multi-objective optimization. - Abstract: Ejector refrigeration systems powered by low-grade heat sources have been an attractive research subject for a lot of researchers. In the present work the waste heat from exhaust gases of a HCCI (homogeneous charge compression ignition) engine is utilized to drive the ejector refrigeration system. Considering the frictional effects on the ejector wall, a new two-dimensional model is developed for the ejector. Energy, exergy and exergoeconomic analysis performed for the proposed system using the MATLAB software. In addition, considering the exergy efficiency and the product unit cost of the system as objective functions, a multi-objective optimization is performed for the system to find the optimum design variables including the generator, condenser and evaporator temperatures. The product unit cost is minimized while the exergy efficiency is maximized using the genetic algorithm. The optimization results are obtained as a set of optimal points and the Pareto frontier is plotted for multi-objective optimization. The results of the optimization show that ejector refrigeration cycle is operating at optimum state based on exergy efficiency and product unit cost when generator, condenser and evaporator work at 94.54 °C, 33.44 °C and 0.03 °C, respectively

Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

Energy Technology Data Exchange (ETDEWEB)

Bellary, Sayed Ahmed Imran; Samad, Abdus [Indian Institute of Technology Madras, Chennai (India); Husain, Afzal [Sultan Qaboos University, Al-Khoudh (Oman)

2014-12-15

The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

International Nuclear Information System (INIS)

Bellary, Sayed Ahmed Imran; Samad, Abdus; Husain, Afzal

2014-01-01

The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

A probabilistic computational framework for bridge network optimal maintenance scheduling

International Nuclear Information System (INIS)

Bocchini, Paolo; Frangopol, Dan M.

2011-01-01

This paper presents a probabilistic computational framework for the Pareto optimization of the preventive maintenance applications to bridges of a highway transportation network. The bridge characteristics are represented by their uncertain reliability index profiles. The in/out of service states of the bridges are simulated taking into account their correlation structure. Multi-objective Genetic Algorithms have been chosen as numerical tool for the solution of the optimization problem. The design variables of the optimization are the preventive maintenance schedules of all the bridges of the network. The two conflicting objectives are the minimization of the total present maintenance cost and the maximization of the network performance indicator. The final result is the Pareto front of optimal solutions among which the managers should chose, depending on engineering and economical factors. A numerical example illustrates the application of the proposed approach.

Optimizing Probability of Detection Point Estimate Demonstration

Science.gov (United States)

Koshti, Ajay M.

2017-01-01

Probability of detection (POD) analysis is used in assessing reliably detectable flaw size in nondestructive evaluation (NDE). MIL-HDBK-18231and associated mh18232POD software gives most common methods of POD analysis. Real flaws such as cracks and crack-like flaws are desired to be detected using these NDE methods. A reliably detectable crack size is required for safe life analysis of fracture critical parts. The paper provides discussion on optimizing probability of detection (POD) demonstration experiments using Point Estimate Method. POD Point estimate method is used by NASA for qualifying special NDE procedures. The point estimate method uses binomial distribution for probability density. Normally, a set of 29 flaws of same size within some tolerance are used in the demonstration. The optimization is performed to provide acceptable value for probability of passing demonstration (PPD) and achieving acceptable value for probability of false (POF) calls while keeping the flaw sizes in the set as small as possible.

Multi-agent Pareto appointment exchanging in hospital patient scheduling

NARCIS (Netherlands)

I.B. Vermeulen (Ivan); S.M. Bohte (Sander); D.J.A. Somefun (Koye); J.A. La Poutré (Han)

2007-01-01

htmlabstractWe present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment

Multi-agent Pareto appointment exchanging in hospital patient scheduling

NARCIS (Netherlands)

Vermeulen, I.B.; Bohté, S.M.; Somefun, D.J.A.; Poutré, La J.A.

2007-01-01

We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm:

Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing

NARCIS (Netherlands)

Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.

2006-01-01

The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval

A fast method for calculating reliable event supports in tree reconciliations via Pareto optimality.

Science.gov (United States)

To, Thu-Hien; Jacox, Edwin; Ranwez, Vincent; Scornavacca, Celine

2015-11-14

Given a gene and a species tree, reconciliation methods attempt to retrieve the macro-evolutionary events that best explain the discrepancies between the two tree topologies. The DTL parsimonious approach searches for a most parsimonious reconciliation between a gene tree and a (dated) species tree, considering four possible macro-evolutionary events (speciation, duplication, transfer, and loss) with specific costs. Unfortunately, many events are erroneously predicted due to errors in the input trees, inappropriate input cost values or because of the existence of several equally parsimonious scenarios. It is thus crucial to provide a measure of the reliability for predicted events. It has been recently proposed that the reliability of an event can be estimated via its frequency in the set of most parsimonious reconciliations obtained using a variety of reasonable input cost vectors. To compute such a support, a straightforward but time-consuming approach is to generate the costs slightly departing from the original ones, independently compute the set of all most parsimonious reconciliations for each vector, and combine these sets a posteriori. Another proposed approach uses Pareto-optimality to partition cost values into regions which induce reconciliations with the same number of DTL events. The support of an event is then defined as its frequency in the set of regions. However, often, the number of regions is not large enough to provide reliable supports. We present here a method to compute efficiently event supports via a polynomial-sized graph, which can represent all reconciliations for several different costs. Moreover, two methods are proposed to take into account alternative input costs: either explicitly providing an input cost range or allowing a tolerance for the over cost of a reconciliation. Our methods are faster than the region based method, substantially faster than the sampling-costs approach, and have a higher event-prediction accuracy on

Total Path Length and Number of Terminal Nodes for Decision Trees

KAUST Repository

Hussain, Shahid

2014-09-13

This paper presents a new tool for study of relationships between total path length (average depth) and number of terminal nodes for decision trees. These relationships are important from the point of view of optimization of decision trees. In this particular case of total path length and number of terminal nodes, the relationships between these two cost functions are closely related with space-time trade-off. In addition to algorithm to compute the relationships, the paper also presents results of experiments with datasets from UCI ML Repository1. These experiments show how two cost functions behave for a given decision table and the resulting plots show the Pareto frontier or Pareto set of optimal points. Furthermore, in some cases this Pareto frontier is a singleton showing the total optimality of decision trees for the given decision table.

Vector optimization theory, applications, and extensions

CERN Document Server

Jahn, Johannes

2011-01-01

This new edition of a key monograph has fresh sections on the work of Edgeworth and Pareto in its presentation in a general setting of the fundamentals and important results of vector optimization. It examines background material, applications and theories.

A multiobjective optimization framework for multicontaminant industrial water network design.

Science.gov (United States)

Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge

2011-07-01

The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

International Nuclear Information System (INIS)

Zio, E.; Baraldi, P.; Pedroni, N.

2009-01-01

Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants. As many other real-world engineering problems, power system generation scheduling involves multiple, conflicting optimization criteria for which there exists no single best solution with respect to all criteria considered. Multi-objective optimization algorithms, based on the principle of Pareto optimality, can then be designed to search for the set of nondominated scheduling solutions from which the decision-maker (DM) must a posteriori choose the preferred alternative. On the other hand, often, information is available a priori regarding the preference values of the DM with respect to the objectives. When possible, it is important to exploit this information during the search so as to focus it on the region of preference of the Pareto-optimal set. In this paper, ways are explored to use this preference information for driving a multi-objective genetic algorithm towards the preferential region of the Pareto-optimal front. Two methods are considered: the first one extends the concept of Pareto dominance by biasing the chromosome replacement step of the algorithm by means of numerical weights that express the DM' s preferences; the second one drives the search algorithm by changing the shape of the dominance region according to linear trade-off functions specified by the DM. The effectiveness of the proposed approaches is first compared on a case study of literature. Then, a nonlinear, constrained, two-objective power generation scheduling problem is effectively tackled

Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT

Energy Technology Data Exchange (ETDEWEB)

Aleman, Dionne M [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King' s College Road, Toronto, ON M5S 3G8 (Canada); Glaser, Daniel [Division of Optimization and Systems Theory, Department of Mathematics, Royal Institute of Technology, Stockholm (Sweden); Romeijn, H Edwin [Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109-2117 (United States); Dempsey, James F, E-mail: aleman@mie.utoronto.c, E-mail: romeijn@umich.ed, E-mail: jfdempsey@viewray.co [ViewRay, Inc. 2 Thermo Fisher Way, Village of Oakwood, OH 44146 (United States)

2010-09-21

One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT

International Nuclear Information System (INIS)

Aleman, Dionne M; Glaser, Daniel; Romeijn, H Edwin; Dempsey, James F

2010-01-01

One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

Random phenotypic variation of yeast (Saccharomyces cerevisiae) single-gene knockouts fits a double pareto-lognormal distribution.

Science.gov (United States)

Graham, John H; Robb, Daniel T; Poe, Amy R

2012-01-01

Distributed robustness is thought to influence the buffering of random phenotypic variation through the scale-free topology of gene regulatory, metabolic, and protein-protein interaction networks. If this hypothesis is true, then the phenotypic response to the perturbation of particular nodes in such a network should be proportional to the number of links those nodes make with neighboring nodes. This suggests a probability distribution approximating an inverse power-law of random phenotypic variation. Zero phenotypic variation, however, is impossible, because random molecular and cellular processes are essential to normal development. Consequently, a more realistic distribution should have a y-intercept close to zero in the lower tail, a mode greater than zero, and a long (fat) upper tail. The double Pareto-lognormal (DPLN) distribution is an ideal candidate distribution. It consists of a mixture of a lognormal body and upper and lower power-law tails. If our assumptions are true, the DPLN distribution should provide a better fit to random phenotypic variation in a large series of single-gene knockout lines than other skewed or symmetrical distributions. We fit a large published data set of single-gene knockout lines in Saccharomyces cerevisiae to seven different probability distributions: DPLN, right Pareto-lognormal (RPLN), left Pareto-lognormal (LPLN), normal, lognormal, exponential, and Pareto. The best model was judged by the Akaike Information Criterion (AIC). Phenotypic variation among gene knockouts in S. cerevisiae fits a double Pareto-lognormal (DPLN) distribution better than any of the alternative distributions, including the right Pareto-lognormal and lognormal distributions. A DPLN distribution is consistent with the hypothesis that developmental stability is mediated, in part, by distributed robustness, the resilience of gene regulatory, metabolic, and protein-protein interaction networks. Alternatively, multiplicative cell growth, and the mixing of

WE-AB-209-07: Explicit and Convex Optimization of Plan Quality Metrics in Intensity-Modulated Radiation Therapy Treatment Planning

International Nuclear Information System (INIS)

Engberg, L; Eriksson, K; Hardemark, B; Forsgren, A

2016-01-01

Purpose: To formulate objective functions of a multicriteria fluence map optimization model that correlate well with plan quality metrics, and to solve this multicriteria model by convex approximation. Methods: In this study, objectives of a multicriteria model are formulated to explicitly either minimize or maximize a dose-at-volume measure. Given the widespread agreement that dose-at-volume levels play important roles in plan quality assessment, these objectives correlate well with plan quality metrics. This is in contrast to the conventional objectives, which are to maximize clinical goal achievement by relating to deviations from given dose-at-volume thresholds: while balancing the new objectives means explicitly balancing dose-at-volume levels, balancing the conventional objectives effectively means balancing deviations. Constituted by the inherently non-convex dose-at-volume measure, the new objectives are approximated by the convex mean-tail-dose measure (CVaR measure), yielding a convex approximation of the multicriteria model. Results: Advantages of using the convex approximation are investigated through juxtaposition with the conventional objectives in a computational study of two patient cases. Clinical goals of each case respectively point out three ROI dose-at-volume measures to be considered for plan quality assessment. This is translated in the convex approximation into minimizing three mean-tail-dose measures. Evaluations of the three ROI dose-at-volume measures on Pareto optimal plans are used to represent plan quality of the Pareto sets. Besides providing increased accuracy in terms of feasibility of solutions, the convex approximation generates Pareto sets with overall improved plan quality. In one case, the Pareto set generated by the convex approximation entirely dominates that generated with the conventional objectives. Conclusion: The initial computational study indicates that the convex approximation outperforms the conventional objectives

A New Generalization of the Pareto Distribution and Its Application to Insurance Data

Directory of Open Access Journals (Sweden)

Mohamed E. Ghitany

2018-02-01

Full Text Available The Pareto classical distribution is one of the most attractive in statistics and particularly in the scenario of actuarial statistics and finance. For example, it is widely used when calculating reinsurance premiums. In the last years, many alternative distributions have been proposed to obtain better adjustments especially when the tail of the empirical distribution of the data is very long. In this work, an alternative generalization of the Pareto distribution is proposed and its properties are studied. Finally, application of the proposed model to the earthquake insurance data set is presented.

A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization

International Nuclear Information System (INIS)

Zhang, Enze; Wu, Yifei; Chen, Qingwei

2014-01-01

This paper proposes a practical approach, combining bare-bones particle swarm optimization and sensitivity-based clustering for solving multi-objective reliability redundancy allocation problems (RAPs). A two-stage process is performed to identify promising solutions. Specifically, a new bare-bones multi-objective particle swarm optimization algorithm (BBMOPSO) is developed and applied in the first stage to identify a Pareto-optimal set. This algorithm mainly differs from other multi-objective particle swarm optimization algorithms in the parameter-free particle updating strategy, which is especially suitable for handling the complexity and nonlinearity of RAPs. Moreover, by utilizing an approach based on the adaptive grid to update the global particle leaders, a mutation operator to improve the exploration ability and an effective constraint handling strategy, the integrated BBMOPSO algorithm can generate excellent approximation of the true Pareto-optimal front for RAPs. This is followed by a data clustering technique based on difference sensitivity in the second stage to prune the obtained Pareto-optimal set and obtain a small, workable sized set of promising solutions for system implementation. Two illustrative examples are presented to show the feasibility and effectiveness of the proposed approach

Interactive Nonlinear Multiobjective Optimization Methods

OpenAIRE

Miettinen, Kaisa; Hakanen, Jussi; Podkopaev, Dmitry

2016-01-01

An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the most satisfactory Pareto optimal solution can be found for her or his. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the...

Adaptive surrogate model based multiobjective optimization for coastal aquifer management

Science.gov (United States)

Song, Jian; Yang, Yun; Wu, Jianfeng; Wu, Jichun; Sun, Xiaomin; Lin, Jin

2018-06-01

In this study, a novel surrogate model assisted multiobjective memetic algorithm (SMOMA) is developed for optimal pumping strategies of large-scale coastal groundwater problems. The proposed SMOMA integrates an efficient data-driven surrogate model with an improved non-dominated sorted genetic algorithm-II (NSGAII) that employs a local search operator to accelerate its convergence in optimization. The surrogate model based on Kernel Extreme Learning Machine (KELM) is developed and evaluated as an approximate simulator to generate the patterns of regional groundwater flow and salinity levels in coastal aquifers for reducing huge computational burden. The KELM model is adaptively trained during evolutionary search to satisfy desired fidelity level of surrogate so that it inhibits error accumulation of forecasting and results in correctly converging to true Pareto-optimal front. The proposed methodology is then applied to a large-scale coastal aquifer management in Baldwin County, Alabama. Objectives of minimizing the saltwater mass increase and maximizing the total pumping rate in the coastal aquifers are considered. The optimal solutions achieved by the proposed adaptive surrogate model are compared against those solutions obtained from one-shot surrogate model and original simulation model. The adaptive surrogate model does not only improve the prediction accuracy of Pareto-optimal solutions compared with those by the one-shot surrogate model, but also maintains the equivalent quality of Pareto-optimal solutions compared with those by NSGAII coupled with original simulation model, while retaining the advantage of surrogate models in reducing computational burden up to 94% of time-saving. This study shows that the proposed methodology is a computationally efficient and promising tool for multiobjective optimizations of coastal aquifer managements.

Joint global optimization of tomographic data based on particle swarm optimization and decision theory

Science.gov (United States)

Paasche, H.; Tronicke, J.

2012-04-01

In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto

Exploring trade-offs between VMAT dose quality and delivery efficiency using a network optimization approach

International Nuclear Information System (INIS)

Salari, Ehsan; Craft, David; Wala, Jeremiah

2012-01-01

To formulate and solve the fluence-map merging procedure of the recently-published VMAT treatment-plan optimization method, called vmerge, as a bi-criteria optimization problem. Using an exact merging method rather than the previously-used heuristic, we are able to better characterize the trade-off between the delivery efficiency and dose quality. vmerge begins with a solution of the fluence-map optimization problem with 180 equi-spaced beams that yields the ‘ideal’ dose distribution. Neighboring fluence maps are then successively merged, meaning that they are added together and delivered as a single map. The merging process improves the delivery efficiency at the expense of deviating from the initial high-quality dose distribution. We replace the original merging heuristic by considering the merging problem as a discrete bi-criteria optimization problem with the objectives of maximizing the treatment efficiency and minimizing the deviation from the ideal dose. We formulate this using a network-flow model that represents the merging problem. Since the problem is discrete and thus non-convex, we employ a customized box algorithm to characterize the Pareto frontier. The Pareto frontier is then used as a benchmark to evaluate the performance of the standard vmerge algorithm as well as two other similar heuristics. We test the exact and heuristic merging approaches on a pancreas and a prostate cancer case. For both cases, the shape of the Pareto frontier suggests that starting from a high-quality plan, we can obtain efficient VMAT plans through merging neighboring fluence maps without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial plan simplifications, but to deviate in quality for more drastic efficiency improvements. This work presents a network optimization approach to the merging problem. Contrasting the trade-off curves of the

Exploring trade-offs between VMAT dose quality and delivery efficiency using a network optimization approach.

Science.gov (United States)

Salari, Ehsan; Wala, Jeremiah; Craft, David

2012-09-07

To formulate and solve the fluence-map merging procedure of the recently-published VMAT treatment-plan optimization method, called VMERGE, as a bi-criteria optimization problem. Using an exact merging method rather than the previously-used heuristic, we are able to better characterize the trade-off between the delivery efficiency and dose quality. VMERGE begins with a solution of the fluence-map optimization problem with 180 equi-spaced beams that yields the 'ideal' dose distribution. Neighboring fluence maps are then successively merged, meaning that they are added together and delivered as a single map. The merging process improves the delivery efficiency at the expense of deviating from the initial high-quality dose distribution. We replace the original merging heuristic by considering the merging problem as a discrete bi-criteria optimization problem with the objectives of maximizing the treatment efficiency and minimizing the deviation from the ideal dose. We formulate this using a network-flow model that represents the merging problem. Since the problem is discrete and thus non-convex, we employ a customized box algorithm to characterize the Pareto frontier. The Pareto frontier is then used as a benchmark to evaluate the performance of the standard VMERGE algorithm as well as two other similar heuristics. We test the exact and heuristic merging approaches on a pancreas and a prostate cancer case. For both cases, the shape of the Pareto frontier suggests that starting from a high-quality plan, we can obtain efficient VMAT plans through merging neighboring fluence maps without substantially deviating from the initial dose distribution. The trade-off curves obtained by the various heuristics are contrasted and shown to all be equally capable of initial plan simplifications, but to deviate in quality for more drastic efficiency improvements. This work presents a network optimization approach to the merging problem. Contrasting the trade-off curves of the merging

Synthetic optimization of air turbine for dental handpieces.

Science.gov (United States)

Shi, Z Y; Dong, T

2014-01-01

A synthetic optimization of Pelton air turbine in dental handpieces concerning the power output, compressed air consumption and rotation speed in the mean time is implemented by employing a standard design procedure and variable limitation from practical dentistry. The Pareto optimal solution sets acquired by using the Normalized Normal Constraint method are mainly comprised of two piecewise continuous parts. On the Pareto frontier, the supply air stagnation pressure stalls at the lower boundary of the design space, the rotation speed is a constant value within the recommended range from literature, the blade tip clearance insensitive to while the nozzle radius increases with power output and mass flow rate of compressed air to which the residual geometric dimensions are showing an opposite trend within their respective "pieces" compared to the nozzle radius.

The dosimetric impact of leaf interdigitation and leaf width on VMAT treatment planning in Pinnacle: comparing Pareto fronts

International Nuclear Information System (INIS)

Van Kesteren, Z; Janssen, T M; Damen, E; Van Vliet-Vroegindeweij, C

2012-01-01

To evaluate in an objective way the effect of leaf interdigitation and leaf width on volumetric modulated arc therapy plans in Pinnacle. Three multileaf collimators (MLCs) were modeled: two 10 mm leaf width MLCs, with and without interdigitating leafs, and a 5 mm leaf width MLC with interdigitating leafs. Three rectum patients and three prostate patients were used for the planning study. In order to compare treatment techniques in an objective way, a Pareto front comparison was carried out. 200 plans were generated in an automated way, per patient per MLC model, resulting in a total of 3600 plans. From these plans, Pareto-optimal plans were selected which were evaluated for various dosimetric variables. The capability of leaf interdigitation showed little dosimetric impact on the treatment plans, when comparing the 10 mm leaf width MLC with and without leaf interdigitation. When comparing the 10 mm leaf width MLC with the 5 mm leaf width MLC, both with interdigitating leafs, improvement in plan quality was observed. For both patient groups, the integral dose was reduced by 0.6 J for the thin MLC. For the prostate patients, the mean dose to the anal sphincter was reduced by 1.8 Gy and the conformity of the V 95% was reduced by 0.02 using the thin MLC. The V 65% of the rectum was reduced by 0.1% and the dose homogeneity with 1.5%. For rectum patients, the mean dose to the bowel was reduced by 1.4 Gy and the mean dose to the bladder with 0.8 Gy for the thin MLC. The conformity of the V 95% was equivalent for the 10 and 5 mm leaf width MLCs for the rectum patients. We have objectively compared three types of MLCs in a planning study for prostate and rectum patients by analyzing Pareto-optimal plans which were generated in an automated way. Interdigitation of MLC leafs does not generate better plans using the SmartArc algorithm in Pinnacle. Changing the MLC leaf width from 10 to 5 mm generates better treatment plans although the clinical relevance remains to be proven

The dosimetric impact of leaf interdigitation and leaf width on VMAT treatment planning in Pinnacle: comparing Pareto fronts.

Science.gov (United States)

van Kesteren, Z; Janssen, T M; Damen, E; van Vliet-Vroegindeweij, C

2012-05-21

To evaluate in an objective way the effect of leaf interdigitation and leaf width on volumetric modulated arc therapy plans in Pinnacle. Three multileaf collimators (MLCs) were modeled: two 10 mm leaf width MLCs, with and without interdigitating leafs, and a 5 mm leaf width MLC with interdigitating leafs. Three rectum patients and three prostate patients were used for the planning study. In order to compare treatment techniques in an objective way, a Pareto front comparison was carried out. 200 plans were generated in an automated way, per patient per MLC model, resulting in a total of 3600 plans. From these plans, Pareto-optimal plans were selected which were evaluated for various dosimetric variables. The capability of leaf interdigitation showed little dosimetric impact on the treatment plans, when comparing the 10 mm leaf width MLC with and without leaf interdigitation. When comparing the 10 mm leaf width MLC with the 5 mm leaf width MLC, both with interdigitating leafs, improvement in plan quality was observed. For both patient groups, the integral dose was reduced by 0.6 J for the thin MLC. For the prostate patients, the mean dose to the anal sphincter was reduced by 1.8 Gy and the conformity of the V(95%) was reduced by 0.02 using the thin MLC. The V(65%) of the rectum was reduced by 0.1% and the dose homogeneity with 1.5%. For rectum patients, the mean dose to the bowel was reduced by 1.4 Gy and the mean dose to the bladder with 0.8 Gy for the thin MLC. The conformity of the V(95%) was equivalent for the 10 and 5 mm leaf width MLCs for the rectum patients. We have objectively compared three types of MLCs in a planning study for prostate and rectum patients by analyzing Pareto-optimal plans which were generated in an automated way. Interdigitation of MLC leafs does not generate better plans using the SmartArc algorithm in Pinnacle. Changing the MLC leaf width from 10 to 5 mm generates better treatment plans although the clinical relevance remains

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

Science.gov (United States)

Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

2015-01-01

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Solving multiobjective optimal reactive power dispatch using modified NSGA-II

Energy Technology Data Exchange (ETDEWEB)

Jeyadevi, S.; Baskar, S.; Babulal, C.K.; Willjuice Iruthayarajan, M. [Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu 625 015 (India)

2011-02-15

This paper addresses an application of modified NSGA-II (MNSGA-II) by incorporating controlled elitism and dynamic crowding distance (DCD) strategies in NSGA-II to multiobjective optimal reactive power dispatch (ORPD) problem by minimizing real power loss and maximizing the system voltage stability. To validate the Pareto-front obtained using MNSGA-II, reference Pareto-front is generated using multiple runs of single objective optimization with weighted sum of objectives. For simulation purposes, IEEE 30 and IEEE 118 bus test systems are considered. The performance of MNSGA-II, NSGA-II and multiobjective particle swarm optimization (MOPSO) approaches are compared with respect to multiobjective performance measures. TOPSIS technique is applied on obtained non-dominated solutions to determine best compromise solution (BCS). Karush-Kuhn-Tucker (KKT) conditions are also applied on the obtained non-dominated solutions to substantiate a claim on optimality. Simulation results are quite promising and the MNSGA-II performs better than NSGA-II in maintaining diversity and authenticates its potential to solve multiobjective ORPD effectively. (author)

A superlinear interior points algorithm for engineering design optimization

Science.gov (United States)

Herskovits, J.; Asquier, J.

1990-01-01

We present a quasi-Newton interior points algorithm for nonlinear constrained optimization. It is based on a general approach consisting of the iterative solution in the primal and dual spaces of the equalities in Karush-Kuhn-Tucker optimality conditions. This is done in such a way to have primal and dual feasibility at each iteration, which ensures satisfaction of those optimality conditions at the limit points. This approach is very strong and efficient, since at each iteration it only requires the solution of two linear systems with the same matrix, instead of quadratic programming subproblems. It is also particularly appropriate for engineering design optimization inasmuch at each iteration a feasible design is obtained. The present algorithm uses a quasi-Newton approximation of the second derivative of the Lagrangian function in order to have superlinear asymptotic convergence. We discuss theoretical aspects of the algorithm and its computer implementation.

An EM Algorithm for Double-Pareto-Lognormal Generalized Linear Model Applied to Heavy-Tailed Insurance Claims

Directory of Open Access Journals (Sweden)

Enrique Calderín-Ojeda

2017-11-01

Full Text Available Generalized linear models might not be appropriate when the probability of extreme events is higher than that implied by the normal distribution. Extending the method for estimating the parameters of a double Pareto lognormal distribution (DPLN in Reed and Jorgensen (2004, we develop an EM algorithm for the heavy-tailed Double-Pareto-lognormal generalized linear model. The DPLN distribution is obtained as a mixture of a lognormal distribution with a double Pareto distribution. In this paper the associated generalized linear model has the location parameter equal to a linear predictor which is used to model insurance claim amounts for various data sets. The performance is compared with those of the generalized beta (of the second kind and lognorma distributions.

Primal Interior-Point Method for Large Sparse Minimax Optimization

Czech Academy of Sciences Publication Activity Database

Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan

2009-01-01

Roč. 45, č. 5 (2009), s. 841-864 ISSN 0023-5954 R&D Projects: GA AV ČR IAA1030405; GA ČR GP201/06/P397 Institutional research plan: CEZ:AV0Z10300504 Keywords : unconstrained optimization * large-scale optimization * minimax optimization * nonsmooth optimization * interior-point methods * modified Newton methods * variable metric methods * computational experiments Subject RIV: BA - General Mathematics Impact factor: 0.445, year: 2009 http://dml.cz/handle/10338.dmlcz/140034

Optimal Design of a Centrifugal Compressor Impeller Using Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Soo-Yong Cho

2012-01-01

Full Text Available An optimization study was conducted on a centrifugal compressor. Eight design variables were chosen from the control points for the Bezier curves which widely influenced the geometric variation; four design variables were selected to optimize the flow passage between the hub and the shroud, and other four design variables were used to improve the performance of the impeller blade. As an optimization algorithm, an artificial neural network (ANN was adopted. Initially, the design of experiments was applied to set up the initial data space of the ANN, which was improved during the optimization process using a genetic algorithm. If a result of the ANN reached a higher level, that result was re-calculated by computational fluid dynamics (CFD and was applied to develop a new ANN. The prediction difference between the ANN and CFD was consequently less than 1% after the 6th generation. Using this optimization technique, the computational time for the optimization was greatly reduced and the accuracy of the optimization algorithm was increased. The efficiency was improved by 1.4% without losing the pressure ratio, and Pareto-optimal solutions of the efficiency versus the pressure ratio were obtained through the 21st generation.

Word frequencies: A comparison of Pareto type distributions

Science.gov (United States)

Wiegand, Martin; Nadarajah, Saralees; Si, Yuancheng

2018-03-01

Mehri and Jamaati (2017) [18] used Zipf's law to model word frequencies in Holy Bible translations for one hundred live languages. We compare the fit of Zipf's law to a number of Pareto type distributions. The latter distributions are shown to provide the best fit, as judged by a number of comparative plots and error measures. The fit of Zipf's law appears generally poor.

Particle swarm optimization: an alternative in marine propeller optimization?

Science.gov (United States)

Vesting, F.; Bensow, R. E.

2018-01-01

This article deals with improving and evaluating the performance of two evolutionary algorithm approaches for automated engineering design optimization. Here a marine propeller design with constraints on cavitation nuisance is the intended application. For this purpose, the particle swarm optimization (PSO) algorithm is adapted for multi-objective optimization and constraint handling for use in propeller design. Three PSO algorithms are developed and tested for the optimization of four commercial propeller designs for different ship types. The results are evaluated by interrogating the generation medians and the Pareto front development. The same propellers are also optimized utilizing the well established NSGA-II genetic algorithm to provide benchmark results. The authors' PSO algorithms deliver comparable results to NSGA-II, but converge earlier and enhance the solution in terms of constraints violation.

Including robustness in multi-criteria optimization for intensity-modulated proton therapy

Science.gov (United States)

Chen, Wei; Unkelbach, Jan; Trofimov, Alexei; Madden, Thomas; Kooy, Hanne; Bortfeld, Thomas; Craft, David

2012-02-01

We present a method to include robustness in a multi-criteria optimization (MCO) framework for intensity-modulated proton therapy (IMPT). The approach allows one to simultaneously explore the trade-off between different objectives as well as the trade-off between robustness and nominal plan quality. In MCO, a database of plans each emphasizing different treatment planning objectives, is pre-computed to approximate the Pareto surface. An IMPT treatment plan that strikes the best balance between the different objectives can be selected by navigating on the Pareto surface. In our approach, robustness is integrated into MCO by adding robustified objectives and constraints to the MCO problem. Uncertainties (or errors) of the robust problem are modeled by pre-calculated dose-influence matrices for a nominal scenario and a number of pre-defined error scenarios (shifted patient positions, proton beam undershoot and overshoot). Objectives and constraints can be defined for the nominal scenario, thus characterizing nominal plan quality. A robustified objective represents the worst objective function value that can be realized for any of the error scenarios and thus provides a measure of plan robustness. The optimization method is based on a linear projection solver and is capable of handling large problem sizes resulting from a fine dose grid resolution, many scenarios, and a large number of proton pencil beams. A base-of-skull case is used to demonstrate the robust optimization method. It is demonstrated that the robust optimization method reduces the sensitivity of the treatment plan to setup and range errors to a degree that is not achieved by a safety margin approach. A chordoma case is analyzed in more detail to demonstrate the involved trade-offs between target underdose and brainstem sparing as well as robustness and nominal plan quality. The latter illustrates the advantage of MCO in the context of robust planning. For all cases examined, the robust optimization for

Pareto genealogies arising from a Poisson branching evolution model with selection.

Science.gov (United States)

Huillet, Thierry E

2014-02-01

We study a class of coalescents derived from a sampling procedure out of N i.i.d. Pareto(α) random variables, normalized by their sum, including β-size-biasing on total length effects (β Poisson-Dirichlet (α, -β) Ξ-coalescent (α ε[0, 1)), or to a family of continuous-time Beta (2 - α, α - β)Λ-coalescents (α ε[1, 2)), or to the Kingman coalescent (α ≥ 2). We indicate that this class of coalescent processes (and their scaling limits) may be viewed as the genealogical processes of some forward in time evolving branching population models including selection effects. In such constant-size population models, the reproduction step, which is based on a fitness-dependent Poisson Point Process with scaling power-law(α) intensity, is coupled to a selection step consisting of sorting out the N fittest individuals issued from the reproduction step.

An Evolutionary Efficiency Alternative to the Notion of Pareto Efficiency

NARCIS (Netherlands)

I.P. van Staveren (Irene)

2012-01-01

textabstractThe paper argues that the notion of Pareto efficiency builds on two normative assumptions: the more general consequentialist norm of any efficiency criterion, and the strong no-harm principle of the prohibition of any redistribution during the economic process that hurts at least one

An experimental and multi-objective optimization study of a forced draft cooling tower with different fills

International Nuclear Information System (INIS)

Singh, Kuljeet; Das, Ranjan

2016-01-01

Highlights: • Experimental and optimization study on forced draft cooling tower is done. • New correlations for splash, trickle and film type fills are proposed. • Multi-objective performance optimization study has been done using NSGA-II. • Weighted decision making criterion is proposed depending upon user priority. • Proposed generalized methodology can be implemented in industrial cooling towers. - Abstract: In the present study, a forced draft mechanical cooling tower has been experimentally investigated using trickle, film and splash fills. Various performance parameters such as range, tower characteristic ratio, effectiveness and water evaporation rate are first analyzed for each fill. Thereafter, based upon the experimental data, pertinent correlations have been developed for performance parameters by considering mass flow rates of water and air as design variables. Each of the performance parameters is considered to be an individual objective function and all objectives are then simultaneously optimized for maximizing the performance of the cooling tower using elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II). The multi-objective optimization algorithm gives a set of possible combinations of design variables, which is referred as the optimal Pareto-front, out of which a unique combination is selected based upon a decision making criterion. The proposed decision making procedure evaluates a Decision Making Score (DMS) based on assigned performance priorities for each point of the Pareto-front. Depending on DMS a unique combination of design variables is then selected for each type of fill that maximizes the tower’s performance. These optimal points and the corresponding objective function are finally compared and based upon the highest DMS value, the wire-mesh (trickle) fill is found to be the most efficient fill under the present experimental conditions. The methodology presented in this work has been made more generalized, so that it

Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

Directory of Open Access Journals (Sweden)

Yasuaki Miyazato

2016-12-01

Full Text Available A smart house generally has a Photovoltaic panel (PV, a Heat Pump (HP, a Solar Collector (SC and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions.

Constrained multi-objective optimization of radial expanders in organic Rankine cycles by firefly algorithm

International Nuclear Information System (INIS)

Bahadormanesh, Nikrouz; Rahat, Shayan; Yarali, Milad

2017-01-01

Highlights: • A multi-objective optimization for radial expander in Organic Rankine Cycles is implemented. • By using firefly algorithm, Pareto front based on the size of turbine and thermal efficiency is produced. • Tension and vibration constrains have a significant effect on optimum design points. - Abstract: Organic Rankine Cycles are viable energy conversion systems in sustainable energy systems due to their compatibility with low-temperature heat sources. In the present study, one dimensional model of radial expanders in conjunction with a thermodynamic model of organic Rankine cycles is prepared. After verification, by defining thermal efficiency of the cycle and size parameter of a radial turbine as the objective functions, a multi-objective optimization was conducted regarding tension and vibration constraints for 4 different organic working fluids (R22, R245fa, R236fa and N-Pentane). In addition to mass flow rate, evaporator temperature, maximum pressure of cycle and turbo-machinery design parameters are selected as the decision variables. Regarding Pareto fronts, by a little increase in size of radial expanders, it is feasible to reach high efficiency. Moreover, by assessing the distribution of decision variables, the variables that play a major role in trending between the objective functions are found. Effects of mechanical and vibration constrains on optimum decision variables are investigated. The results of optimization can be considered as an initial values for design of radial turbines for Organic Rankine Cycles.

Blue-noise remeshing with farthest point optimization

KAUST Repository

Yan, Dongming

2014-08-01

In this paper, we present a novel method for surface sampling and remeshing with good blue-noise properties. Our approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality blue-noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive sampling and sampling on surfaces. A simple and efficient algorithm for accelerating the FPO framework is also proposed. Experimental results show that the generalized FPO generates point sets with excellent blue-noise properties for adaptive and surface sampling. Furthermore, we demonstrate that our remeshing quality is superior to the current state-of-the art approaches. © 2014 The Eurographics Association and John Wiley & Sons Ltd.

Blue-noise remeshing with farthest point optimization

KAUST Repository

Yan, Dongming; Guo, Jianwei; Jia, Xiaohong; Zhang, Xiaopeng; Wonka, Peter

2014-01-01

In this paper, we present a novel method for surface sampling and remeshing with good blue-noise properties. Our approach is based on the farthest point optimization (FPO), a relaxation technique that generates high quality blue-noise point sets in 2D. We propose two important generalizations of the original FPO framework: adaptive sampling and sampling on surfaces. A simple and efficient algorithm for accelerating the FPO framework is also proposed. Experimental results show that the generalized FPO generates point sets with excellent blue-noise properties for adaptive and surface sampling. Furthermore, we demonstrate that our remeshing quality is superior to the current state-of-the art approaches. © 2014 The Eurographics Association and John Wiley & Sons Ltd.

Computing the Distribution of Pareto Sums Using Laplace Transformation and Stehfest Inversion

Science.gov (United States)

Harris, C. K.; Bourne, S. J.

2017-05-01

In statistical seismology, the properties of distributions of total seismic moment are important for constraining seismological models, such as the strain partitioning model (Bourne et al. J Geophys Res Solid Earth 119(12): 8991-9015, 2014). This work was motivated by the need to develop appropriate seismological models for the Groningen gas field in the northeastern Netherlands, in order to address the issue of production-induced seismicity. The total seismic moment is the sum of the moments of individual seismic events, which in common with many other natural processes, are governed by Pareto or "power law" distributions. The maximum possible moment for an induced seismic event can be constrained by geomechanical considerations, but rather poorly, and for Groningen it cannot be reliably inferred from the frequency distribution of moment magnitude pertaining to the catalogue of observed events. In such cases it is usual to work with the simplest form of the Pareto distribution without an upper bound, and we follow the same approach here. In the case of seismicity, the exponent β appearing in the power-law relation is small enough for the variance of the unbounded Pareto distribution to be infinite, which renders standard statistical methods concerning sums of statistical variables, based on the central limit theorem, inapplicable. Determinations of the properties of sums of moderate to large numbers of Pareto-distributed variables with infinite variance have traditionally been addressed using intensive Monte Carlo simulations. This paper presents a novel method for accurate determination of the properties of such sums that is accurate, fast and easily implemented, and is applicable to Pareto-distributed variables for which the power-law exponent β lies within the interval [0, 1]. It is based on shifting the original variables so that a non-zero density is obtained exclusively for non-negative values of the parameter and is identically zero elsewhere, a property

Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems

International Nuclear Information System (INIS)

Chen, Gonggui; Liu, Lilan; Song, Peizhu; Du, Yangwei

2014-01-01

Highlights: • New method for MOORPD problem using MOCIPSO and MOIPSO approaches. • Constrain-prior Pareto-dominance method is proposed to meet the constraints. • The limits of the apparent power flow of transmission line are considered. • MOORPD model is built up for MOORPD problem. • The achieved results by MOCIPSO and MOIPSO approaches are better than MOPSO method. - Abstract: Multi-objective optimal reactive power dispatch (MOORPD) seeks to not only minimize power losses, but also improve the stability of power system simultaneously. In this paper, the static voltage stability enhancement is achieved through incorporating L index in MOORPD problem. Chaotic improved PSO-based multi-objective optimization (MOCIPSO) and improved PSO-based multi-objective optimization (MOIPSO) approaches are proposed for solving complex multi-objective, mixed integer nonlinear problems such as minimization of power losses and L index in power systems simultaneously. In MOCIPSO and MOIPSO based optimization approaches, crossover operator is proposed to enhance PSO diversity and improve their global searching capability, and for MOCIPSO based optimization approach, chaotic sequences based on logistic map instead of random sequences is introduced to PSO for enhancing exploitation capability. In the two approaches, constrain-prior Pareto-dominance method (CPM) is proposed to meet the inequality constraints on state variables, the sorting and crowding distance methods are considered to maintain a well distributed Pareto optimal solutions, and moreover, fuzzy set theory is employed to extract the best compromise solution over the Pareto optimal curve. The proposed approaches have been examined and tested in the IEEE 30 bus and the IEEE 57 bus power systems. The performances of MOCIPSO, MOIPSO, and multi-objective PSO (MOPSO) approaches are compared with respect to multi-objective performance measures. The simulation results are promising and confirm the ability of MOCIPSO and

Multi-objective optimization of the carbon dioxide transcritical power cycle with various configurations for engine waste heat recovery

International Nuclear Information System (INIS)

Tian, Hua; Chang, Liwen; Shu, Gequn; Shi, Lingfeng

2017-01-01

Highlights: • A systematic optimization methodology is presented for carbon dioxide power cycle. • Adding the regenerator is a significant means to improve the system performance. • A decision making based on the optimization results is conducted in depth. • Specific optimal solutions are selected from Pareto fronts for different demands. - Abstract: In this paper, a systematic multi-objective optimization methodology is presented for the carbon dioxide transcritical power cycle with various configurations used in engine waste heat recovery to generate more power efficiently and economically. The parametric optimization is performed for the maximum net power output and exergy efficiency, as well as the minimum electricity production cost by using the genetic algorithm. The comparison of the optimization results shows the thermodynamic performance can be most enhanced by simultaneously adding the preheater and regenerator based on the basic configuration, and the highest net power output and exergy efficiency are 25.89 kW and 40.95%, respectively. Meanwhile, the best economic performance corresponding to the lowest electricity production cost of 0.560$/kW·h is achieved with simply applying an additional regenerator. Moreover, a thorough decision making is conducted for a further screening of the obtained optimal solutions. A most preferred Pareto optimal solution or a representative subset of the Pareto optimal solutions is obtained according to additional subjective preferences while a referential optimal solution is also provided on the condition of no additional preference.

Sequential Change-Point Detection via Online Convex Optimization

Directory of Open Access Journals (Sweden)

Yang Cao

2018-02-01

Full Text Available Sequential change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. When the post-change parameters are unknown, we consider a set of detection procedures based on sequential likelihood ratios with non-anticipating estimators constructed using online convex optimization algorithms such as online mirror descent, which provides a more versatile approach to tackling complex situations where recursive maximum likelihood estimators cannot be found. When the underlying distributions belong to a exponential family and the estimators satisfy the logarithm regret property, we show that this approach is nearly second-order asymptotically optimal. This means that the upper bound for the false alarm rate of the algorithm (measured by the average-run-length meets the lower bound asymptotically up to a log-log factor when the threshold tends to infinity. Our proof is achieved by making a connection between sequential change-point and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical and real data examples validate our theory.

Application of the Pareto chart and Ishikawa diagram for the identification of major defects in metal composite castings

OpenAIRE

K. Gawdzińska

2011-01-01

This author discusses the use of selected quality management tools, i.e. the Pareto chart and Ishikawa fishbone diagram, for the descriptionof composite casting defects. The Pareto chart allows to determine defect priority related with metallic composite castings, while theIshikawa diagram indicates the causes of defect formation and enables calculating defect weights.

Optimal Retention Level for Infinite Time Horizons under MADM

Directory of Open Access Journals (Sweden)

Başak Bulut Karageyik

2016-12-01

Full Text Available In this paper, we approximate the aggregate claims process by using the translated gamma process under the classical risk model assumptions, and we investigate the ultimate ruin probability. We consider optimal reinsurance under the minimum ultimate ruin probability, as well as the maximum benefit criteria: released capital, expected profit and exponential-fractional-logarithmic utility from the insurer’s point of view. Numerical examples are presented to explain how the optimal initial surplus and retention level are changed according to the individual claim amounts, loading factors and weights of the criteria. In the decision making process, we use The Analytical Hierarchy Process (AHP and The Technique for Order of Preference by Similarity to ideal Solution (TOPSIS methods as the Multi-Attribute Decision Making methods (MADM and compare our results considering different combinations of loading factors for both exponential and Pareto individual claims.

Multiobjective Optimization for Electronic Circuit Design in Time and Frequency Domains

Directory of Open Access Journals (Sweden)

J. Dobes

2013-04-01

Full Text Available The multiobjective optimization provides an extraordinary opportunity for the finest design of electronic circuits because it allows to mathematically balance contradictory requirements together with possible constraints. In this paper, an original and substantial improvement of an existing method for the multiobjective optimization known as GAM (Goal Attainment Method is suggested. In our proposal, the GAM algorithm itself is combined with a procedure that automatically provides a set of parameters -- weights, coordinates of the reference point -- for which the method generates noninferior solutions uniformly spread over an appropriately selected part of the Pareto front. Moreover, the resulting set of obtained solutions is then presented in a suitable graphic form so that the solution representing the most satisfactory tradeoff can be easily chosen by the designer. Our system generates various types of plots that conveniently characterize results of up to four-dimensional problems. Technically, the procedures of the multiobjective optimization were created as a software add-on to the CIA (Circuit Interactive Analyzer program. This way enabled us to utilize many powerful features of this program, including the sensitivity analyses in time and frequency domains. As a result, the system is also able to perform the multiobjective optimization in the time domain and even highly nonlinear circuits can be significantly improved by our program. As a demonstration of this feature, a multiobjective optimization of a C-class power amplifier in the time domain is thoroughly described in the paper. Further, a four-dimensional optimization of a video amplifier is demonstrated with an original graphic representation of the Pareto front, and also some comparison with the weighting method is done. As an example of improving noise properties, a multiobjective optimization of a low-noise amplifier is performed, and the results in the frequency domain are shown

Linear Energy Transfer-Guided Optimization in Intensity Modulated Proton Therapy: Feasibility Study and Clinical Potential

Energy Technology Data Exchange (ETDEWEB)

Giantsoudi, Drosoula, E-mail: dgiantsoudi@partners.org [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts (United States); Grassberger, Clemens; Craft, David; Niemierko, Andrzej; Trofimov, Alexei; Paganetti, Harald [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts (United States)

2013-09-01

Purpose: To investigate the feasibility and potential clinical benefit of linear energy transfer (LET) guided plan optimization in intensity modulated proton therapy (IMPT). Methods and Materials: A multicriteria optimization (MCO) module was used to generate a series of Pareto-optimal IMPT base plans (BPs), corresponding to defined objectives, for 5 patients with head-and-neck cancer and 2 with pancreatic cancer. A Monte Carlo platform was used to calculate dose and LET distributions for each BP. A custom-designed MCO navigation module allowed the user to interpolate between BPs to produce deliverable Pareto-optimal solutions. Differences among the BPs were evaluated for each patient, based on dose–volume and LET–volume histograms and 3-dimensional distributions. An LET-based relative biological effectiveness (RBE) model was used to evaluate the potential clinical benefit when navigating the space of Pareto-optimal BPs. Results: The mean LET values for the target varied up to 30% among the BPs for the head-and-neck patients and up to 14% for the pancreatic cancer patients. Variations were more prominent in organs at risk (OARs), where mean LET values differed by a factor of up to 2 among the BPs for the same patient. An inverse relation between dose and LET distributions for the OARs was typically observed. Accounting for LET-dependent variable RBE values, a potential improvement on RBE-weighted dose of up to 40%, averaged over several structures under study, was noticed during MCO navigation. Conclusions: We present a novel strategy for optimizing proton therapy to maximize dose-averaged LET in tumor targets while simultaneously minimizing dose-averaged LET in normal tissue structures. MCO BPs show substantial LET variations, leading to potentially significant differences in RBE-weighted doses. Pareto-surface navigation, using both dose and LET distributions for guidance, provides the means for evaluating a large variety of deliverable plans and aids in

Linear Energy Transfer-Guided Optimization in Intensity Modulated Proton Therapy: Feasibility Study and Clinical Potential

International Nuclear Information System (INIS)

Giantsoudi, Drosoula; Grassberger, Clemens; Craft, David; Niemierko, Andrzej; Trofimov, Alexei; Paganetti, Harald

2013-01-01

Purpose: To investigate the feasibility and potential clinical benefit of linear energy transfer (LET) guided plan optimization in intensity modulated proton therapy (IMPT). Methods and Materials: A multicriteria optimization (MCO) module was used to generate a series of Pareto-optimal IMPT base plans (BPs), corresponding to defined objectives, for 5 patients with head-and-neck cancer and 2 with pancreatic cancer. A Monte Carlo platform was used to calculate dose and LET distributions for each BP. A custom-designed MCO navigation module allowed the user to interpolate between BPs to produce deliverable Pareto-optimal solutions. Differences among the BPs were evaluated for each patient, based on dose–volume and LET–volume histograms and 3-dimensional distributions. An LET-based relative biological effectiveness (RBE) model was used to evaluate the potential clinical benefit when navigating the space of Pareto-optimal BPs. Results: The mean LET values for the target varied up to 30% among the BPs for the head-and-neck patients and up to 14% for the pancreatic cancer patients. Variations were more prominent in organs at risk (OARs), where mean LET values differed by a factor of up to 2 among the BPs for the same patient. An inverse relation between dose and LET distributions for the OARs was typically observed. Accounting for LET-dependent variable RBE values, a potential improvement on RBE-weighted dose of up to 40%, averaged over several structures under study, was noticed during MCO navigation. Conclusions: We present a novel strategy for optimizing proton therapy to maximize dose-averaged LET in tumor targets while simultaneously minimizing dose-averaged LET in normal tissue structures. MCO BPs show substantial LET variations, leading to potentially significant differences in RBE-weighted doses. Pareto-surface navigation, using both dose and LET distributions for guidance, provides the means for evaluating a large variety of deliverable plans and aids in

La narrazione dell’azione sociale: spunti dal Trattato di Vilfredo Pareto

Directory of Open Access Journals (Sweden)

Ilaria Riccioni

2017-08-01

Full Text Available La rilettura dei classici porta con sé sempre una duplice operazione: da una parte un ritorno a riflessioni, ritmi, storicità che spesso sembrano già superate; dall’altra la riscoperta delle origini di fenomeni contemporanei da punti di vista che ne delineano le interconnessioni profonde, non più visibili allo stato di avanzamento in cui le osserviamo oggi. Tale maggiore chiarezza è forse dovuta al fatto che ogni fenomeno nella sua fase aurorale è più chiaramente identificabile rispetto alle sue fasi successive, dove le caratteristiche primarie tendono a stemperarsi nelle cifre dominanti della contemporaneità, perdendosi nelle pratiche quotidiane che ne celano la provenienza. Se la sociologia è un processo di conoscenza della realtà dei fenomeni, il punto centrale della scienza sociale va distinto tra quelle scienze che schematizzano il reale in equazioni formali funzionali e funzionanti, il sistema economico, normativo, e le scienze sociali che si occupano della realtà e della sua complessità, che in quanto scienze si devono occupare non tanto di ciò che la realtà deve essere, bensì di ciò che la realtà è, di come si pone e di come manifesta i movimenti desideranti e profondi del vivere collettivo oltre il sistema che ne gestisce il funzionamento. Il punto che Pareto sembra scorgere, con estrema lucidità, è la necessità di ribaltare l’importanza della logica economica nell’organizzazione sociale da scienza che detta la realtà a scienza che propone uno schema di gestione di essa: da essa si cerca di dettare la realtà, ma l’economia, dal greco moderno Oikòs, Oikòsgeneia (casa e generazione, il termine utilizzato per definire l’unità famigliare non è di fatto “la realtà”, sembra dirci Pareto in più digressioni, bensì l’arte e la scienza della gestione di unità familiari e produttive. La realtà rimane in ombra e non può che essere “avvicinata” da una scienza che ne registri, ed eventualmente

El Principio de Pareto en el control documental de programas informativos televisivos: implicaciones en el Media Asset Management

Directory of Open Access Journals (Sweden)

Jorge Caldera-Serrano

2015-09-01

Full Text Available Se analiza la reutilización de las colecciones audiovisuales de las cadenas de televisión con el fin de detectar si se cumple el Índice de Pareto, facilitando mecanismos para su control y explotación de la parte de la colección audiovisual menos utilizada. Se detecta que la correlación de Pareto se establece no sólo en el uso sino también en la presencia de elementos temáticos y elementos onomásticos en el archivo y en la difusión de contenidos, por lo que se plantea formas de control en la integración de información en la colección y de recursos en la difusión. Igualmente se describe el Índice de Pareto, los Media Asset Management y el cambio de paradigma al digital, elementos fundamentales para entender los problemas y las soluciones para la eliminación de problemas en la recuperación y en la conformación de la colección. Abstract: Reuse of audiovisual collections television networks in order to detect whether the Pareto index, providing mechanisms for control and exploitation of the least used part of the audiovisual collection holds analyzed. It is found that the correlation of Pareto is established not only in the use but also the presence of thematic elements and onomastic elements in the file and in the distribution of content, so forms of control arises in the integration of information collection and distributing resources. Likewise, the Pareto index, the Media Asset Management and the paradigm shift to digital, essential to understanding the problems and solutions to eliminate problems in recovery and in the establishment of collection elements described. Keywords:  Information processing. Television. Electronic media. Information systems evaluation.

Multiobjective genetic algorithm conjunctive use optimization for production, cost, and energy with dynamic return flow

Science.gov (United States)

Peralta, Richard C.; Forghani, Ali; Fayad, Hala

2014-04-01

Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.

Retrofitting of heat exchanger networks involving streams with variable heat capacity: Application of single and multi-objective optimization

International Nuclear Information System (INIS)

Sreepathi, Bhargava Krishna; Rangaiah, G.P.

2015-01-01

Heat exchanger network (HEN) retrofitting improves the energy efficiency of the current process by reducing external utilities. In this work, HEN retrofitting involving streams having variable heat capacity is studied. For this, enthalpy values of a stream are fitted to a continuous cubic polynomial instead of a stepwise approach employed in the previous studies [1,2]. The former methodology is closer to reality as enthalpy or heat capacity changes gradually instead of step changes. Using the polynomial fitting formulation, single objective optimization (SOO) and multi-objective optimization (MOO) of a HEN retrofit problem are investigated. The results obtained show an improvement in the utility savings, and MOO provides many Pareto-optimal solutions to choose from. Also, Pareto-optimal solutions involving area addition in existing heat exchangers only (but no new exchangers and no structural modifications) are found and provided for comparison with those involving new exchangers and structural modifications as well. - Highlights: • HEN retrofitting involving streams with variable heat capacities is studied. • A continuous approach to handle variable heat capacity is proposed and tested. • Better and practical solutions are obtained for HEN retrofitting in process plants. • Pareto-optimal solutions provide many alternate choices for HEN retrofitting

Application of the Pareto chart and Ishikawa diagram for the identification of major defects in metal composite castings

Directory of Open Access Journals (Sweden)

K. Gawdzińska

2011-04-01

Full Text Available This author discusses the use of selected quality management tools, i.e. the Pareto chart and Ishikawa fishbone diagram, for the descriptionof composite casting defects. The Pareto chart allows to determine defect priority related with metallic composite castings, while theIshikawa diagram indicates the causes of defect formation and enables calculating defect weights.

Model-based problem solving through symbolic regression via pareto genetic programming

NARCIS (Netherlands)

Vladislavleva, E.

2008-01-01

Pareto genetic programming methodology is extended by additional generic model selection and generation strategies that (1) drive the modeling engine to creation of models of reduced non-linearity and increased generalization capabilities, and (2) improve the effectiveness of the search for robust

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

Science.gov (United States)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Global stability-based design optimization of truss structures using ...

Indian Academy of Sciences (India)

The quality of current pareto front obtained in the end of a whole genetic search is assessed according to its closeness to the ...... better optimal designation with a lower displacement value of 0.3075 in. satisfying the service- .... Internal force. R.

Mechanical properties of two-way grid shells optimized considering roundness and elastic stiffness

International Nuclear Information System (INIS)

Ogawa, Toshiyuki; Yuta, Nishikawa; Rie, Tateishi; Ohsaki, Makoto

2002-01-01

A single-layer two-way grid shell defined by Bezier surface is optimized by coordinates of the control points as design variables. The purpose of this paper is to find optimal shapes considering roundness and elastic stiffness, and to investigate their mechanical properties. The distance of the center of curvature from the specified point is used for formulating the objective function for generating a round shape. Consider next a problem of minimizing the compliance as mechanical performance measure. The compliance is defined by the external work against the static loads applied to the nodes. The mechanically optimal shape is different from the round shape. Therefore, the multi objective optimization problem is formulated for optimizing the two objectives, which are roundness and the elastic stiffness defined by using the compliance. The constraint method is used for obtaining Pareto optimal solutions between the two objectives. We optimize single-layer two-way grid shells with square and rectangle plans. Mechanical properties of the optimal shapes are investigated by compliance and the distributions of axial force and bending moment. The round shape is significantly dominated by the bending moment and its compliance is large. The bending moment of the mechanically optimal shape is not very large, and the latticed shell has large stiffness through axial deformation. A trade-off shape is round enough, and the influence of the bending moment is smaller than that of the optimal round shape and the elastic stiffness is moderately large

Optimal colour quality of LED clusters based on memory colours.

Science.gov (United States)

Smet, Kevin; Ryckaert, Wouter R; Pointer, Michael R; Deconinck, Geert; Hanselaer, Peter

2011-03-28

The spectral power distributions of tri- and tetrachromatic clusters of Light-Emitting-Diodes, composed of simulated and commercially available LEDs, were optimized with a genetic algorithm to maximize the luminous efficacy of radiation and the colour quality as assessed by the memory colour quality metric developed by the authors. The trade-off of the colour quality as assessed by the memory colour metric and the luminous efficacy of radiation was investigated by calculating the Pareto optimal front using the NSGA-II genetic algorithm. Optimal peak wavelengths and spectral widths of the LEDs were derived, and over half of them were found to be close to Thornton's prime colours. The Pareto optimal fronts of real LED clusters were always found to be smaller than those of the simulated clusters. The effect of binning on designing a real LED cluster was investigated and was found to be quite large. Finally, a real LED cluster of commercially available AlGaInP, InGaN and phosphor white LEDs was optimized to obtain a higher score on memory colour quality scale than its corresponding CIE reference illuminant.

Optimal Decisions in a Single-Period Supply Chain with Price-Sensitive Random Demand under a Buy-Back Contract

Directory of Open Access Journals (Sweden)

Feng Wang

2014-01-01

Full Text Available This paper studies a single-period supply chain with a buy-back contract under a Stackelberg game model, in which the supplier (leader decides on the wholesale price, and the retailer (follower responds to determine the retail price and the order quantity. We analytically investigate the decentralized retailer’s optimal decision. Our results demonstrate that the retailer has a unique optimal simultaneous decision on the retail price and the order quantity, under a mild restriction on the demand distribution. Moreover, as it can be shown that the decentralized supply chain facing price-sensitive random demand cannot be coordinated with buy-back contract, we propose a scheme for the system to achieve Pareto-improvement. Theoretical analysis suggests that there exists a unique Pareto-equilibrium for the supply chain. In particular, when the Pareto-equilibrium is reached, the supply chain is coordinated. Numerical experiments confirm our results.

The Pareto Analysis for Establishing Content Criteria in Surgical Training.

Science.gov (United States)

Kramp, Kelvin H; van Det, Marc J; Veeger, Nic J G M; Pierie, Jean-Pierre E N

2016-01-01

Current surgical training is still highly dependent on expensive operating room (OR) experience. Although there have been many attempts to transfer more training to the skills laboratory, little research is focused on which technical behaviors can lead to the highest profit when they are trained outside the OR. The Pareto principle states that in any population that contributes to a common effect, a few account for the bulk of the effect. This principle has been widely used in business management to increase company profits. This study uses the Pareto principle for establishing content criteria for more efficient surgical training. A retrospective study was conducted to assess verbal guidance provided by 9 supervising surgeons to 12 trainees performing 64 laparoscopic cholecystectomies in the OR. The verbal corrections were documented, tallied, and clustered according to the aimed change in novice behavior. The corrections were rank ordered, and a cumulative distribution curve was used to calculate which corrections accounted for 80% of the total number of verbal corrections. In total, 253 different verbal corrections were uttered 1587 times and were categorized into 40 different clusters of aimed changes in novice behaviors. The 35 highest-ranking verbal corrections (14%) and the 11 highest-ranking clusters (28%) accounted for 80% of the total number of given verbal corrections. Following the Pareto principle, we were able to identify the aspects of trainee behavior that account for most corrections given by supervisors during a laparoscopic cholecystectomy on humans. This strategy can be used for the development of new training programs to prepare the trainee in advance for the challenges encountered in the clinical setting in an OR. Copyright © 2016 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Classification as clustering: a Pareto cooperative-competitive GP approach.

Science.gov (United States)

McIntyre, Andrew R; Heywood, Malcolm I

2011-01-01

Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.

Optimal External-Memory Planar Point Enclosure

DEFF Research Database (Denmark)

Arge, Lars; Samoladas, Vasilis; Yi, Ke

2007-01-01

.g. spatial and temporal databases, and is dual to the important and well-studied orthogonal range searching problem. Surprisingly, despite the fact that the problem can be solved optimally in internal memory with linear space and O(log N+K) query time, we show that one cannot construct a linear sized......In this paper we study the external memory planar point enclosure problem: Given N axis-parallel rectangles in the plane, construct a data structure on disk (an index) such that all K rectangles containing a query point can be reported I/O-efficiently. This problem has important applications in e...... external memory point enclosure data structure that can be used to answer a query in O(log  B N+K/B) I/Os, where B is the disk block size. To obtain this bound, Ω(N/B 1−ε ) disk blocks are needed for some constant ε>0. With linear space, the best obtainable query bound is O(log 2 N+K/B) if a linear output...

Wealth of the world's richest publicly traded companies per industry and per employee: Gamma, Log-normal and Pareto power-law as universal distributions?

Science.gov (United States)

Soriano-Hernández, P.; del Castillo-Mussot, M.; Campirán-Chávez, I.; Montemayor-Aldrete, J. A.

2017-04-01

Forbes Magazine published its list of leading or strongest publicly-traded two thousand companies in the world (G-2000) based on four independent metrics: sales or revenues, profits, assets and market value. Every one of these wealth metrics yields particular information on the corporate size or wealth size of each firm. The G-2000 cumulative probability wealth distribution per employee (per capita) for all four metrics exhibits a two-class structure: quasi-exponential in the lower part, and a Pareto power-law in the higher part. These two-class structure per capita distributions are qualitatively similar to income and wealth distributions in many countries of the world, but the fraction of firms per employee within the high-class Pareto is about 49% in sales per employee, and 33% after averaging on the four metrics, whereas in countries the fraction of rich agents in the Pareto zone is less than 10%. The quasi-exponential zone can be adjusted by Gamma or Log-normal distributions. On the other hand, Forbes classifies the G-2000 firms in 82 different industries or economic activities. Within each industry, the wealth distribution per employee also follows a two-class structure, but when the aggregate wealth of firms in each industry for the four metrics is divided by the total number of employees in that industry, then the 82 points of the aggregate wealth distribution by industry per employee can be well adjusted by quasi-exponential curves for the four metrics.

Small Sample Robust Testing for Normality against Pareto Tails

Czech Academy of Sciences Publication Activity Database

Stehlík, M.; Fabián, Zdeněk; Střelec, L.

2012-01-01

Roč. 41, č. 7 (2012), s. 1167-1194 ISSN 0361-0918 Grant - others:Aktion(CZ-AT) 51p7, 54p21, 50p14, 54p13 Institutional research plan: CEZ:AV0Z10300504 Keywords : consistency * Hill estimator * t-Hill estimator * location functional * Pareto tail * power comparison * returns * robust tests for normality Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.295, year: 2012

Optimal time points sampling in pathway modelling.

Science.gov (United States)

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

Multi-objective optimization design of air distribution of grate cooler by entropy generation minimization and genetic algorithm

International Nuclear Information System (INIS)

Shao, Wei; Cui, Zheng; Cheng, Lin

2016-01-01

Highlights: • A multi-objective optimization model of air distribution of grate cooler by genetic algorithm is proposed. • Pareto Front is obtained and validated by comparing with operating data. • Optimal schemes are compared and selected by engineering background. • Total power consumption after optimization decreases 61.10%. • Thickness of clinker on three grate plates is thinner. - Abstract: The cooling air distributions of grate cooler exercise a great influence on the clinker cooling efficiency and power consumption of cooling fans. A multi-objective optimization model of air distributions of grate cooler with cross-flow heat exchanger analogy is proposed in this paper. Firstly, thermodynamic and flow models of clinker cooling process is carried out. Then based on entropy generation minimization analysis, modified entropy generation numbers caused by heat transfer and pressure drop are chosen as objective functions respectively which optimized by genetic algorithm. The design variables are superficial velocities of air chambers and thicknesses of clinker layers on different grate plates. A set of Pareto optimal solutions which two objectives are optimized simultaneously is achieved. Scattered distributions of design variables resulting in the conflict between two objectives are brought out. The final optimal air distribution and thicknesses of clinker layers are selected from the Pareto optimal solutions based on power consumption of cooling fans minimization and validated by measurements. Compared with actual operating scheme, the total air volumes of optimized schemes decrease 2.4%, total power consumption of cooling fans decreases 61.1% and the outlet temperature of clinker decreases 122.9 °C which shows a remarkable energy-saving effect on energy consumption.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

Science.gov (United States)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this

Accident investigation of construction sites in Qom city using Pareto chart (2009-2012

Directory of Open Access Journals (Sweden)

M. H. Beheshti

2015-07-01

.Conclusions: Employing Pareto charts as a method for analyzing and identification of accident causes can have an effective role in the management of work-related accidents, proper allocation of funds and time.

Bi and tri-objective optimization in the deterministic network interdiction problem

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Emmanuel Ramirez-Marquez, Jose; Salazar A, Daniel E.

2010-01-01

Solution approaches to the deterministic network interdiction problem have previously been developed for optimizing a single figure-of-merit of the network configuration (i.e. flow that can be transmitted between a source node and a sink node for a fixed network design) under constraints related to limited amount of resources available to interdict network links. These approaches work under the assumption that: (1) nominal capacity of each link is completely reduced when interdicted and (2) there is a single criterion to optimize. This paper presents a newly developed evolutionary algorithm that for the first time allows solving multi-objective optimization models for the design of network interdiction strategies that take into account a variety of figures-of-merit. The algorithm provides an approximation to the optimal Pareto frontier using: (a) techniques in Monte Carlo simulation to generate potential network interdiction strategies, (b) graph theory to analyze strategies' maximum source-sink flow and (c) an evolutionary search that is driven by the probability that a link will belong to the optimal Pareto set. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate and validate the approach.

Optimization of hydrometric monitoring network in urban drainage systems using information theory.

Science.gov (United States)

Yazdi, J

2017-10-01

Regular and continuous monitoring of urban runoff in both quality and quantity aspects is of great importance for controlling and managing surface runoff. Due to the considerable costs of establishing new gauges, optimization of the monitoring network is essential. This research proposes an approach for site selection of new discharge stations in urban areas, based on entropy theory in conjunction with multi-objective optimization tools and numerical models. The modeling framework provides an optimal trade-off between the maximum possible information content and the minimum shared information among stations. This approach was applied to the main surface-water collection system in Tehran to determine new optimal monitoring points under the cost considerations. Experimental results on this drainage network show that the obtained cost-effective designs noticeably outperform the consulting engineers' proposal in terms of both information contents and shared information. The research also determined the highly frequent sites at the Pareto front which might be important for decision makers to give a priority for gauge installation on those locations of the network.

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

Directory of Open Access Journals (Sweden)

Jiuyuan Huo

2017-02-01

Full Text Available To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

Komputasi Aliran Daya Optimal Sistem Tenaga Skala Besar dengan Metode Primal Dual Interior Point

Directory of Open Access Journals (Sweden)

Syafii Syafii

2016-03-01

Full Text Available This paper focuses on the use of Primal Dual Interior Point method in the analysis of optimal power flow. Optimal power flow analysis with Primal Dual Interior Point method then compared with Linear Programming Method using Matpower program. The simulation results show that the computation results of Primal Dual Interior Point similar with Linear Programming Method for total cost of generation and large power generated by each power plant. But in terms of computation time Primal Dual Interior Point method is faster than the method of Linear Programming, especially for large systems. Primal Dual Interior Point method have solved the problem in 40.59 seconds, while Linear Programming method takes longer 239.72 seconds for large-scale system 9241 bus. This is because the settlement PDIP algorithm starts from the starting point x0, which is located within the area of feasible move towards the optimal point, in contrast to the simplex method that moves along the border of the feasible from one extreme point to the other extreme point. Thus Primal Dual Interior Point method have more efficient in solving optimal power flow problem of large-scale power systems.

On Usage of Pareto curves to Select Wind Turbine Controller Tunings to the Wind Turbulence Level

DEFF Research Database (Denmark)

Odgaard, Peter Fogh

2015-01-01

Model predictive control has in recently publications shown its potential for lowering of cost of energy of modern wind turbines. Pareto curves can be used to evaluate performance of these controllers with multiple conflicting objectives of power and fatigue loads. In this paper an approach...... to update an model predictive wind turbine controller tuning as the wind turbulence increases, as increased turbulence levels results in higher loads for the same controller tuning. In this paper the Pareto curves are computed using an industrial high fidelity aero-elastic model. Simulations show...

Many-Objective Optimization Using Adaptive Differential Evolution with a New Ranking Method

Directory of Open Access Journals (Sweden)

Xiaoguang He

2014-01-01

Full Text Available Pareto dominance is an important concept and is usually used in multiobjective evolutionary algorithms (MOEAs to determine the nondominated solutions. However, for many-objective problems, using Pareto dominance to rank the solutions even in the early generation, most obtained solutions are often the nondominated solutions, which results in a little selection pressure of MOEAs toward the optimal solutions. In this paper, a new ranking method is proposed for many-objective optimization problems to verify a relatively smaller number of representative nondominated solutions with a uniform and wide distribution and improve the selection pressure of MOEAs. After that, a many-objective differential evolution with the new ranking method (MODER for handling many-objective optimization problems is designed. At last, the experiments are conducted and the proposed algorithm is compared with several well-known algorithms. The experimental results show that the proposed algorithm can guide the search to converge to the true PF and maintain the diversity of solutions for many-objective problems.

Biobjective Optimization of Vibration Performance of Steel-Spring Floating Slab Tracks by Four-Pole Parameter Method Coupled with Ant Colony Optimization

Directory of Open Access Journals (Sweden)

Hao Jin

2015-01-01

Full Text Available Steel-spring floating slab tracks are one of the most effective methods to reduce vibrations from underground railways, which has drawn more and more attention in scientific communities. In this paper, the steel-spring floating slab track located in Track Vibration Abatement and Control Laboratory was modeled with four-pole parameter method. The influences of the fastener damping ratio, the fastener stiffness, the steel-spring damping ratio, and the steel-spring stiffness were researched for the rail displacement and the foundation acceleration. Results show that the rail displacement and the foundation acceleration will decrease with the increase of the fastener stiffness or the steel-spring damping ratio. However, the rail displacement and the foundation acceleration have the opposite variation tendency for the fastener damping ratio and the steel-spring stiffness. In order to optimize the rail displacement and the foundation acceleration affected by the fastener damping ratio and the steel-spring stiffness at the same time, a multiobjective ant colony optimization (ACO was employed. Eventually, Pareto optimal frontier of the rail displacement and the foundation acceleration was derived. Furthermore, the desirable values of the fastener damping ratio and the steel-spring stiffness can be obtained according to the corresponding Pareto optimal solution set.

Shape optimization of draft tubes for Agnew microhydro turbines

International Nuclear Information System (INIS)

Shojaeefard, Mohammad Hasan; Mirzaei, Ammar; Babaei, Ali

2014-01-01

Highlights: • The draft tube of Agnew microhydro turbine was optimized. • Pareto optimal solutions were determined by neural networks and NSGA-II algorithm. • The pressure recovery factor increases with height and angle over design ranges. • The loss coefficient reaches the minimum values at angles about 2 o . • Swirl of the incoming flow has great influence on the optimization results. - Abstract: In this study, the shape optimization of draft tubes utilized in Agnew type microhydro turbines has been discussed. The design parameters of the draft tube such as the cone angle and the height above the tailrace are considered in defining an optimization problem whose goal is to maximize the pressure recovery factor and minimize the energy loss coefficient of flow. The design space is determined by considering the experimental constraints and parameterized by the method of face-centered uniform ascertain distribution. The numerical simulations are performed using the boundary conditions found from laboratory tests and the obtained results are analyzed to create and validate a feed-forward neural network model, which is implemented as a surrogate model. The optimal Pareto solutions are finally determined using the NSGA-II evolutionary algorithm and compared for different inlet conditions. The results predict that the high swirl of the incoming flow drastically reduces the performance of the draft tube

Bilevel Optimization for Scene Segmentation of LiDAR Point Cloud

Directory of Open Access Journals (Sweden)

LI Minglei

2018-02-01

Full Text Available The segmentation of point clouds obtained by light detection and ranging (LiDAR systems is a critical step for many tasks,such as data organization,reconstruction and information extraction.In this paper,we propose a bilevel progressive optimization algorithm based on the local differentiability.First,we define the topological relation and distance metric of points in the framework of Riemannian geometry,and in the point-based level using k-means method generates over-segmentation results,e.g.super voxels.Then these voxels are formulated as nodes which consist a minimal spanning tree.High level features are extracted from voxel structures,and a graph-based optimization method is designed to yield the final adaptive segmentation results.The implementation experiments on real data demonstrate that our method is efficient and superior to state-of-the-art methods.

Point charges optimally placed to represent the multipole expansion of charge distributions.

Directory of Open Access Journals (Sweden)

Ramu Anandakrishnan

Full Text Available We propose an approach for approximating electrostatic charge distributions with a small number of point charges to optimally represent the original charge distribution. By construction, the proposed optimal point charge approximation (OPCA retains many of the useful properties of point multipole expansion, including the same far-field asymptotic behavior of the approximate potential. A general framework for numerically computing OPCA, for any given number of approximating charges, is described. We then derive a 2-charge practical point charge approximation, PPCA, which approximates the 2-charge OPCA via closed form analytical expressions, and test the PPCA on a set of charge distributions relevant to biomolecular modeling. We measure the accuracy of the new approximations as the RMS error in the electrostatic potential relative to that produced by the original charge distribution, at a distance 2x the extent of the charge distribution--the mid-field. The error for the 2-charge PPCA is found to be on average 23% smaller than that of optimally placed point dipole approximation, and comparable to that of the point quadrupole approximation. The standard deviation in RMS error for the 2-charge PPCA is 53% lower than that of the optimal point dipole approximation, and comparable to that of the point quadrupole approximation. We also calculate the 3-charge OPCA for representing the gas phase quantum mechanical charge distribution of a water molecule. The electrostatic potential calculated by the 3-charge OPCA for water, in the mid-field (2.8 Å from the oxygen atom, is on average 33.3% more accurate than the potential due to the point multipole expansion up to the octupole order. Compared to a 3 point charge approximation in which the charges are placed on the atom centers, the 3-charge OPCA is seven times more accurate, by RMS error. The maximum error at the oxygen-Na distance (2.23 Å is half that of the point multipole expansion up to the octupole

Photovoltaic System with Smart Tracking of the Optimal Working Point

Directory of Open Access Journals (Sweden)

PATARAU, T.

2010-08-01

Full Text Available A photovoltaic (PV system, based on a Maximum Power Point Tracking (MPPT controller that extracts the maximum possible output power from the solar panel is described. Output efficiency of a PV energy system can be achieved only if the system working point is brought near the maximum power point (MPP. The proposed system, making use of several MPPT control algorithms (Perturb and Observe, Incremental conductance, Fuzzy Logic, demonstrates in simulations as well as in real experiments good tracking of the optimal working point.

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Directory of Open Access Journals (Sweden)

Jinjiao Hou

2018-05-01

Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

Application of the multicriterion optimization techniques and hierarchy of computational models to the research of ion acceleration due to laser-plasma interaction

Science.gov (United States)

Inovenkov, I. N.; Echkina, E. Yu.; Nefedov, V. V.; Ponomarenko, L. S.

2017-12-01

In this paper we discuss how a particles-in-cell computation code can be combined with methods of multicriterion optimization (in particular the Pareto optimal solutions of the multicriterion optimization problem) and a hierarchy of computational models approach to create an efficient tool for solving a wide array of problems related to the laser-plasma interaction. In case of the computational experiment the multicriterion optimization can be applied as follows: the researcher defines the objectives of the experiment - some computable scalar values (i.e. high kinetic energy of the ions leaving the domain, least possible number of electrons leaving domain in the given direction, etc). After that the parameters of the experiment which can be varied to achieve these objectives and the constrains on these parameters are chosen (e.g. amplitude and wave-length of the laser radiation, dimensions of the plasma slab(s)). The Pareto optimality of the vector of the parameters can be seen as this: x 0 is Pareto optimal if there exists no vector which would improve some criterion without causing a simultaneous degradation in at least one other criterion. These efficient set of parameter and constrains can be selected based on the preliminary calculations in the simplified models (one or two-dimensional) either analytical or numerical. The multistage computation of the Pareto set radically reduces the number of variants which are to be evaluated to achieve the given accuracy. During the final stage we further improve the results by recomputing some of the optimal variants on the finer grids, with more particles and/or in the frame of a more detailed model. As an example we have considered the ion acceleration caused by interaction of very intense and ultra-short laser pulses with plasmas and have calculated the optimal set of experiment parameters for optimizing number and average energy of high energy ions leaving the domain in the given direction and minimizing the expulsion

Multicriteria VMAT optimization

International Nuclear Information System (INIS)

Craft, David; McQuaid, Dualta; Wala, Jeremiah; Chen, Wei; Salari, Ehsan; Bortfeld, Thomas

2012-01-01

Purpose: To make the planning of volumetric modulated arc therapy (VMAT) faster and to explore the tradeoffs between planning objectives and delivery efficiency. Methods: A convex multicriteria dose optimization problem is solved for an angular grid of 180 equi-spaced beams. This allows the planner to navigate the ideal dose distribution Pareto surface and select a plan of desired target coverage versus organ at risk sparing. The selected plan is then made VMAT deliverable by a fluence map merging and sequencing algorithm, which combines neighboring fluence maps based on a similarity score and then delivers the merged maps together, simplifying delivery. Successive merges are made as long as the dose distribution quality is maintained. The complete algorithm is called VMERGE. Results: VMERGE is applied to three cases: a prostate, a pancreas, and a brain. In each case, the selected Pareto-optimal plan is matched almost exactly with the VMAT merging routine, resulting in a high quality plan delivered with a single arc in less than 5 min on average. Conclusions: VMERGE offers significant improvements over existing VMAT algorithms. The first is the multicriteria planning aspect, which greatly speeds up planning time and allows the user to select the plan, which represents the most desirable compromise between target coverage and organ at risk sparing. The second is the user-chosen epsilon-optimality guarantee of the final VMAT plan. Finally, the user can explore the tradeoff between delivery time and plan quality, which is a fundamental aspect of VMAT that cannot be easily investigated with current commercial planning systems.

Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Amlashi, Emad Hadaddi; Amidpour, Majid

2009-01-01

Thermodynamic and thermoeconomic optimization of a vertical ground source heat pump system has been studied. A model based on the energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. The proposed vertical ground source heat pump system including eight decision variables is considered for optimization. An artificial intelligence technique known as evolutionary algorithm (EA) has been utilized as an optimization method. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective, thermoeconomic single objective and multi-objective optimizations are performed. In Multi-objective optimization, both thermodynamic and thermoeconomic objectives are considered, simultaneously. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from available optimal points on Pareto frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, to the annual number of operating hours and to the electricity cost are studied in detail.

A guide to multi-objective optimization for ecological problems with an application to cackling goose management

Science.gov (United States)

Williams, Perry J.; Kendall, William L.

2017-01-01

Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem.A large class of methods for solving MOO problems can be separated into two strategies: modelling preferences pre-optimization (the a priori strategy), or modelling preferences post-optimization (the a posteriori strategy). The a priori strategy requires describing preferences among objectives without knowledge of how preferences affect the resulting decision. In the a posteriori strategy, the decision maker simultaneously considers a set of solutions (the Pareto optimal set) and makes a choice based on the trade-offs observed in the set. We describe several methods for modelling preferences pre-optimization, including: the bounded objective function method, the lexicographic method, and the weighted-sum method. We discuss modelling preferences post-optimization through examination of the Pareto optimal set. We applied each MOO strategy to the natural resource management problem of selecting a population target for cackling goose (Branta hutchinsii minima) abundance. Cackling geese provide food security to Native Alaskan subsistence hunters in the goose's nesting area, but depredate crops on private agricultural fields in wintering areas. We developed objective functions to represent the competing objectives related to the cackling goose population target and identified an optimal solution

Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Mehrabipour, Reza

2012-01-01

A simple gas turbine cycle namely as the Kraftwerk Union AG unit including a Siemens gas turbine model V93.1 with 60 MW nominal power and 26.0% thermal efficiency utilized in the Fars power plant located is considered for the efficiency enhancement. A typical tubular vertical recuperative heat exchanger is designed in order to integrate into the cycle as an air pre-heater for thermal efficiency improvement. Thermal and geometric specifications of the recuperative heat exchanger are obtained in a multi-objective optimization process. The exergetic efficiency of the gas cycle is maximized while the payback time for the capital investment of the recuperator is minimized. Combination of these objectives and decision variables with suitable engineering and physical constraints makes a set of the MINLP optimization problem. Optimization programming is performed using the NSGA-II algorithm and Pareto optimal frontiers are obtained in three cases including the minimum, average and maximum ambient air temperatures. In each case, the final optimal solution has been selected using three decision-making approaches including the fuzzy Bellman-Zadeh, LINMAP and TOPSIS methods. It has been shown that the TOPSIS and LINMAP decision-makers when applied on the Pareto frontier which is obtained at average ambient air temperature yields best results in comparison to other cases. -- Highlights: ► A simple Brayton gas cycle is considered for the efficiency improvement by integrating of a recuperator. ► Objective functions based on thermodynamic and economic analysis are obtained. ► The payback time for the capital investment is minimized and the exergetic efficiency of the system is maximized. ► Pareto optimal frontiers at various site conditions are obtained. ► A final optimal configuration is found using various decision-making approaches.

A primal-dual interior point method for large-scale free material optimization

DEFF Research Database (Denmark)

Weldeyesus, Alemseged Gebrehiwot; Stolpe, Mathias

2015-01-01

Free Material Optimization (FMO) is a branch of structural optimization in which the design variable is the elastic material tensor that is allowed to vary over the design domain. The requirements are that the material tensor is symmetric positive semidefinite with bounded trace. The resulting...... optimization problem is a nonlinear semidefinite program with many small matrix inequalities for which a special-purpose optimization method should be developed. The objective of this article is to propose an efficient primal-dual interior point method for FMO that can robustly and accurately solve large...... of iterations the interior point method requires is modest and increases only marginally with problem size. The computed optimal solutions obtain a higher precision than other available special-purpose methods for FMO. The efficiency and robustness of the method is demonstrated by numerical experiments on a set...

Prediction in Partial Duration Series With Generalized Pareto-Distributed Exceedances

DEFF Research Database (Denmark)

Rosbjerg, Dan; Madsen, Henrik; Rasmussen, Peter Funder

1992-01-01

As a generalization of the common assumption of exponential distribution of the exceedances in Partial duration series the generalized Pareto distribution has been adopted. Estimators for the parameters are presented using estimation by both method of moments and probability-weighted moments...... distributions (with physically justified upper limit) the correct exceedance distribution should be applied despite a possible acceptance of the exponential assumption by a test of significance....

Optimization and experimental validation of stiff porous phononic plates for widest complete bandgap of mixed fundamental guided wave modes

Science.gov (United States)

Hedayatrasa, Saeid; Kersemans, Mathias; Abhary, Kazem; Uddin, Mohammad; Van Paepegem, Wim

2018-01-01

Phononic crystal plates (PhPs) have promising application in manipulation of guided waves for design of low-loss acoustic devices and built-in acoustic metamaterial lenses in plate structures. The prominent feature of phononic crystals is the existence of frequency bandgaps over which the waves are stopped, or are resonated and guided within appropriate defects. Therefore, maximized bandgaps of PhPs are desirable to enhance their phononic controllability. Porous PhPs produced through perforation of a uniform background plate, in which the porous interfaces act as strong reflectors of wave energy, are relatively easy to produce. However, the research in optimization of porous PhPs and experimental validation of achieved topologies has been very limited and particularly focused on bandgaps of flexural (asymmetric) wave modes. In this paper, porous PhPs are optimized through an efficient multiobjective genetic algorithm for widest complete bandgap of mixed fundamental guided wave modes (symmetric and asymmetric) and maximized stiffness. The Pareto front of optimization is analyzed and variation of bandgap efficiency with respect to stiffness is presented for various optimized topologies. Selected optimized topologies from the stiff and compliant regimes of Pareto front are manufactured by water-jetting an aluminum plate and their promising bandgap efficiency is experimentally observed. An optimized Pareto topology is also chosen and manufactured by laser cutting a Plexiglas (PMMA) plate, and its performance in self-collimation and focusing of guided waves is verified as compared to calculated dispersion properties.

Multi-pruning of decision trees for knowledge representation and classification

KAUST Repository

Azad, Mohammad

2016-06-09

We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

Multi-pruning of decision trees for knowledge representation and classification

KAUST Repository

Azad, Mohammad; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

2016-01-01

We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

Lettere di Vilfredo Pareto all’amico Roberto Michels: confini e confine nel Trattato di Sociologia Generale del 1916

Directory of Open Access Journals (Sweden)

Raffaele Federici

2017-08-01

Full Text Available In questa ricerca di senso fra la fine di un'epoca e la nuova visione del mondo, c’è, nei due Autori, quello che potrebbe chiamarsi una betweenness: Pareto, quasi un franco-italiano, e Michels, un italiano-tedesco, anzi un più che italiano. Nella linea di faglia rappresentata dal primo conflitto mondiale, i due sociologi sono in una doppia relazione interiore appunto franco-italiana Pareto e italo-tedesca Michels e una relazione esteriore fra il mondo di ieri e il mondo successivo al cataclisma che fu la prima guerra mondiale, quando ben quattro imperi colossali erano stati smembrati (l’Impero Russo, l’Impero Tedesco, l’Impero Austro-ungarico e l’Impero ottomano, nello stesso tempo in cui Emile Durkheim guardava con inquietudine alla disgregazione delle vecchie comunità tradizionali, dove il senso della crisi del tempo investe non solo le persone e i comportamenti, ma il mondo logico stesso. Lo scambio epistolare avviene nella stessa terra: Pareto a Celigny, sul lago di Ginevra , e Michels a Basilea , lungo le rive del Reno. Vi è, fra i due sociologi un profondo rispetto, che vedrà Robert Michels dedicare allo “scienziato e amico Vilfredo Pareto con venerazione” un’opera importante come “Problemi di sociologia applicata” pubblicata solo tre anni dopo il Trattato di Sociologia Generale del Maestro. In questa antologia di saggi Robert Michels, probabilmente composti fra il 1914 e il 1917, negli anni del grande cataclisma, anzi concepiti prima «dell’insediamento di questa terribile corte suprema di cassazione di tutte le nostre ideologie, che è la guerra» , quindi contemporanea al Trattato, il Maestro viene citato tre volte, come Max Weber, ma, de facto, la presenza di Pareto è continua. In particolare, il richiamo al Maestro è iscritto a due piste di ricerca: da una parte la realtà della ricerca sociologica e del suo amplissimo spettro di analisi e dall’altra la teoria della circolazione delle elités. È proprio

Convex reformulation of biologically-based multi-criteria intensity-modulated radiation therapy optimization including fractionation effects.

Science.gov (United States)

Hoffmann, Aswin L; den Hertog, Dick; Siem, Alex Y D; Kaanders, Johannes H A M; Huizenga, Henk

2008-11-21

Finding fluence maps for intensity-modulated radiation therapy (IMRT) can be formulated as a multi-criteria optimization problem for which Pareto optimal treatment plans exist. To account for the dose-per-fraction effect of fractionated IMRT, it is desirable to exploit radiobiological treatment plan evaluation criteria based on the linear-quadratic (LQ) cell survival model as a means to balance the radiation benefits and risks in terms of biologic response. Unfortunately, the LQ-model-based radiobiological criteria are nonconvex functions, which make the optimization problem hard to solve. We apply the framework proposed by Romeijn et al (2004 Phys. Med. Biol. 49 1991-2013) to find transformations of LQ-model-based radiobiological functions and establish conditions under which transformed functions result in equivalent convex criteria that do not change the set of Pareto optimal treatment plans. The functions analysed are: the LQ-Poisson-based model for tumour control probability (TCP) with and without inter-patient heterogeneity in radiation sensitivity, the LQ-Poisson-based relative seriality s-model for normal tissue complication probability (NTCP), the equivalent uniform dose (EUD) under the LQ-Poisson model and the fractionation-corrected Probit-based model for NTCP according to Lyman, Kutcher and Burman. These functions differ from those analysed before in that they cannot be decomposed into elementary EUD or generalized-EUD functions. In addition, we show that applying increasing and concave transformations to the convexified functions is beneficial for the piecewise approximation of the Pareto efficient frontier.

Application of the Pareto principle to identify and address drug-therapy safety issues.

Science.gov (United States)

Müller, Fabian; Dormann, Harald; Pfistermeister, Barbara; Sonst, Anja; Patapovas, Andrius; Vogler, Renate; Hartmann, Nina; Plank-Kiegele, Bettina; Kirchner, Melanie; Bürkle, Thomas; Maas, Renke

2014-06-01

Adverse drug events (ADE) and medication errors (ME) are common causes of morbidity in patients presenting at emergency departments (ED). Recognition of ADE as being drug related and prevention of ME are key to enhancing pharmacotherapy safety in ED. We assessed the applicability of the Pareto principle (~80 % of effects result from 20 % of causes) to address locally relevant problems of drug therapy. In 752 cases consecutively admitted to the nontraumatic ED of a major regional hospital, ADE, ME, contributing drugs, preventability, and detection rates of ADE by ED staff were investigated. Symptoms, errors, and drugs were sorted by frequency in order to apply the Pareto principle. In total, 242 ADE were observed, and 148 (61.2 %) were assessed as preventable. ADE contributed to 110 inpatient hospitalizations. The ten most frequent symptoms were causally involved in 88 (80.0 %) inpatient hospitalizations. Only 45 (18.6 %) ADE were recognized as drug-related problems until discharge from the ED. A limited set of 33 drugs accounted for 184 (76.0 %) ADE; ME contributed to 57 ADE. Frequency-based listing of ADE, ME, and drugs involved allowed identification of the most relevant problems and development of easily to implement safety measures, such as wall and pocket charts. The Pareto principle provides a method for identifying the locally most relevant ADE, ME, and involved drugs. This permits subsequent development of interventions to increase patient safety in the ED admission process that best suit local needs.

Several comparison result of two types of equilibrium (Pareto Schemes and Stackelberg Scheme) of game theory approach in probabilistic vendor – buyer supply chain system with imperfect quality

Science.gov (United States)

Setiawan, R.

2018-05-01

In this paper, Economic Order Quantity (EOQ) of the vendor-buyer supply-chain model under a probabilistic condition with imperfect quality items has been analysed. The analysis is delivered using two concepts in game theory approach, which is Stackelberg equilibrium and Pareto Optimal, under non-cooperative and cooperative games, respectively. Another result is getting acomparison of theoptimal result between integrated scheme and game theory approach based on analytical and numerical result using appropriate simulation data.

Optimal design criteria - prediction vs. parameter estimation

Science.gov (United States)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Single Cell Dynamics Causes Pareto-Like Effect in Stimulated T Cell Populations.

Science.gov (United States)

Cosette, Jérémie; Moussy, Alice; Onodi, Fanny; Auffret-Cariou, Adrien; Neildez-Nguyen, Thi My Anh; Paldi, Andras; Stockholm, Daniel

2015-12-09

Cell fate choice during the process of differentiation may obey to deterministic or stochastic rules. In order to discriminate between these two strategies we used time-lapse microscopy of individual murine CD4 + T cells that allows investigating the dynamics of proliferation and fate commitment. We observed highly heterogeneous division and death rates between individual clones resulting in a Pareto-like dominance of a few clones at the end of the experiment. Commitment to the Treg fate was monitored using the expression of a GFP reporter gene under the control of the endogenous Foxp3 promoter. All possible combinations of proliferation and differentiation were observed and resulted in exclusively GFP-, GFP+ or mixed phenotype clones of very different population sizes. We simulated the process of proliferation and differentiation using a simple mathematical model of stochastic decision-making based on the experimentally observed parameters. The simulations show that a stochastic scenario is fully compatible with the observed Pareto-like imbalance in the final population.

The geometry of the Pareto front in biological phenotype space

Science.gov (United States)

Sheftel, Hila; Shoval, Oren; Mayo, Avi; Alon, Uri