Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning

International Nuclear Information System (INIS)

Aubry, Jean-Francois; Beaulieu, Frederic; Sevigny, Caroline; Beaulieu, Luc; Tremblay, Daniel

2006-01-01

Inverse planning in external beam radiotherapy often requires a scalar objective function that incorporates importance factors to mimic the planner's preferences between conflicting objectives. Defining those importance factors is not straightforward, and frequently leads to an iterative process in which the importance factors become variables of the optimization problem. In order to avoid this drawback of inverse planning, optimization using algorithms more suited to multiobjective optimization, such as evolutionary algorithms, has been suggested. However, much inverse planning software, including one based on simulated annealing developed at our institution, does not include multiobjective-oriented algorithms. This work investigates the performance of a modified simulated annealing algorithm used to drive aperture-based intensity-modulated radiotherapy inverse planning software in a multiobjective optimization framework. For a few test cases involving gastric cancer patients, the use of this new algorithm leads to an increase in optimization speed of a little more than a factor of 2 over a conventional simulated annealing algorithm, while giving a close approximation of the solutions produced by a standard simulated annealing. A simple graphical user interface designed to facilitate the decision-making process that follows an optimization is also presented

Algorithms for the optimization of RBE-weighted dose in particle therapy.

Science.gov (United States)

Horcicka, M; Meyer, C; Buschbacher, A; Durante, M; Krämer, M

2013-01-21

We report on various algorithms used for the nonlinear optimization of RBE-weighted dose in particle therapy. Concerning the dose calculation carbon ions are considered and biological effects are calculated by the Local Effect Model. Taking biological effects fully into account requires iterative methods to solve the optimization problem. We implemented several additional algorithms into GSI's treatment planning system TRiP98, like the BFGS-algorithm and the method of conjugated gradients, in order to investigate their computational performance. We modified textbook iteration procedures to improve the convergence speed. The performance of the algorithms is presented by convergence in terms of iterations and computation time. We found that the Fletcher-Reeves variant of the method of conjugated gradients is the algorithm with the best computational performance. With this algorithm we could speed up computation times by a factor of 4 compared to the method of steepest descent, which was used before. With our new methods it is possible to optimize complex treatment plans in a few minutes leading to good dose distributions. At the end we discuss future goals concerning dose optimization issues in particle therapy which might benefit from fast optimization solvers.

On the efficiency of chaos optimization algorithms for global optimization

International Nuclear Information System (INIS)

Yang Dixiong; Li Gang; Cheng Gengdong

2007-01-01

Chaos optimization algorithms as a novel method of global optimization have attracted much attention, which were all based on Logistic map. However, we have noticed that the probability density function of the chaotic sequences derived from Logistic map is a Chebyshev-type one, which may affect the global searching capacity and computational efficiency of chaos optimization algorithms considerably. Considering the statistical property of the chaotic sequences of Logistic map and Kent map, the improved hybrid chaos-BFGS optimization algorithm and the Kent map based hybrid chaos-BFGS algorithm are proposed. Five typical nonlinear functions with multimodal characteristic are tested to compare the performance of five hybrid optimization algorithms, which are the conventional Logistic map based chaos-BFGS algorithm, improved Logistic map based chaos-BFGS algorithm, Kent map based chaos-BFGS algorithm, Monte Carlo-BFGS algorithm, mesh-BFGS algorithm. The computational performance of the five algorithms is compared, and the numerical results make us question the high efficiency of the chaos optimization algorithms claimed in some references. It is concluded that the efficiency of the hybrid optimization algorithms is influenced by the statistical property of chaotic/stochastic sequences generated from chaotic/stochastic algorithms, and the location of the global optimum of nonlinear functions. In addition, it is inappropriate to advocate the high efficiency of the global optimization algorithms only depending on several numerical examples of low-dimensional functions

Optimization algorithms and applications

CERN Document Server

Arora, Rajesh Kumar

2015-01-01

Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc

Stochastic Recursive Algorithms for Optimization Simultaneous Perturbation Methods

CERN Document Server

Bhatnagar, S; Prashanth, L A

2013-01-01

Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from sim...

Optimized Bayesian dynamic advising theory and algorithms

CERN Document Server

Karny, Miroslav

2006-01-01

Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture models

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.

Genetic algorithms and fuzzy multiobjective optimization

CERN Document Server

Sakawa, Masatoshi

2002-01-01

Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...

A Novel Particle Swarm Optimization Algorithm for Global Optimization.

Science.gov (United States)

Wang, Chun-Feng; Liu, Kui

2016-01-01

Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

New Optimization Algorithms in Physics

CERN Document Server

Hartmann, Alexander K

2004-01-01

Many physicists are not aware of the fact that they can solve their problems by applying optimization algorithms. Since the number of such algorithms is steadily increasing, many new algorithms have not been presented comprehensively until now. This presentation of recently developed algorithms applied in physics, including demonstrations of how they work and related results, aims to encourage their application, and as such the algorithms selected cover concepts and methods from statistical physics to optimization problems emerging in theoretical computer science.

Treatment planning optimization for linear accelerator radiosurgery

International Nuclear Information System (INIS)

Meeks, Sanford L.; Buatti, John M.; Bova, Francis J.; Friedman, William A.; Mendenhall, William M.

1998-01-01

Purpose: Linear accelerator radiosurgery uses multiple arcs delivered through circular collimators to produce a nominally spherical dose distribution. Production of dose distributions that conform to irregular lesions or conformally avoid critical neural structures requires a detailed understanding of the available treatment planning parameters. Methods and Materials: Treatment planning parameters that may be manipulated within a single isocenter to provide conformal avoidance and dose conformation to ellipsoidal lesions include differential arc weighting and gantry start/stop angles. More irregular lesions require the use of multiple isocenters. Iterative manipulation of treatment planning variables can be difficult and computationally expensive, especially if the effects of these manipulations are not well defined. Effects of treatment parameter manipulation are explained and illustrated. This is followed by description of the University of Florida Stereotactic Radiosurgery Treatment Planning Algorithm. This algorithm organizes the manipulations into a practical approach for radiosurgery treatment planning. Results: Iterative treatment planning parameters may be efficiently manipulated to achieve optimal treatment plans by following the University of Florida Treatment Planning Algorithm. The ability to produce conformal stereotactic treatment plans using the algorithm is demonstrated for a variety of clinical presentations. Conclusion: The standard dose distribution produced in linear accelerator radiosurgery is spherical, but manipulation of available treatment planning parameters may result in optimal dose conformation. The University of Florida Treatment Planning Algorithm organizes available treatment parameters to efficiently produce conformal radiosurgery treatment plans

Particle swarm optimizer for weighting factor selection in intensity-modulated radiation therapy optimization algorithms.

Science.gov (United States)

Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo

2017-01-01

In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human

Group leaders optimization algorithm

Science.gov (United States)

Daskin, Anmer; Kais, Sabre

2011-03-01

We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multi-dimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N 2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for a two-qubit Grover search algorithm which is a quantum algorithm providing quadratic speedup over the classical counterpart.

Ant colony search algorithm for optimal reactive power optimization

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Lenin K.

2006-01-01

Full Text Available The paper presents an (ACSA Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical test bus system. The proposed approach is tested and compared to genetic algorithm (GA, Adaptive Genetic Algorithm (AGA.

Optimally stopped variational quantum algorithms

Science.gov (United States)

Vinci, Walter; Shabani, Alireza

2018-04-01

Quantum processors promise a paradigm shift in high-performance computing which needs to be assessed by accurate benchmarking measures. In this article, we introduce a benchmark for the variational quantum algorithm (VQA), recently proposed as a heuristic algorithm for small-scale quantum processors. In VQA, a classical optimization algorithm guides the processor's quantum dynamics to yield the best solution for a given problem. A complete assessment of the scalability and competitiveness of VQA should take into account both the quality and the time of dynamics optimization. The method of optimal stopping, employed here, provides such an assessment by explicitly including time as a cost factor. Here, we showcase this measure for benchmarking VQA as a solver for some quadratic unconstrained binary optimization. Moreover, we show that a better choice for the cost function of the classical routine can significantly improve the performance of the VQA algorithm and even improve its scaling properties.

SU-F-T-209: Multicriteria Optimization Algorithm for Intensity Modulated Radiation Therapy Using Pencil Proton Beam Scanning

Energy Technology Data Exchange (ETDEWEB)

Beltran, C; Kamal, H [Mayo Clinic, Rochester, MN (United States)

2016-06-15

Purpose: To provide a multicriteria optimization algorithm for intensity modulated radiation therapy using pencil proton beam scanning. Methods: Intensity modulated radiation therapy using pencil proton beam scanning requires efficient optimization algorithms to overcome the uncertainties in the Bragg peaks locations. This work is focused on optimization algorithms that are based on Monte Carlo simulation of the treatment planning and use the weights and the dose volume histogram (DVH) control points to steer toward desired plans. The proton beam treatment planning process based on single objective optimization (representing a weighted sum of multiple objectives) usually leads to time-consuming iterations involving treatment planning team members. We proved a time efficient multicriteria optimization algorithm that is developed to run on NVIDIA GPU (Graphical Processing Units) cluster. The multicriteria optimization algorithm running time benefits from up-sampling of the CT voxel size of the calculations without loss of fidelity. Results: We will present preliminary results of Multicriteria optimization for intensity modulated proton therapy based on DVH control points. The results will show optimization results of a phantom case and a brain tumor case. Conclusion: The multicriteria optimization of the intensity modulated radiation therapy using pencil proton beam scanning provides a novel tool for treatment planning. Work support by a grant from Varian Inc.

SU-F-T-209: Multicriteria Optimization Algorithm for Intensity Modulated Radiation Therapy Using Pencil Proton Beam Scanning

International Nuclear Information System (INIS)

Beltran, C; Kamal, H

2016-01-01

Purpose: To provide a multicriteria optimization algorithm for intensity modulated radiation therapy using pencil proton beam scanning. Methods: Intensity modulated radiation therapy using pencil proton beam scanning requires efficient optimization algorithms to overcome the uncertainties in the Bragg peaks locations. This work is focused on optimization algorithms that are based on Monte Carlo simulation of the treatment planning and use the weights and the dose volume histogram (DVH) control points to steer toward desired plans. The proton beam treatment planning process based on single objective optimization (representing a weighted sum of multiple objectives) usually leads to time-consuming iterations involving treatment planning team members. We proved a time efficient multicriteria optimization algorithm that is developed to run on NVIDIA GPU (Graphical Processing Units) cluster. The multicriteria optimization algorithm running time benefits from up-sampling of the CT voxel size of the calculations without loss of fidelity. Results: We will present preliminary results of Multicriteria optimization for intensity modulated proton therapy based on DVH control points. The results will show optimization results of a phantom case and a brain tumor case. Conclusion: The multicriteria optimization of the intensity modulated radiation therapy using pencil proton beam scanning provides a novel tool for treatment planning. Work support by a grant from Varian Inc.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

Science.gov (United States)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

Science.gov (United States)

Zirkohi, Majid Moradi

2015-04-01

The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.

FIREWORKS ALGORITHM FOR UNCONSTRAINED FUNCTION OPTIMIZATION PROBLEMS

Directory of Open Access Journals (Sweden)

Evans BAIDOO

2017-03-01

Full Text Available Modern real world science and engineering problems can be classified as multi-objective optimisation problems which demand for expedient and efficient stochastic algorithms to respond to the optimization needs. This paper presents an object-oriented software application that implements a firework optimization algorithm for function optimization problems. The algorithm, a kind of parallel diffuse optimization algorithm is based on the explosive phenomenon of fireworks. The algorithm presented promising results when compared to other population or iterative based meta-heuristic algorithm after it was experimented on five standard benchmark problems. The software application was implemented in Java with interactive interface which allow for easy modification and extended experimentation. Additionally, this paper validates the effect of runtime on the algorithm performance.

Advances in metaheuristic algorithms for optimal design of structures

CERN Document Server

Kaveh, A

2017-01-01

This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...

Advances in metaheuristic algorithms for optimal design of structures

CERN Document Server

Kaveh, A

2014-01-01

This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...

A Feedback Optimal Control Algorithm with Optimal Measurement Time Points

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Felix Jost

2017-02-01

Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.

A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization

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Soroor Sarafrazi

2015-07-01

Full Text Available It is now well recognized that pure algorithms can be promisingly improved by hybridization with other techniques. One of the relatively new metaheuristic algorithms is Gravitational Search Algorithm (GSA which is based on the Newton laws. In this paper, to enhance the performance of GSA, a novel algorithm called “Kepler”, inspired by the astrophysics, is introduced. The Kepler algorithm is based on the principle of the first Kepler law. The hybridization of GSA and Kepler algorithm is an efficient approach to provide much stronger specialization in intensification and/or diversification. The performance of GSA–Kepler is evaluated by applying it to 14 benchmark functions with 20–1000 dimensions and the optimal approximation of linear system as a practical optimization problem. The results obtained reveal that the proposed hybrid algorithm is robust enough to optimize the benchmark functions and practical optimization problems.

Plug pattern optimization for gamma knife radiosurgery treatment planning

International Nuclear Information System (INIS)

Zhang Pengpeng; Wu, Jackie; Dean, David; Xing Lei; Xue Jinyue; Maciunas, Robert; Sibata, Claudio

2003-01-01

Purpose: To develop a novel dose optimization algorithm for improving the sparing of critical structures during gamma knife radiosurgery by shaping the plug pattern of each individual shot. Method and Materials: We first use a geometric information (medial axis) aided guided evolutionary simulated annealing (GESA) optimization algorithm to determine the number of shots and isocenter location, size, and weight of each shot. Then we create a plug quality score system that checks the dose contribution to the volume of interest by each plug in the treatment plan. A positive score implies that the corresponding source could be open to improve tumor coverage, whereas a negative score means the source could be blocked for the purpose of sparing normal and critical structures. The plug pattern is then optimized via the GESA algorithm that is integrated with this score system. Weight and position of each shot are also tuned in this procedure. Results: An acoustic tumor case is used to evaluate our algorithm. Compared to the treatment plan generated without plug patterns, adding an optimized plug pattern into the treatment planning process boosts tumor coverage index from 95.1% to 97.2%, reduces RTOG conformity index from 1.279 to 1.167, lowers Paddick's index from 1.34 to 1.20, and trims the critical structure receiving more than 30% maximum dose from 16 mm 3 to 6 mm 3 . Conclusions: Automated GESA-based plug pattern optimization of gamma knife radiosurgery frees the treatment planning team from the manual forward planning procedure and provides an optimal treatment plan

Hybrid Optimization Algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

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Jianwen Guo

2016-01-01

Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

Support vector machines optimization based theory, algorithms, and extensions

CERN Document Server

Deng, Naiyang; Zhang, Chunhua

2013-01-01

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi

Optimal hydrogenerator governor tuning with a genetic algorithm

International Nuclear Information System (INIS)

Lansberry, J.E.; Wozniak, L.; Goldberg, D.E.

1992-01-01

Many techniques exist for developing optimal controllers. This paper investigates genetic algorithms as a means of finding optimal solutions over a parameter space. In particular, the genetic algorithm is applied to optimal tuning of a governor for a hydrogenerator plant. Analog and digital simulation methods are compared for use in conjunction with the genetic algorithm optimization process. It is shown that analog plant simulation provides advantages in speed over digital plant simulation. This speed advantage makes application of the genetic algorithm in an actual plant environment feasible. Furthermore, the genetic algorithm is shown to possess the ability to reject plant noise and other system anomalies in its search for optimizing solutions

Loading pattern optimization using ant colony algorithm

International Nuclear Information System (INIS)

Hoareau, Fabrice

2008-01-01

Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)

On benchmarking Stochastic Global Optimization Algorithms

NARCIS (Netherlands)

Hendrix, E.M.T.; Lancinskas, A.

2015-01-01

A multitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

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Daqing Wu

2012-01-01

Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

Genetic algorithms applied to nuclear reactor design optimization

International Nuclear Information System (INIS)

Pereira, C.M.N.A.; Schirru, R.; Martinez, A.S.

2000-01-01

A genetic algorithm is a powerful search technique that simulates natural evolution in order to fit a population of computational structures to the solution of an optimization problem. This technique presents several advantages over classical ones such as linear programming based techniques, often used in nuclear engineering optimization problems. However, genetic algorithms demand some extra computational cost. Nowadays, due to the fast computers available, the use of genetic algorithms has increased and its practical application has become a reality. In nuclear engineering there are many difficult optimization problems related to nuclear reactor design. Genetic algorithm is a suitable technique to face such kind of problems. This chapter presents applications of genetic algorithms for nuclear reactor core design optimization. A genetic algorithm has been designed to optimize the nuclear reactor cell parameters, such as array pitch, isotopic enrichment, dimensions and cells materials. Some advantages of this genetic algorithm implementation over a classical method based on linear programming are revealed through the application of both techniques to a simple optimization problem. In order to emphasize the suitability of genetic algorithms for design optimization, the technique was successfully applied to a more complex problem, where the classical method is not suitable. Results and comments about the applications are also presented. (orig.)

A Cooperative Harmony Search Algorithm for Function Optimization

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Gang Li

2014-01-01

Full Text Available Harmony search algorithm (HS is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs and particle swarm optimizers (PSOs. It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.

Optimization of Residual Stress of High Temperature Treatment Using Genetic Algorithm and Neural Network

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M. Susmikanti

2015-12-01

Full Text Available In a nuclear industry area, high temperature treatment of materials is a factor which requires special attention. Assessment needs to be conducted on the properties of the materials used, including the strength of the materials. The measurement of material properties under thermal processes may reflect residual stresses. The use of Genetic Algorithm (GA to determine the optimal residual stress is one way to determine the strength of a material. In residual stress modeling with several parameters, it is sometimes difficult to solve for the optimal value through analytical or numerical calculations. Here, GA is an efficient algorithm which can generate the optimal values, both minima and maxima. The purposes of this research are to obtain the optimization of variable in residual stress models using GA and to predict the center of residual stress distribution, using fuzzy neural network (FNN while the artificial neural network (ANN used for modeling. In this work a single-material 316/316L stainless steel bar is modeled. The minimal residual stresses of the material at high temperatures were obtained with GA and analytical calculations. At a temperature of 6500C, the GA optimal residual stress estimation converged at –711.3689 MPa at adistance of 0.002934 mm from center point, whereas the analytical calculation result at that temperature and position is -975.556 MPa . At a temperature of 8500C, the GA result was -969.868 MPa at 0.002757 mm from the center point, while with analytical result was -1061.13 MPa. The difference in residual stress between GA and analytical results at a temperatureof6500C is about 27 %, while at 8500C it is 8.67 %. The distribution of residual stress showed a grouping concentrated around a coordinate of (-76; 76 MPa. The residuals stress model is a degree-two polynomial with coefficients of 50.33, -76.54, and -55.2, respectively, with a standard deviation of 7.874.

Interactively exploring optimized treatment plans

International Nuclear Information System (INIS)

Rosen, Isaac; Liu, H. Helen; Childress, Nathan; Liao Zhongxing

2005-01-01

Purpose: A new paradigm for treatment planning is proposed that embodies the concept of interactively exploring the space of optimized plans. In this approach, treatment planning ignores the details of individual plans and instead presents the physician with clinical summaries of sets of solutions to well-defined clinical goals in which every solution has been optimized in advance by computer algorithms. Methods and materials: Before interactive planning, sets of optimized plans are created for a variety of treatment delivery options and critical structure dose-volume constraints. Then, the dose-volume parameters of the optimized plans are fit to linear functions. These linear functions are used to show in real time how the target dose-volume histogram (DVH) changes as the DVHs of the critical structures are changed interactively. A bitmap of the space of optimized plans is used to restrict the feasible solutions. The physician selects the critical structure dose-volume constraints that give the desired dose to the planning target volume (PTV) and then those constraints are used to create the corresponding optimized plan. Results: The method is demonstrated using prototype software, Treatment Plan Explorer (TPEx), and a clinical example of a patient with a tumor in the right lung. For this example, the delivery options included 4 open beams, 12 open beams, 4 wedged beams, and 12 wedged beams. Beam directions and relative weights were optimized for a range of critical structure dose-volume constraints for the lungs and esophagus. Cord dose was restricted to 45 Gy. Using the interactive interface, the physician explored how the tumor dose changed as critical structure dose-volume constraints were tightened or relaxed and selected the best compromise for each delivery option. The corresponding treatment plans were calculated and compared with the linear parameterization presented to the physician in TPEx. The linear fits were best for the maximum PTV dose and worst

Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm

International Nuclear Information System (INIS)

Rao, R.V.; More, K.C.

2015-01-01

Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. - Highlights: • The TLBO (Teaching–Learning-Based Optimization) algorithm is used for the design and optimization of a heat pipe. • Two examples of heat pipe design and optimization are presented. • The TLBO algorithm is proved better than the other optimization algorithms in terms of results and the convergence

Distributed Algorithms for Time Optimal Reachability Analysis

DEFF Research Database (Denmark)

Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand

2016-01-01

. We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general.......Time optimal reachability analysis is a novel model based technique for solving scheduling and planning problems. After modeling them as reachability problems using timed automata, a real-time model checker can compute the fastest trace to the goal states which constitutes a time optimal schedule...

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Directory of Open Access Journals (Sweden)

Narinder Singh

2018-03-01

Full Text Available The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO and Whale Optimizer Algorithm (WOA. We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO algorithm: (i firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

Configurable intelligent optimization algorithm design and practice in manufacturing

CERN Document Server

Tao, Fei; Laili, Yuanjun

2014-01-01

Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorit

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

Optimization of Multipurpose Reservoir Operation with Application Particle Swarm Optimization Algorithm

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Elahe Fallah Mehdipour

2012-12-01

Full Text Available Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research, application of multi-objective particle swarm optimization (MOPSO in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture, recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

An Efficient Algorithm for Unconstrained Optimization

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Sergio Gerardo de-los-Cobos-Silva

2015-01-01

Full Text Available This paper presents an original and efficient PSO algorithm, which is divided into three phases: (1 stabilization, (2 breadth-first search, and (3 depth-first search. The proposed algorithm, called PSO-3P, was tested with 47 benchmark continuous unconstrained optimization problems, on a total of 82 instances. The numerical results show that the proposed algorithm is able to reach the global optimum. This work mainly focuses on unconstrained optimization problems from 2 to 1,000 variables.

Comparison of optimization algorithms in intensity-modulated radiation therapy planning

Science.gov (United States)

Kendrick, Rachel

Intensity-modulated radiation therapy is used to better conform the radiation dose to the target, which includes avoiding healthy tissue. Planning programs employ optimization methods to search for the best fluence of each photon beam, and therefore to create the best treatment plan. The Computational Environment for Radiotherapy Research (CERR), a program written in MATLAB, was used to examine some commonly-used algorithms for one 5-beam plan. Algorithms include the genetic algorithm, quadratic programming, pattern search, constrained nonlinear optimization, simulated annealing, the optimization method used in Varian EclipseTM, and some hybrids of these. Quadratic programing, simulated annealing, and a quadratic/simulated annealing hybrid were also separately compared using different prescription doses. The results of each dose-volume histogram as well as the visual dose color wash were used to compare the plans. CERR's built-in quadratic programming provided the best overall plan, but avoidance of the organ-at-risk was rivaled by other programs. Hybrids of quadratic programming with some of these algorithms seems to suggest the possibility of better planning programs, as shown by the improved quadratic/simulated annealing plan when compared to the simulated annealing algorithm alone. Further experimentation will be done to improve cost functions and computational time.

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

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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.

Chemical optimization algorithm for fuzzy controller design

CERN Document Server

Astudillo, Leslie; Castillo, Oscar

2014-01-01

In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application

The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

Science.gov (United States)

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Queue and stack sorting algorithm optimization and performance analysis

Science.gov (United States)

Qian, Mingzhu; Wang, Xiaobao

2018-04-01

Sorting algorithm is one of the basic operation of a variety of software development, in data structures course specializes in all kinds of sort algorithm. The performance of the sorting algorithm is directly related to the efficiency of the software. A lot of excellent scientific research queue is constantly optimizing algorithm, algorithm efficiency better as far as possible, the author here further research queue combined with stacks of sorting algorithms, the algorithm is mainly used for alternating operation queue and stack storage properties, Thus avoiding the need for a large number of exchange or mobile operations in the traditional sort. Before the existing basis to continue research, improvement and optimization, the focus on the optimization of the time complexity of the proposed optimization and improvement, The experimental results show that the improved effectively, at the same time and the time complexity and space complexity of the algorithm, the stability study corresponding research. The improvement and optimization algorithm, improves the practicability.

Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem

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Kanagasabai Lenin

2015-04-01

Full Text Available In this paper, a novel approach Modified Monkey optimization (MMO algorithm for solving optimal reactive power dispatch problem has been presented. MMO is a population based stochastic meta-heuristic algorithm and it is inspired by intelligent foraging behaviour of monkeys. This paper improves both local leader and global leader phases.  The proposed (MMO algorithm has been tested in standard IEEE 30 bus test system and simulation results show the worthy performance of the proposed algorithm in reducing the real power loss.

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

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Yuksel Celik

2013-01-01

Full Text Available Marriage in honey bees optimization (MBO is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

Heterogeneous architecture to process swarm optimization algorithms

Directory of Open Access Journals (Sweden)

Maria A. Dávila-Guzmán

2014-01-01

Full Text Available Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.

Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Directory of Open Access Journals (Sweden)

Imam Ahmad Ashari

2016-11-01

Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization  algorithm in solving the case of course scheduling.

An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

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Vivek Patel

2012-08-01

Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

Algorithms for optimal dyadic decision trees

Energy Technology Data Exchange (ETDEWEB)

Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory

2009-01-01

A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System

CERN Document Server

Tydrichova, Magdalena

2017-01-01

In this project, various available multi-objective optimization evolutionary algorithms were compared considering their performance and distribution of solutions. The main goal was to select the most suitable algorithms for applications in cancer hadron therapy planning. For our purposes, a complex testing and analysis software was developed. Also, many conclusions and hypothesis have been done for the further research.

COOMA: AN OBJECT-ORIENTED STOCHASTIC OPTIMIZATION ALGORITHM

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Stanislav Alexandrovich Tavridovich

2017-09-01

Full Text Available Stochastic optimization methods such as genetic algorithm, particle swarm optimization algorithm, and others are successfully used to solve optimization problems. They are all based on similar ideas and need minimal adaptation when being implemented. But several factors complicate the application of stochastic search methods in practice: multimodality of the objective function, optimization with constraints, finding the best parameter configuration of the algorithm, the increasing of the searching space, etc. This paper proposes a new Cascade Object Optimization and Modification Algorithm (COOMA which develops the best ideas of known stochastic optimization methods and can be applied to a wide variety of real-world problems described in the terms of object-oriented models with practically any types of parameters, variables, and associations between objects. The objects of different classes are organized in pools and pools form the hierarchical structure according to the associations between classes. The algorithm is also executed according to the pool structure: the methods of the upper-level pools before changing their objects call the analogous methods of all their subpools. The algorithm starts with initialization step and then passes through a number of iterations during which the objects are modified until the stop criteria are satisfied. The objects are modified using movement, replication and mutation operations. Two-level version of COOMA realizes a built-in self-adaptive mechanism. The optimization statistics for a number of test problems shows that COOMA is able to solve multi-level problems (with objects of different associated classes, problems with multimodal fitness functions and systems of constraints. COOMA source code on Java is available on request.

Engineering local optimality in quantum Monte Carlo algorithms

Science.gov (United States)

Pollet, Lode; Van Houcke, Kris; Rombouts, Stefan M. A.

2007-08-01

Quantum Monte Carlo algorithms based on a world-line representation such as the worm algorithm and the directed loop algorithm are among the most powerful numerical techniques for the simulation of non-frustrated spin models and of bosonic models. Both algorithms work in the grand-canonical ensemble and can have a winding number larger than zero. However, they retain a lot of intrinsic degrees of freedom which can be used to optimize the algorithm. We let us guide by the rigorous statements on the globally optimal form of Markov chain Monte Carlo simulations in order to devise a locally optimal formulation of the worm algorithm while incorporating ideas from the directed loop algorithm. We provide numerical examples for the soft-core Bose-Hubbard model and various spin- S models.

Simulated annealing algorithm for optimal capital growth

Science.gov (United States)

Luo, Yong; Zhu, Bo; Tang, Yong

2014-08-01

We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.

Parallel Evolutionary Optimization Algorithms for Peptide-Protein Docking

Science.gov (United States)

Poluyan, Sergey; Ershov, Nikolay

2018-02-01

In this study we examine the possibility of using evolutionary optimization algorithms in protein-peptide docking. We present the main assumptions that reduce the docking problem to a continuous global optimization problem and provide a way of using evolutionary optimization algorithms. The Rosetta all-atom force field was used for structural representation and energy scoring. We describe the parallelization scheme and MPI/OpenMP realization of the considered algorithms. We demonstrate the efficiency and the performance for some algorithms which were applied to a set of benchmark tests.

Decoherence in optimized quantum random-walk search algorithm

International Nuclear Information System (INIS)

Zhang Yu-Chao; Bao Wan-Su; Wang Xiang; Fu Xiang-Qun

2015-01-01

This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative. (paper)

Belief Propagation Algorithm for Portfolio Optimization Problems.

Science.gov (United States)

Shinzato, Takashi; Yasuda, Muneki

2015-01-01

The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.

Belief Propagation Algorithm for Portfolio Optimization Problems.

Directory of Open Access Journals (Sweden)

Takashi Shinzato

Full Text Available The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.

Warehouse stocking optimization based on dynamic ant colony genetic algorithm

Science.gov (United States)

Xiao, Xiaoxu

2018-04-01

In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

Two-Phase Algorithm for Optimal Camera Placement

Directory of Open Access Journals (Sweden)

Jun-Woo Ahn

2016-01-01

Full Text Available As markers for visual sensor networks have become larger, interest in the optimal camera placement problem has continued to increase. The most featured solution for the optimal camera placement problem is based on binary integer programming (BIP. Due to the NP-hard characteristic of the optimal camera placement problem, however, it is difficult to find a solution for a complex, real-world problem using BIP. Many approximation algorithms have been developed to solve this problem. In this paper, a two-phase algorithm is proposed as an approximation algorithm based on BIP that can solve the optimal camera placement problem for a placement space larger than in current studies. This study solves the problem in three-dimensional space for a real-world structure.

A novel optimization method, Gravitational Search Algorithm (GSA), for PWR core optimization

International Nuclear Information System (INIS)

Mahmoudi, S.M.; Aghaie, M.; Bahonar, M.; Poursalehi, N.

2016-01-01

Highlights: • The Gravitational Search Algorithm (GSA) is introduced. • The advantage of GSA is verified in Shekel’s Foxholes. • Reload optimizing in WWER-1000 and WWER-440 cases are performed. • Maximizing K eff , minimizing PPFs and flattening power density is considered. - Abstract: In-core fuel management optimization (ICFMO) is one of the most challenging concepts of nuclear engineering. In recent decades several meta-heuristic algorithms or computational intelligence methods have been expanded to optimize reactor core loading pattern. This paper presents a new method of using Gravitational Search Algorithm (GSA) for in-core fuel management optimization. The GSA is constructed based on the law of gravity and the notion of mass interactions. It uses the theory of Newtonian physics and searcher agents are the collection of masses. In this work, at the first step, GSA method is compared with other meta-heuristic algorithms on Shekel’s Foxholes problem. In the second step for finding the best core, the GSA algorithm has been performed for three PWR test cases including WWER-1000 and WWER-440 reactors. In these cases, Multi objective optimizations with the following goals are considered, increment of multiplication factor (K eff ), decrement of power peaking factor (PPF) and power density flattening. It is notable that for neutronic calculation, PARCS (Purdue Advanced Reactor Core Simulator) code is used. The results demonstrate that GSA algorithm have promising performance and could be proposed for other optimization problems of nuclear engineering field.

Privacy Preservation in Distributed Subgradient Optimization Algorithms

OpenAIRE

Lou, Youcheng; Yu, Lean; Wang, Shouyang

2015-01-01

Privacy preservation is becoming an increasingly important issue in data mining and machine learning. In this paper, we consider the privacy preserving features of distributed subgradient optimization algorithms. We first show that a well-known distributed subgradient synchronous optimization algorithm, in which all agents make their optimization updates simultaneously at all times, is not privacy preserving in the sense that the malicious agent can learn other agents' subgradients asymptotic...

Practical mathematical optimization basic optimization theory and gradient-based algorithms

CERN Document Server

Snyman, Jan A

2018-01-01

This textbook presents a wide range of tools for a course in mathematical optimization for upper undergraduate and graduate students in mathematics, engineering, computer science, and other applied sciences. Basic optimization principles are presented with emphasis on gradient-based numerical optimization strategies and algorithms for solving both smooth and noisy discontinuous optimization problems. Attention is also paid to the difficulties of expense of function evaluations and the existence of multiple minima that often unnecessarily inhibit the use of gradient-based methods. This second edition addresses further advancements of gradient-only optimization strategies to handle discontinuities in objective functions. New chapters discuss the construction of surrogate models as well as new gradient-only solution strategies and numerical optimization using Python. A special Python module is electronically available (via springerlink) that makes the new algorithms featured in the text easily accessible and dir...

Teaching learning based optimization algorithm and its engineering applications

CERN Document Server

Rao, R Venkata

2016-01-01

Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.

Glowworm swarm optimization theory, algorithms, and applications

CERN Document Server

Kaipa, Krishnanand N

2017-01-01

This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intellige...

Analog Circuit Design Optimization Based on Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Mansour Barari

2014-01-01

Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

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)

A Novel Hybrid Firefly Algorithm for Global Optimization.

Directory of Open Access Journals (Sweden)

Lina Zhang

Full Text Available Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA, is proposed by combining the advantages of both the firefly algorithm (FA and differential evolution (DE. FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA, differential evolution (DE and particle swarm optimization (PSO in the sense of avoiding local minima and increasing the convergence rate.

ADORE-GA: Genetic algorithm variant of the ADORE algorithm for ROP detector layout optimization in CANDU reactors

International Nuclear Information System (INIS)

Kastanya, Doddy

2012-01-01

Highlights: ► ADORE is an algorithm for CANDU ROP Detector Layout Optimization. ► ADORE-GA is a Genetic Algorithm variant of the ADORE algorithm. ► Robustness test of ADORE-GA algorithm is presented in this paper. - Abstract: The regional overpower protection (ROP) systems protect CANDU® reactors against overpower in the fuel that could reduce the safety margin-to-dryout. The overpower could originate from a localized power peaking within the core or a general increase in the global core power level. The design of the detector layout for ROP systems is a challenging discrete optimization problem. In recent years, two algorithms have been developed to find a quasi optimal solution to this detector layout optimization problem. Both of these algorithms utilize the simulated annealing (SA) algorithm as their optimization engine. In the present paper, an alternative optimization algorithm, namely the genetic algorithm (GA), has been implemented as the optimization engine. The implementation is done within the ADORE algorithm. Results from evaluating the effects of using various mutation rates and crossover parameters are presented in this paper. It has been demonstrated that the algorithm is sufficiently robust in producing similar quality solutions.

Genetic Optimization Algorithm for Metabolic Engineering Revisited

Directory of Open Access Journals (Sweden)

Tobias B. Alter

2018-05-01

Full Text Available To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i multiple, non-linear engineering objectives; (ii the identification of gene target-sets according to logical gene-protein-reaction associations; (iii minimization of the number of network perturbations; and (iv the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

Science.gov (United States)

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

Scaling Sparse Matrices for Optimization Algorithms

OpenAIRE

Gajulapalli Ravindra S; Lasdon Leon S

2006-01-01

To iteratively solve large scale optimization problems in various contexts like planning, operations, design etc., we need to generate descent directions that are based on linear system solutions. Irrespective of the optimization algorithm or the solution method employed for the linear systems, ill conditioning introduced by problem characteristics or the algorithm or both need to be addressed. In [GL01] we used an intuitive heuristic approach in scaling linear systems that improved performan...

Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm

Directory of Open Access Journals (Sweden)

D.B. Prakash

2017-12-01

Full Text Available In present days, continuous effort is being made in bringing down the line losses of the electrical distribution networks. Therefore proper allocation of capacitors is of utmost importance because, it will help in reducing the line losses and maintaining the bus voltage. This in turn results in improving the stability and reliability of the system. In this paper Whale Optimization Algorithm (WOA is used to find optimal sizing and placement of capacitors for a typical radial distribution system. Multi objectives such as operating cost reduction and power loss minimization with inequality constraints on voltage limits are considered and the proposed algorithm is validated by applying it on standard radial systems: IEEE-34 bus and IEEE-85 bus radial distribution test systems. The results obtained are compared with those of existing algorithms. The results show that the proposed algorithm is more effective in bringing down the operating costs and in maintaining better voltage profile. Keywords: Whale Optimization Algorithm (WOA, Optimal allocation and sizing of capacitors, Power loss reduction and voltage stability improvement, Radial distribution system, Operating cost minimization

Modified artificial bee colony algorithm for reactive power optimization

Science.gov (United States)

Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

2015-05-01

Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.

Optimization in engineering models and algorithms

CERN Document Server

Sioshansi, Ramteen

2017-01-01

This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...

A Direct Search Algorithm for Global Optimization

Directory of Open Access Journals (Sweden)

Enrique Baeyens

2016-06-01

Full Text Available A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.

Optimization of Pressurizer Based on Genetic-Simplex Algorithm

International Nuclear Information System (INIS)

Wang, Cheng; Yan, Chang Qi; Wang, Jian Jun

2014-01-01

Pressurizer is one of key components in nuclear power system. It's important to control the dimension in the design of pressurizer through optimization techniques. In this work, a mathematic model of a vertical electric heating pressurizer was established. A new Genetic-Simplex Algorithm (GSA) that combines genetic algorithm and simplex algorithm was developed to enhance the searching ability, and the comparison among modified and original algorithms is conducted by calculating the benchmark function. Furthermore, the optimization design of pressurizer, taking minimization of volume and net weight as objectives, was carried out considering thermal-hydraulic and geometric constraints through GSA. The results indicate that the mathematical model is agreeable for the pressurizer and the new algorithm is more effective than the traditional genetic algorithm. The optimization design shows obvious validity and can provide guidance for real engineering design

Optimization algorithm based on densification and dynamic canonical descent

Science.gov (United States)

Bousson, K.; Correia, S. D.

2006-07-01

Stochastic methods have gained some popularity in global optimization in that most of them do not assume the cost functions to be differentiable. They have capabilities to avoid being trapped by local optima, and may converge even faster than gradient-based optimization methods on some problems. The present paper proposes an optimization method, which reduces the search space by means of densification curves, coupled with the dynamic canonical descent algorithm. The performances of the new method are shown on several known problems classically used for testing optimization algorithms, and proved to outperform competitive algorithms such as simulated annealing and genetic algorithms.

Genetic algorithms in loading pattern optimization

International Nuclear Information System (INIS)

Yilmazbayhan, A.; Tombakoglu, M.; Bekar, K. B.; Erdemli, A. Oe

2001-01-01

Genetic Algorithm (GA) based systems are used for the loading pattern optimization. The use of Genetic Algorithm operators such as regional crossover, crossover and mutation, and selection of initial population size for PWRs are discussed. Antithetic variates are used to generate the initial population. The performance of GA with antithetic variates is compared to traditional GA. The results of multi-cycle optimization are discussed for objective function taking into account cycle burn-up and discharge burn-up

Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

Directory of Open Access Journals (Sweden)

P. SrideviPonmalar

2017-01-01

Full Text Available Localization is one of the key issues in wireless sensor networks. Several algorithms and techniques have been introduced for localization. Localization is a procedural technique of estimating the sensor node location. In this paper, a novel three hybrid algorithms based on firefly is proposed for localization problem. Hybrid Genetic Algorithm-Firefly Localization Algorithm (GA-FFLA, Hybrid Differential Evolution-Firefly Localization Algorithm (DE-FFLA and Hybrid Particle Swarm Optimization -Firefly Localization Algorithm (PSO-FFLA are analyzed, designed and implemented to optimize the localization error. The localization algorithms are compared based on accuracy of estimation of location, time complexity and iterations required to achieve the accuracy. All the algorithms have hundred percent estimation accuracy but with variations in the number of firefliesr requirements, variation in time complexity and number of iteration requirements. Keywords: Localization; Genetic Algorithm; Differential Evolution; Particle Swarm Optimization

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.

Optimization of extracranial stereotactic radiation therapy of small lung lesions using accurate dose calculation algorithms

International Nuclear Information System (INIS)

Dobler, Barbara; Walter, Cornelia; Knopf, Antje; Fabri, Daniella; Loeschel, Rainer; Polednik, Martin; Schneider, Frank; Wenz, Frederik; Lohr, Frank

2006-01-01

The aim of this study was to compare and to validate different dose calculation algorithms for the use in radiation therapy of small lung lesions and to optimize the treatment planning using accurate dose calculation algorithms. A 9-field conformal treatment plan was generated on an inhomogeneous phantom with lung mimics and a soft tissue equivalent insert, mimicking a lung tumor. The dose distribution was calculated with the Pencil Beam and Collapsed Cone algorithms implemented in Masterplan (Nucletron) and the Monte Carlo system XVMC and validated using Gafchromic EBT films. Differences in dose distribution were evaluated. The plans were then optimized by adding segments to the outer shell of the target in order to increase the dose near the interface to the lung. The Pencil Beam algorithm overestimated the dose by up to 15% compared to the measurements. Collapsed Cone and Monte Carlo predicted the dose more accurately with a maximum difference of -8% and -3% respectively compared to the film. Plan optimization by adding small segments to the peripheral parts of the target, creating a 2-step fluence modulation, allowed to increase target coverage and homogeneity as compared to the uncorrected 9 field plan. The use of forward 2-step fluence modulation in radiotherapy of small lung lesions allows the improvement of tumor coverage and dose homogeneity as compared to non-modulated treatment plans and may thus help to increase the local tumor control probability. While the Collapsed Cone algorithm is closer to measurements than the Pencil Beam algorithm, both algorithms are limited at tissue/lung interfaces, leaving Monte-Carlo the most accurate algorithm for dose prediction

Quality assurance for high dose rate brachytherapy treatment planning optimization: using a simple optimization to verify a complex optimization

International Nuclear Information System (INIS)

Deufel, Christopher L; Furutani, Keith M

2014-01-01

As dose optimization for high dose rate brachytherapy becomes more complex, it becomes increasingly important to have a means of verifying that optimization results are reasonable. A method is presented for using a simple optimization as quality assurance for the more complex optimization algorithms typically found in commercial brachytherapy treatment planning systems. Quality assurance tests may be performed during commissioning, at regular intervals, and/or on a patient specific basis. A simple optimization method is provided that optimizes conformal target coverage using an exact, variance-based, algebraic approach. Metrics such as dose volume histogram, conformality index, and total reference air kerma agree closely between simple and complex optimizations for breast, cervix, prostate, and planar applicators. The simple optimization is shown to be a sensitive measure for identifying failures in a commercial treatment planning system that are possibly due to operator error or weaknesses in planning system optimization algorithms. Results from the simple optimization are surprisingly similar to the results from a more complex, commercial optimization for several clinical applications. This suggests that there are only modest gains to be made from making brachytherapy optimization more complex. The improvements expected from sophisticated linear optimizations, such as PARETO methods, will largely be in making systems more user friendly and efficient, rather than in finding dramatically better source strength distributions. (paper)

Development of independent MU/treatment time verification algorithm for non-IMRT treatment planning: A clinical experience

Science.gov (United States)

Tatli, Hamza; Yucel, Derya; Yilmaz, Sercan; Fayda, Merdan

2018-02-01

The aim of this study is to develop an algorithm for independent MU/treatment time (TT) verification for non-IMRT treatment plans, as a part of QA program to ensure treatment delivery accuracy. Two radiotherapy delivery units and their treatment planning systems (TPS) were commissioned in Liv Hospital Radiation Medicine Center, Tbilisi, Georgia. Beam data were collected according to vendors' collection guidelines, and AAPM reports recommendations, and processed by Microsoft Excel during in-house algorithm development. The algorithm is designed and optimized for calculating SSD and SAD treatment plans, based on AAPM TG114 dose calculation recommendations, coded and embedded in MS Excel spreadsheet, as a preliminary verification algorithm (VA). Treatment verification plans were created by TPSs based on IAEA TRS 430 recommendations, also calculated by VA, and point measurements were collected by solid water phantom, and compared. Study showed that, in-house VA can be used for non-IMRT plans MU/TT verifications.

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

International Nuclear Information System (INIS)

Tian, Zhen; Folkerts, Michael; Tan, Jun; Jia, Xun; Jiang, Steve B.; Peng, Fei

2015-01-01

Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Tian, Zhen, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Folkerts, Michael; Tan, Jun; Jia, Xun, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Jiang, Steve B., E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu [Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390 (United States); Peng, Fei [Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 (United States)

2015-06-15

Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is

Optimization of Algorithms Using Extensions of Dynamic Programming

KAUST Repository

AbouEisha, Hassan M.

2017-04-09

We study and answer questions related to the complexity of various important problems such as: multi-frontal solvers of hp-adaptive finite element method, sorting and majority. We advocate the use of dynamic programming as a viable tool to study optimal algorithms for these problems. The main approach used to attack these problems is modeling classes of algorithms that may solve this problem using a discrete model of computation then defining cost functions on this discrete structure that reflect different complexity measures of the represented algorithms. As a last step, dynamic programming algorithms are designed and used to optimize those models (algorithms) and to obtain exact results on the complexity of the studied problems. The first part of the thesis presents a novel model of computation (element partition tree) that represents a class of algorithms for multi-frontal solvers along with cost functions reflecting various complexity measures such as: time and space. It then introduces dynamic programming algorithms for multi-stage and bi-criteria optimization of element partition trees. In addition, it presents results based on optimal element partition trees for famous benchmark meshes such as: meshes with point and edge singularities. New improved heuristics for those benchmark meshes were ob- tained based on insights of the optimal results found by our algorithms. The second part of the thesis starts by introducing a general problem where different problems can be reduced to and show how to use a decision table to model such problem. We describe how decision trees and decision tests for this table correspond to adaptive and non-adaptive algorithms for the original problem. We present exact bounds on the average time complexity of adaptive algorithms for the eight elements sorting problem. Then bounds on adaptive and non-adaptive algorithms for a variant of the majority problem are introduced. Adaptive algorithms are modeled as decision trees whose depth

A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

International Nuclear Information System (INIS)

Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

2014-01-01

Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)

Space mapping optimization algorithms for engineering design

DEFF Research Database (Denmark)

Koziel, Slawomir; Bandler, John W.; Madsen, Kaj

2006-01-01

A simple, efficient optimization algorithm based on space mapping (SM) is presented. It utilizes input SM to reduce the misalignment between the coarse and fine models of the optimized object over a region of interest, and output space mapping (OSM) to ensure matching of response and first...... to a benchmark problem. In comparison with SMIS, the models presented are simple and have a small number of parameters that need to be extracted. The new algorithm is applied to the optimization of coupled-line band-pass filter....

Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization

Directory of Open Access Journals (Sweden)

Peng Wang

2013-01-01

Full Text Available This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO. The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions.

Optimizing graph algorithms on pregel-like systems

KAUST Repository

Salihoglu, Semih

2014-03-01

We study the problem of implementing graph algorithms efficiently on Pregel-like systems, which can be surprisingly challenging. Standard graph algorithms in this setting can incur unnecessary inefficiencies such as slow convergence or high communication or computation cost, typically due to structural properties of the input graphs such as large diameters or skew in component sizes. We describe several optimization techniques to address these inefficiencies. Our most general technique is based on the idea of performing some serial computation on a tiny fraction of the input graph, complementing Pregel\\'s vertex-centric parallelism. We base our study on thorough implementations of several fundamental graph algorithms, some of which have, to the best of our knowledge, not been implemented on Pregel-like systems before. The algorithms and optimizations we describe are fully implemented in our open-source Pregel implementation. We present detailed experiments showing that our optimization techniques improve runtime significantly on a variety of very large graph datasets.

Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

OpenAIRE

Yumin, Dong; Li, Zhao

2014-01-01

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the a...

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.

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

Directory of Open Access Journals (Sweden)

R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Short-Term Wind Speed Forecasting Using Support Vector Regression Optimized by Cuckoo Optimization Algorithm

Directory of Open Access Journals (Sweden)

Jianzhou Wang

2015-01-01

Full Text Available This paper develops an effectively intelligent model to forecast short-term wind speed series. A hybrid forecasting technique is proposed based on recurrence plot (RP and optimized support vector regression (SVR. Wind caused by the interaction of meteorological systems makes itself extremely unsteady and difficult to forecast. To understand the wind system, the wind speed series is analyzed using RP. Then, the SVR model is employed to forecast wind speed, in which the input variables are selected by RP, and two crucial parameters, including the penalties factor and gamma of the kernel function RBF, are optimized by various optimization algorithms. Those optimized algorithms are genetic algorithm (GA, particle swarm optimization algorithm (PSO, and cuckoo optimization algorithm (COA. Finally, the optimized SVR models, including COA-SVR, PSO-SVR, and GA-SVR, are evaluated based on some criteria and a hypothesis test. The experimental results show that (1 analysis of RP reveals that wind speed has short-term predictability on a short-term time scale, (2 the performance of the COA-SVR model is superior to that of the PSO-SVR and GA-SVR methods, especially for the jumping samplings, and (3 the COA-SVR method is statistically robust in multi-step-ahead prediction and can be applied to practical wind farm applications.

Algorithm comparison for schedule optimization in MR fingerprinting.

Science.gov (United States)

Cohen, Ouri; Rosen, Matthew S

2017-09-01

In MR Fingerprinting, the flip angles and repetition times are chosen according to a pseudorandom schedule. In previous work, we have shown that maximizing the discrimination between different tissue types by optimizing the acquisition schedule allows reductions in the number of measurements required. The ideal optimization algorithm for this application remains unknown, however. In this work we examine several different optimization algorithms to determine the one best suited for optimizing MR Fingerprinting acquisition schedules. Copyright © 2017 Elsevier Inc. All rights reserved.

Optimal partial-arcs in VMAT treatment planning

International Nuclear Information System (INIS)

Wala, Jeremiah; Salari, Ehsan; Chen Wei; Craft, David

2012-01-01

We present a method for improving the delivery efficiency of VMAT by extending the recently published VMAT treatment planning algorithm vmerge to automatically generate optimal partial-arc plans. A high-quality initial plan is created by solving a convex multicriteria optimization problem using 180 equi-spaced beams. This initial plan is used to form a set of dose constraints, and a set of partial-arc plans is created by searching the space of all possible partial-arc plans that satisfy these constraints. For each partial-arc, an iterative fluence map merging and sequencing algorithm (vmerge) is used to improve the delivery efficiency. Merging continues as long as the dose quality is maintained above a user-defined threshold. The final plan is selected as the partial-arc with the lowest treatment time. The complete algorithm is called pmerge. Partial-arc plans are created using pmerge for a lung, liver and prostate case, with final treatment times of 127, 245 and 147 s. Treatment times using full arcs with vmerge are 211, 357 and 178 s. The mean doses to the critical structures for the vmerge and pmerge plans are kept within 5% of those in the initial plan, and the target volume covered by the prescription isodose is maintained above 98% for the pmerge and vmerge plans. Additionally, we find that the angular distribution of fluence in the initial plans is predictive of the start and end angles of the optimal partial-arc. We conclude that VMAT delivery efficiency can be improved by employing partial-arcs without compromising dose quality, and that partial-arcs are most applicable to cases with non-centralized targets. (paper)

A chaos wolf optimization algorithm with self-adaptive variable step-size

Science.gov (United States)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance

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Zhiwen ZU

2018-04-01

Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.

Economic dispatch optimization algorithm based on particle diffusion

International Nuclear Information System (INIS)

Han, Li; Romero, Carlos E.; Yao, Zheng

2015-01-01

Highlights: • A dispatch model that considers fuel, emissions control and wind power cost is built. • An optimization algorithm named diffusion particle optimization (DPO) is proposed. • DPO was used to analyze the impact of wind power risk and emissions on dispatch. - Abstract: Due to the widespread installation of emissions control equipment in fossil fuel-fired power plants, the cost of emissions control needs to be considered, together with the plant fuel cost, in providing economic power dispatch of those units to the grid. On the other hand, while using wind power decreases the overall power generation cost for the power grid, it poses a risk to a traditional grid, because of its inherent stochastic characteristics. Therefore, an economic dispatch optimization model needs to consider all of the fuel cost, emissions control cost and wind power cost for each of the generating unit conforming the fleet that meets the required grid power demand. In this study, an optimization algorithm referred as diffusion particle optimization (DPO) is proposed to solve such complex optimization problem. In this algorithm, Brownian motion theory is used to guide the movement of particles so that the particles can search for an optimal solution over the entire definition region. Several benchmark functions and power grid system data were used to test the performance of DPO, and compared to traditional algorithms used for economic dispatch optimization, such as, particle swarm optimization and artificial bee colony algorithm. It was found that DPO has less probability to be trapped in local optimums. According to results of different power systems DPO was able to find economic dispatch solutions with lower costs. DPO was also used to analyze the impact of wind power risk and fossil unit emissions coefficients on power dispatch. The result are encouraging for the use of DPO as a dynamic tool for economic dispatch of the power grid.

Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

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Xun Zhang

2014-01-01

Full Text Available Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

An algorithm for online optimization of accelerators

Energy Technology Data Exchange (ETDEWEB)

Huang, Xiaobiao [SLAC National Accelerator Lab., Menlo Park, CA (United States); Corbett, Jeff [SLAC National Accelerator Lab., Menlo Park, CA (United States); Safranek, James [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wu, Juhao [SLAC National Accelerator Lab., Menlo Park, CA (United States)

2013-10-01

We developed a general algorithm for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. Moreover, it is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.

Optimal reservoir operation policies using novel nested algorithms

Science.gov (United States)

Delipetrev, Blagoj; Jonoski, Andreja; Solomatine, Dimitri

2015-04-01

Historically, the two most widely practiced methods for optimal reservoir operation have been dynamic programming (DP) and stochastic dynamic programming (SDP). These two methods suffer from the so called "dual curse" which prevents them to be used in reasonably complex water systems. The first one is the "curse of dimensionality" that denotes an exponential growth of the computational complexity with the state - decision space dimension. The second one is the "curse of modelling" that requires an explicit model of each component of the water system to anticipate the effect of each system's transition. We address the problem of optimal reservoir operation concerning multiple objectives that are related to 1) reservoir releases to satisfy several downstream users competing for water with dynamically varying demands, 2) deviations from the target minimum and maximum reservoir water levels and 3) hydropower production that is a combination of the reservoir water level and the reservoir releases. Addressing such a problem with classical methods (DP and SDP) requires a reasonably high level of discretization of the reservoir storage volume, which in combination with the required releases discretization for meeting the demands of downstream users leads to computationally expensive formulations and causes the curse of dimensionality. We present a novel approach, named "nested" that is implemented in DP, SDP and reinforcement learning (RL) and correspondingly three new algorithms are developed named nested DP (nDP), nested SDP (nSDP) and nested RL (nRL). The nested algorithms are composed from two algorithms: 1) DP, SDP or RL and 2) nested optimization algorithm. Depending on the way we formulate the objective function related to deficits in the allocation problem in the nested optimization, two methods are implemented: 1) Simplex for linear allocation problems, and 2) quadratic Knapsack method in the case of nonlinear problems. The novel idea is to include the nested

Application of colony complex algorithm to nuclear component optimization design

International Nuclear Information System (INIS)

Yan Changqi; Li Guijing; Wang Jianjun

2014-01-01

Complex algorithm (CA) has got popular application to the region of nuclear engineering. In connection with the specific features of the application of traditional complex algorithm (TCA) to the optimization design in engineering structures, an improved method, colony complex algorithm (CCA), was developed based on the optimal combination of many complexes, in which the disadvantages of TCA were overcame. The optimized results of benchmark function show that CCA has better optimizing performance than TCA. CCA was applied to the high-pressure heater optimization design, and the optimization effect is obvious. (authors)

A new hybrid optimization algorithm CRO-DE for optimal coordination of overcurrent relays in complex power systems

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Mohamed Zellagui

2017-09-01

Full Text Available The paper presents a new hybrid global optimization algorithm based on Chemical Reaction based Optimization (CRO and Di¤erential evolution (DE algorithm for nonlinear constrained optimization problems. This approach proposed for the optimal coordination and setting relays of directional overcurrent relays in complex power systems. In protection coordination problem, the objective function to be minimized is the sum of the operating time of all main relays. The optimization problem is subject to a number of constraints which are mainly focused on the operation of the backup relay, which should operate if a primary relay fails to respond to the fault near to it, Time Dial Setting (TDS, Plug Setting (PS and the minimum operating time of a relay. The hybrid global proposed optimization algorithm aims to minimize the total operating time of each protection relay. Two systems are used as case study to check the effeciency of the optimization algorithm which are IEEE 4-bus and IEEE 6-bus models. Results are obtained and presented for CRO and DE and hybrid CRO-DE algorithms. The obtained results for the studied cases are compared with those results obtained when using other optimization algorithms which are Teaching Learning-Based Optimization (TLBO, Chaotic Differential Evolution Algorithm (CDEA and Modiffied Differential Evolution Algorithm (MDEA, and Hybrid optimization algorithms (PSO-DE, IA-PSO, and BFOA-PSO. From analysing the obtained results, it has been concluded that hybrid CRO-DO algorithm provides the most optimum solution with the best convergence rate.

Improved hybrid optimization algorithm for 3D protein structure prediction.

Science.gov (United States)

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

PWR loading pattern optimization using Harmony Search algorithm

International Nuclear Information System (INIS)

Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.

2013-01-01

Highlights: ► Numerical results reveal that the HS method is reliable. ► The great advantage of HS is significant gain in computational cost. ► On the average, the final band width of search fitness values is narrow. ► Our experiments show that the search approaches the optimal value fast. - Abstract: In this paper a core reloading technique using Harmony Search, HS, is presented in the context of finding an optimal configuration of fuel assemblies, FA, in pressurized water reactors. To implement and evaluate the proposed technique a Harmony Search along Nodal Expansion Code for 2-D geometry, HSNEC2D, is developed to obtain nearly optimal arrangement of fuel assemblies in PWR cores. This code consists of two sections including Harmony Search algorithm and Nodal Expansion modules using fourth degree flux expansion which solves two dimensional-multi group diffusion equations with one node per fuel assembly. Two optimization test problems are investigated to demonstrate the HS algorithm capability in converging to near optimal loading pattern in the fuel management field and other subjects. Results, convergence rate and reliability of the method are quite promising and show the HS algorithm performs very well and is comparable to other competitive algorithms such as Genetic Algorithm and Particle Swarm Intelligence. Furthermore, implementation of nodal expansion technique along HS causes considerable reduction of computational time to process and analysis optimization in the core fuel management problems

The Optimal Wavelengths for Light Absorption Spectroscopy Measurements Based on Genetic Algorithm-Particle Swarm Optimization

Science.gov (United States)

Tang, Ge; Wei, Biao; Wu, Decao; Feng, Peng; Liu, Juan; Tang, Yuan; Xiong, Shuangfei; Zhang, Zheng

2018-03-01

To select the optimal wavelengths in the light extinction spectroscopy measurement, genetic algorithm-particle swarm optimization (GAPSO) based on genetic algorithm (GA) and particle swarm optimization (PSO) is adopted. The change of the optimal wavelength positions in different feature size parameters and distribution parameters is evaluated. Moreover, the Monte Carlo method based on random probability is used to identify the number of optimal wavelengths, and good inversion effects of the particle size distribution are obtained. The method proved to have the advantage of resisting noise. In order to verify the feasibility of the algorithm, spectra with bands ranging from 200 to 1000 nm are computed. Based on this, the measured data of standard particles are used to verify the algorithm.

Multi objective genetic algorithm to optimize the local heat treatment of a hardenable aluminum alloy

Science.gov (United States)

Piccininni, A.; Palumbo, G.; Franco, A. Lo; Sorgente, D.; Tricarico, L.; Russello, G.

2018-05-01

The continuous research for lightweight components for transport applications to reduce the harmful emissions drives the attention to the light alloys as in the case of Aluminium (Al) alloys, capable to combine low density with high values of the strength-to-weight ratio. Such advantages are partially counterbalanced by the poor formability at room temperature. A viable solution is to adopt a localized heat treatment by laser of the blank before the forming process to obtain a tailored distribution of material properties so that the blank can be formed at room temperature by means of conventional press machines. Such an approach has been extensively investigated for age hardenable alloys, but in the present work the attention is focused on the 5000 series; in particular, the optimization of the deep drawing process of the alloy AA5754 H32 is proposed through a numerical/experimental approach. A preliminary investigation was necessary to correctly tune the laser parameters (focus length, spot dimension) to effectively obtain the annealed state. Optimal process parameters were then obtained coupling a 2D FE model with an optimization platform managed by a multi-objective genetic algorithm. The optimal solution (i.e. able to maximize the LDR) in terms of blankholder force and extent of the annealed region was thus evaluated and validated through experimental trials. A good matching between experimental and numerical results was found. The optimal solution allowed to obtain an LDR of the locally heat treated blank larger than the one of the material either in the wrought condition (H32) either in the annealed condition (H111).

An Ameliorative Whale Optimization Algorithm for Multi-Objective Optimal Allocation of Water Resources in Handan, China

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Zhihong Yan

2018-01-01

Full Text Available With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. How to allocate water resources optimally, rapidly, and effectively has become a challenging problem. Thus, this study employs a meta-heuristic swarm-based algorithm, the whale optimization algorithm (WOA. To overcome drawbacks like relatively low convergence precision and convergence rates, when applying the WOA algorithm to complex optimization problems, logistic mapping is used to initialize swarm location, and inertia weighting is employed to improve the algorithm. The resulting ameliorative whale optimization algorithm (AWOA shows substantially enhanced convergence rates and precision than the WOA and particle swarm optimization algorithms, demonstrating relatively high reliability and applicability. A water resource allocation optimization model with optimal economic efficiency and least total water shortage volume is established for Handan, China, and solved by the AWOA. The allocation results better reflect actual water usage in Handan. In 2030, the p = 50% total water shortage is forecast as 404.34 × 106 m3 or 14.8%. The shortage is mainly in the primary agricultural sector. The allocation results provide a reference for regional water resources management.

A Combined Algorithm for Optimization: Application for Optimization of the Transition Gas-Liquid in Stirred Tank Bioreactors

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Mitko Petrov

2005-12-01

Full Text Available A combined algorithm for static optimization is developed. The algorithm includes a method for random search of optimal an initial point and a method based on fuzzy sets theory, combined in order to be found for the best solution of the optimization problem. The application of the combined algorithm eliminates the main disadvantage of the used fuzzy optimization method, namely decreases the number of discrete values of control variables. In this way, the algorithm allows problems with larger scale to be solved. The combined algorithm is used for optimization of gas-liquid transition in dependence on some constructive and regime parameters of a laboratory scale stirred tank bioreactor. After the application of developed optimization algorithm significant increase of mass-transfer effectiveness, aeration and mixing processes in the bioreactor are observed.

Optimal Quadratic Programming Algorithms

CERN Document Server

Dostal, Zdenek

2009-01-01

Quadratic programming (QP) is one technique that allows for the optimization of a quadratic function in several variables in the presence of linear constraints. This title presents various algorithms for solving large QP problems. It is suitable as an introductory text on quadratic programming for graduate students and researchers

Cloud Service Scheduling Algorithm Research and Optimization

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Hongyan Cui

2017-01-01

Full Text Available We propose a cloud service scheduling model that is referred to as the Task Scheduling System (TSS. In the user module, the process time of each task is in accordance with a general distribution. In the task scheduling module, we take a weighted sum of makespan and flowtime as the objective function and use an Ant Colony Optimization (ACO and a Genetic Algorithm (GA to solve the problem of cloud task scheduling. Simulation results show that the convergence speed and output performance of our Genetic Algorithm-Chaos Ant Colony Optimization (GA-CACO are optimal.

Gems of combinatorial optimization and graph algorithms

CERN Document Server

Skutella, Martin; Stiller, Sebastian; Wagner, Dorothea

2015-01-01

Are you looking for new lectures for your course on algorithms, combinatorial optimization, or algorithmic game theory?  Maybe you need a convenient source of relevant, current topics for a graduate student or advanced undergraduate student seminar?  Or perhaps you just want an enjoyable look at some beautiful mathematical and algorithmic results, ideas, proofs, concepts, and techniques in discrete mathematics and theoretical computer science?   Gems of Combinatorial Optimization and Graph Algorithms is a handpicked collection of up-to-date articles, carefully prepared by a select group of international experts, who have contributed some of their most mathematically or algorithmically elegant ideas.  Topics include longest tours and Steiner trees in geometric spaces, cartograms, resource buying games, congestion games, selfish routing, revenue equivalence and shortest paths, scheduling, linear structures in graphs, contraction hierarchies, budgeted matching problems, and motifs in networks.   This ...

Multiphase Return Trajectory Optimization Based on Hybrid Algorithm

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Yi Yang

2016-01-01

Full Text Available A hybrid trajectory optimization method consisting of Gauss pseudospectral method (GPM and natural computation algorithm has been developed and utilized to solve multiphase return trajectory optimization problem, where a phase is defined as a subinterval in which the right-hand side of the differential equation is continuous. GPM converts the optimal control problem to a nonlinear programming problem (NLP, which helps to improve calculation accuracy and speed of natural computation algorithm. Through numerical simulations, it is found that the multiphase optimal control problem could be solved perfectly.

A chaos wolf optimization algorithm with self-adaptive variable step-size

Directory of Open Access Journals (Sweden)

Yong Zhu

2017-10-01

Full Text Available To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as “winner-take-all” and the update mechanism as “survival of the fittest” were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

Science.gov (United States)

Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

2017-04-01

Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

Kriging-based algorithm for nuclear reactor neutronic design optimization

International Nuclear Information System (INIS)

Kempf, Stephanie; Forget, Benoit; Hu, Lin-Wen

2012-01-01

Highlights: ► A Kriging-based algorithm was selected to guide research reactor optimization. ► We examined impacts of parameter values upon the algorithm. ► The best parameter values were incorporated into a set of best practices. ► Algorithm with best practices used to optimize thermal flux of concept. ► Final design produces thermal flux 30% higher than other 5 MW reactors. - Abstract: Kriging, a geospatial interpolation technique, has been used in the present work to drive a search-and-optimization algorithm which produces the optimum geometric parameters for a 5 MW research reactor design. The technique has been demonstrated to produce an optimal neutronic solution after a relatively small number of core calculations. It has additionally been successful in producing a design which significantly improves thermal neutron fluxes by 30% over existing reactors of the same power rating. Best practices for use of this algorithm in reactor design were identified and indicated the importance of selecting proper correlation functions.

Optimization of Gamma Knife treatment planning via guided evolutionary simulated annealing

International Nuclear Information System (INIS)

Zhang Pengpeng; Dean, David; Metzger, Andrew; Sibata, Claudio

2001-01-01

We present a method for generating optimized Gamma Knife trade mark sign (Elekta, Stockholm, Sweden) radiosurgery treatment plans. This semiautomatic method produces a highly conformal shot packing plan for the irradiation of an intracranial tumor. We simulate optimal treatment planning criteria with a probability function that is linked to every voxel in a volumetric (MR or CT) region of interest. This sigmoidal P + parameter models the requirement of conformality (i.e., tumor ablation and normal tissue sparing). After determination of initial radiosurgery treatment parameters, a guided evolutionary simulated annealing (GESA) algorithm is used to find the optimal size, position, and weight for each shot. The three-dimensional GESA algorithm searches the shot parameter space more thoroughly than is possible during manual shot packing and provides one plan that is suitable to the treatment criteria of the attending neurosurgeon and radiation oncologist. The result is a more conformal plan, which also reduces redundancy, and saves treatment administration time

Characteristic statistic algorithm (CSA) for in-core loading pattern optimization

International Nuclear Information System (INIS)

Liu Zhihong; Hu Yongming; Shi Gong

2007-01-01

To solve the problem of PWR in-core loading pattern optimization, a more suitable global optimization algorithm, i.e., Characteristic statistic algorithm (CSA), is used. The searching process of this algorithm and how to apply it to this problem are presented. Loading pattern optimization code SCYCLE is developed. Two different problems on real PWR models are calculated and the results are compared with other algorithms. It is shown that SCYCLE has high efficiency and good global performance on this problem. (authors)

Application of Grey Wolf Optimizer Algorithm for Optimal Power Flow of Two-Terminal HVDC Transmission System

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Heba Ahmed Hassan

2017-01-01

Full Text Available This paper applies a relatively new optimization method, the Grey Wolf Optimizer (GWO algorithm for Optimal Power Flow (OPF of two-terminal High Voltage Direct Current (HVDC electrical power system. The OPF problem of pure AC power systems considers the minimization of total costs under equality and inequality constraints. Hence, the OPF problem of integrated AC-DC power systems is extended to incorporate HVDC links, while taking into consideration the power transfer control characteristics using a GWO algorithm. This algorithm is inspired by the hunting behavior and social leadership of grey wolves in nature. The proposed algorithm is applied to two different case-studies: the modified 5-bus and WSCC 9-bus test systems. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization techniques. Analysis of the obtained results show that the proposed GWO algorithm is able to achieve shorter CPU time, as well as minimized total cost when compared with already existing optimization techniques. This conclusion proves the efficiency of the GWO algorithm.

A quality and efficiency analysis of the IMFASTTM segmentation algorithm in head and neck 'step and shoot' IMRT treatments

International Nuclear Information System (INIS)

Potter, Larry D.; Chang, Sha X.; Cullip, Timothy J.; Siochi, Alfredo C.

2002-01-01

The performance of segmentation algorithms used in IMFAST for 'step and shoot' IMRT treatment delivery is evaluated for three head and neck clinical treatments of different optimization objectives. The segmentation uses the intensity maps generated by the in-house TPS PLANUNC using the index-dose minimization algorithm. The dose optimization objectives include PTV dose uniformity and dose volume histogram-specified critical structure sparing. The optimized continuous intensity maps were truncated into five and ten intensity levels and exported to IMFAST for MLC segments optimization. The MLC segments were imported back to PLUNC for dose optimization quality calculation. The five basic segmentation algorithms included in IMFAST were evaluated alone and in combination with either tongue and groove/match line correction or fluence correction or both. Two criteria were used in the evaluation: treatment efficiency represented by the total number of MLC segments and optimization quality represented by a clinically relevant optimization quality factor. We found that the treatment efficiency depends first on the number of intensity levels used in the intensity map and second the segmentation technique used. The standard optimal segmentation with fluence correction is a consistent good performer for all treatment plans studied. All segmentation techniques evaluated produced treatments with similar dose optimization quality values, especially when ten-level intensity maps are used

Differential harmony search algorithm to optimize PWRs loading pattern

Energy Technology Data Exchange (ETDEWEB)

Poursalehi, N., E-mail: npsalehi@yahoo.com [Engineering Department, Shahid Beheshti University, G.C, P.O.Box: 1983963113, Tehran (Iran, Islamic Republic of); Zolfaghari, A.; Minuchehr, A. [Engineering Department, Shahid Beheshti University, G.C, P.O.Box: 1983963113, Tehran (Iran, Islamic Republic of)

2013-04-15

Highlights: ► Exploit of DHS algorithm in LP optimization reveals its flexibility, robustness and reliability. ► Upshot of our experiments with DHS shows that the search approach to optimal LP is quickly. ► On the average, the final band width of DHS fitness values is narrow relative to HS and GHS. -- Abstract: The objective of this work is to develop a core loading optimization technique using differential harmony search algorithm in the context of obtaining an optimal configuration of fuel assemblies in pressurized water reactors. To implement and evaluate the proposed technique, differential harmony search nodal expansion package for 2-D geometry, DHSNEP-2D, is developed. The package includes two modules; in the first modules differential harmony search (DHS) is implemented and nodal expansion code which solves two dimensional-multi group neutron diffusion equations using fourth degree flux expansion with one node per a fuel assembly is in the second module. For evaluation of DHS algorithm, classical harmony search (HS) and global-best harmony search (GHS) algorithms are also included in DHSNEP-2D in order to compare the outcome of techniques together. For this purpose, two PWR test cases have been investigated to demonstrate the DHS algorithm capability in obtaining near optimal loading pattern. Results show that the convergence rate of DHS and execution times are quite promising and also is reliable for the fuel management operation. Moreover, numerical results show the good performance of DHS relative to other competitive algorithms such as genetic algorithm (GA), classical harmony search (HS) and global-best harmony search (GHS) algorithms.

Differential harmony search algorithm to optimize PWRs loading pattern

International Nuclear Information System (INIS)

Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.

2013-01-01

Highlights: ► Exploit of DHS algorithm in LP optimization reveals its flexibility, robustness and reliability. ► Upshot of our experiments with DHS shows that the search approach to optimal LP is quickly. ► On the average, the final band width of DHS fitness values is narrow relative to HS and GHS. -- Abstract: The objective of this work is to develop a core loading optimization technique using differential harmony search algorithm in the context of obtaining an optimal configuration of fuel assemblies in pressurized water reactors. To implement and evaluate the proposed technique, differential harmony search nodal expansion package for 2-D geometry, DHSNEP-2D, is developed. The package includes two modules; in the first modules differential harmony search (DHS) is implemented and nodal expansion code which solves two dimensional-multi group neutron diffusion equations using fourth degree flux expansion with one node per a fuel assembly is in the second module. For evaluation of DHS algorithm, classical harmony search (HS) and global-best harmony search (GHS) algorithms are also included in DHSNEP-2D in order to compare the outcome of techniques together. For this purpose, two PWR test cases have been investigated to demonstrate the DHS algorithm capability in obtaining near optimal loading pattern. Results show that the convergence rate of DHS and execution times are quite promising and also is reliable for the fuel management operation. Moreover, numerical results show the good performance of DHS relative to other competitive algorithms such as genetic algorithm (GA), classical harmony search (HS) and global-best harmony search (GHS) algorithms

Reactive power dispatch considering voltage stability with seeker optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Dai, Chaohua; Chen, Weirong; Zhang, Xuexia [The School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031 (China); Zhu, Yunfang [Department of Computer and Communication Engineering, E' mei Campus, Southwest Jiaotong University, E' mei 614202 (China)

2009-10-15

Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem. (author)

Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Wenping Zou

2011-01-01

Full Text Available Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

A hybrid algorithm for instant optimization of beam weights in anatomy-based intensity modulated radiotherapy: a performance evaluation study

International Nuclear Information System (INIS)

Vaitheeswaran, Ranganathan; Sathiya Narayanan, V.K.; Bhangle, Janhavi R.; Nirhali, Amit; Kumar, Namita; Basu, Sumit; Maiya, Vikram

2011-01-01

The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) (∼ 2% - 5% improvement) and Homogeneity Index (HI) (∼ 4% - 10% improvement) as compared to GEM and FSA algorithms (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are

On algorithm for building of optimal α-decision trees

KAUST Repository

Alkhalid, Abdulaziz

2010-01-01

The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.

Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm

International Nuclear Information System (INIS)

Rao, R. Venkata; Rai, Dhiraj P.

2017-01-01

Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO).

Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm

Energy Technology Data Exchange (ETDEWEB)

Rao, R. Venkata; Rai, Dhiraj P. [Sardar Vallabhbhai National Institute of Technology, Gujarat (India)

2017-05-15

Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO).

Quantum approximate optimization algorithm for MaxCut: A fermionic view

Science.gov (United States)

Wang, Zhihui; Hadfield, Stuart; Jiang, Zhang; Rieffel, Eleanor G.

2018-02-01

Farhi et al. recently proposed a class of quantum algorithms, the quantum approximate optimization algorithm (QAOA), for approximately solving combinatorial optimization problems (E. Farhi et al., arXiv:1411.4028; arXiv:1412.6062; arXiv:1602.07674). A level-p QAOA circuit consists of p steps; in each step a classical Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. The 2 p times for which these two Hamiltonians are applied are the parameters of the algorithm, which are to be optimized classically for the best performance. As p increases, parameter optimization becomes inefficient due to the curse of dimensionality. The success of the QAOA approach will depend, in part, on finding effective parameter-setting strategies. Here we analytically and numerically study parameter setting for the QAOA applied to MaxCut. For the level-1 QAOA, we derive an analytical expression for a general graph. In principle, expressions for higher p could be derived, but the number of terms quickly becomes prohibitive. For a special case of MaxCut, the "ring of disagrees," or the one-dimensional antiferromagnetic ring, we provide an analysis for an arbitrarily high level. Using a fermionic representation, the evolution of the system under the QAOA translates into quantum control of an ensemble of independent spins. This treatment enables us to obtain analytical expressions for the performance of the QAOA for any p . It also greatly simplifies the numerical search for the optimal values of the parameters. By exploring symmetries, we identify a lower-dimensional submanifold of interest; the search effort can be accordingly reduced. This analysis also explains an observed symmetry in the optimal parameter values. Further, we numerically investigate the parameter landscape and show that it is a simple one in the sense of having no local optima.

A genetic-algorithm-aided stochastic optimization model for regional air quality management under uncertainty.

Science.gov (United States)

Qin, Xiaosheng; Huang, Guohe; Liu, Lei

2010-01-01

A genetic-algorithm-aided stochastic optimization (GASO) model was developed in this study for supporting regional air quality management under uncertainty. The model incorporated genetic algorithm (GA) and Monte Carlo simulation techniques into a general stochastic chance-constrained programming (CCP) framework and allowed uncertainties in simulation and optimization model parameters to be considered explicitly in the design of least-cost strategies. GA was used to seek the optimal solution of the management model by progressively evaluating the performances of individual solutions. Monte Carlo simulation was used to check the feasibility of each solution. A management problem in terms of regional air pollution control was studied to demonstrate the applicability of the proposed method. Results of the case study indicated the proposed model could effectively communicate uncertainties into the optimization process and generate solutions that contained a spectrum of potential air pollutant treatment options with risk and cost information. Decision alternatives could be obtained by analyzing tradeoffs between the overall pollutant treatment cost and the system-failure risk due to inherent uncertainties.

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.

Evolutionary Algorithm for Optimal Vaccination Scheme

International Nuclear Information System (INIS)

Parousis-Orthodoxou, K J; Vlachos, D S

2014-01-01

The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease

Study of dose calculation and beam parameters optimization with genetic algorithm in IMRT

International Nuclear Information System (INIS)

Chen Chaomin; Tang Mutao; Zhou Linghong; Lv Qingwen; Wang Zhuoyu; Chen Guangjie

2006-01-01

Objective: To study the construction of dose calculation model and the method of automatic beam parameters selection in IMRT. Methods: The three-dimension convolution dose calculation model of photon was constructed with the methods of Fast Fourier Transform. The objective function based on dose constrain was used to evaluate the fitness of individuals. The beam weights were optimized with genetic algorithm. Results: After 100 iterative analyses, the treatment planning system produced highly conformal and homogeneous dose distributions. Conclusion: the throe-dimension convolution dose calculation model of photon gave more accurate results than the conventional models; genetic algorithm is valid and efficient in IMRT beam parameters optimization. (authors)

Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture series

CERN Multimedia

Françoise Benz

2004-01-01

ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...

Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture serie

CERN Multimedia

Françoise Benz

2004-01-01

ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on nat...

Using neural networks to speed up optimization algorithms

CERN Document Server

Bazan, M

2000-01-01

The paper presents the application of radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables. (13 refs).

Portfolio optimization by using linear programing models based on genetic algorithm

Science.gov (United States)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

Online algorithms for optimal energy distribution in microgrids

CERN Document Server

Wang, Yu; Nelms, R Mark

2015-01-01

Presenting an optimal energy distribution strategy for microgrids in a smart grid environment, and featuring a detailed analysis of the mathematical techniques of convex optimization and online algorithms, this book provides readers with essential content on how to achieve multi-objective optimization that takes into consideration power subscribers, energy providers and grid smoothing in microgrids. Featuring detailed theoretical proofs and simulation results that demonstrate and evaluate the correctness and effectiveness of the algorithm, this text explains step-by-step how the problem can b

Current algorithm for the surgical treatment of facial pain

Directory of Open Access Journals (Sweden)

Munawar Naureen

2007-07-01

Full Text Available Background Facial pain may be divided into several distinct categories, each requiring a specific treatment approach. In some cases, however, such categorization is difficult and treatment is ineffective. We reviewed our extensive clinical experience and designed an algorithmic approach to the treatment of medically intractable facial pain that can be treated through surgical intervention. Methods Our treatment algorithm is based on taking into account underlying pathological processes, the anatomical distribution of pain, pain characteristics, the patient's age and medical condition, associated medical problems, the history of previous surgical interventions, and, in some cases, the results of psychological evaluation. The treatment modalities involved in this algorithm include diagnostic blocks, peripheral denervation procedures, craniotomy for microvascular decompression of cranial nerves, percutaneous rhizotomies using radiofrequency ablation, glycerol injection, balloon compression, peripheral nerve stimulation procedures, stereotactic radiosurgery, percutaneous trigeminal tractotomy, and motor cortex stimulation. We recommend that some patients not receive surgery at all, but rather be referred for other medical or psychological treatment. Results Our algorithmic approach was used in more than 100 consecutive patients with medically intractable facial pain. Clinical evaluations and diagnostic workups were followed in each case by the systematic choice of the appropriate intervention. The algorithm has proved easy to follow, and the recommendations include the identification of the optimal surgery for each patient with other options reserved for failures or recurrences. Our overall success rate in eliminating facial pain presently reaches 96%, which is higher than that observed in most clinical series reported to date Conclusion This treatment algorithm for the intractable facial pain appears to be effective for patients with a wide variety

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

Science.gov (United States)

Lopez, Nicolas

This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

A dynamic global and local combined particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Chen Qunxian

2009-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.

Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm

International Nuclear Information System (INIS)

Rao, R.V.; More, K.C.

2017-01-01

Highlights: • Self-adaptive Jaya algorithm is proposed for optimal design of thermal devices. • Optimization of heat pipe, cooling tower, heat sink and thermo-acoustic prime mover is presented. • Results of the proposed algorithm are better than the other optimization techniques. • The proposed algorithm may be conveniently used for the optimization of other devices. - Abstract: The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.

Optimization in optical systems revisited: Beyond genetic algorithms

Science.gov (United States)

Gagnon, Denis; Dumont, Joey; Dubé, Louis

2013-05-01

Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).

Algorithms for worst-case tolerance optimization

DEFF Research Database (Denmark)

Schjær-Jacobsen, Hans; Madsen, Kaj

1979-01-01

New algorithms are presented for the solution of optimum tolerance assignment problems. The problems considered are defined mathematically as a worst-case problem (WCP), a fixed tolerance problem (FTP), and a variable tolerance problem (VTP). The basic optimization problem without tolerances...... is denoted the zero tolerance problem (ZTP). For solution of the WCP we suggest application of interval arithmetic and also alternative methods. For solution of the FTP an algorithm is suggested which is conceptually similar to algorithms previously developed by the authors for the ZTP. Finally, the VTP...... is solved by a double-iterative algorithm in which the inner iteration is performed by the FTP- algorithm. The application of the algorithm is demonstrated by means of relatively simple numerical examples. Basic properties, such as convergence properties, are displayed based on the examples....

Applications of metaheuristic optimization algorithms in civil engineering

CERN Document Server

Kaveh, A

2017-01-01

The book presents recently developed efficient metaheuristic optimization algorithms and their applications for solving various optimization problems in civil engineering. The concepts can also be used for optimizing problems in mechanical and electrical engineering.

Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm

DEFF Research Database (Denmark)

Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar

2017-01-01

India's ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging...... stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context......, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Directory of Open Access Journals (Sweden)

Kerim Guney

2015-01-01

Full Text Available An evolutionary method based on backtracking search optimization algorithm (BSA is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO, genetic algorithm (GA, modified touring ant colony algorithm (MTACO, quadratic programming method (QPM, bacterial foraging algorithm (BFA, bees algorithm (BA, clonal selection algorithm (CLONALG, plant growth simulation algorithm (PGSA, tabu search algorithm (TSA, memetic algorithm (MA, nondominated sorting GA-2 (NSGA-2, multiobjective differential evolution (MODE, decomposition with differential evolution (MOEA/D-DE, comprehensive learning PSO (CLPSO, harmony search algorithm (HSA, seeker optimization algorithm (SOA, and mean variance mapping optimization (MVMO. The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

Performance indices and evaluation of algorithms in building energy efficient design optimization

International Nuclear Information System (INIS)

Si, Binghui; Tian, Zhichao; Jin, Xing; Zhou, Xin; Tang, Peng; Shi, Xing

2016-01-01

Building energy efficient design optimization is an emerging technique that is increasingly being used to design buildings with better overall performance and a particular emphasis on energy efficiency. To achieve building energy efficient design optimization, algorithms are vital to generate new designs and thus drive the design optimization process. Therefore, the performance of algorithms is crucial to achieving effective energy efficient design techniques. This study evaluates algorithms used for building energy efficient design optimization. A set of performance indices, namely, stability, robustness, validity, speed, coverage, and locality, is proposed to evaluate the overall performance of algorithms. A benchmark building and a design optimization problem are also developed. Hooke–Jeeves algorithm, Multi-Objective Genetic Algorithm II, and Multi-Objective Particle Swarm Optimization algorithm are evaluated by using the proposed performance indices and benchmark design problem. Results indicate that no algorithm performs best in all six areas. Therefore, when facing an energy efficient design problem, the algorithm must be carefully selected based on the nature of the problem and the performance indices that matter the most. - Highlights: • Six indices of algorithm performance in building energy optimization are developed. • For each index, its concept is defined and the calculation formulas are proposed. • A benchmark building and benchmark energy efficient design problem are proposed. • The performance of three selected algorithms are evaluated.

Hybrid particle swarm optimization algorithm and its application in nuclear engineering

International Nuclear Information System (INIS)

Liu, C.Y.; Yan, C.Q.; Wang, J.J.

2014-01-01

Highlights: • We propose a hybrid particle swarm optimization algorithm (HPSO). • Modified Nelder–Mead simplex search method is applied in HPSO. • The algorithm has a high search precision and rapidly calculation speed. • HPSO can be used in the nuclear engineering optimization design problems. - Abstract: A hybrid particle swarm optimization algorithm with a feasibility-based rule for solving constrained optimization problems has been developed in this research. Firstly, the global optimal solution zone can be obtained through particle swarm optimization process, and then the refined search of the global optimal solution will be achieved through the modified Nelder–Mead simplex algorithm. Simulations based on two well-studied benchmark problems demonstrate the proposed algorithm will be an efficient alternative to solving constrained optimization problems. The vertical electrical heating pressurizer is one of the key components in reactor coolant system. The mathematical model of pressurizer has been established in steady state. The optimization design of pressurizer weight has been carried out through HPSO algorithm. The results show the pressurizer weight can be reduced by 16.92%. The thermal efficiencies of conventional PWR nuclear power plants are about 31–35% so far, which are much lower than fossil fueled plants based in a steam cycle as PWR. The thermal equilibrium mathematic model for nuclear power plant secondary loop has been established. An optimization case study has been conducted to improve the efficiency of the nuclear power plant with the proposed algorithm. The results show the thermal efficiency is improved by 0.5%

Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network

OpenAIRE

Jing Wang; Yourui Huang

2013-01-01

In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...

The PBIL algorithm applied to a nuclear reactor design optimization

Energy Technology Data Exchange (ETDEWEB)

Machado, Marcelo D.; Medeiros, Jose A.C.C.; Lima, Alan M.M. de; Schirru, Roberto [Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia (COPPE/UFRJ-RJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear. Lab. de Monitoracao de Processos]. E-mails: marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; alan@lmp.ufrj.br; schirru@lmp.ufrj.br

2007-07-01

The Population-Based Incremental Learning (PBIL) algorithm is a method that combines the mechanism of genetic algorithm with the simple competitive learning, creating an important tool to be used in the optimization of numeric functions and combinatory problems. PBIL works with a set of solutions to the problems, called population, whose objective is create a probability vector, containing real values in each position, that when used in a decoding procedure gives subjects that present the best solutions for the function to be optimized. In this work a new form of learning for algorithm PBIL is developed, having aimed at to reduce the necessary time for the optimization process. This new algorithm will be used in the nuclear reactor design optimization. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment zone reactor, considering some restrictions. In this optimization is used the computational code HAMMER, and the results compared with other methods of optimization by artificial intelligence. (author)

The PBIL algorithm applied to a nuclear reactor design optimization

International Nuclear Information System (INIS)

Machado, Marcelo D.; Medeiros, Jose A.C.C.; Lima, Alan M.M. de; Schirru, Roberto

2007-01-01

The Population-Based Incremental Learning (PBIL) algorithm is a method that combines the mechanism of genetic algorithm with the simple competitive learning, creating an important tool to be used in the optimization of numeric functions and combinatory problems. PBIL works with a set of solutions to the problems, called population, whose objective is create a probability vector, containing real values in each position, that when used in a decoding procedure gives subjects that present the best solutions for the function to be optimized. In this work a new form of learning for algorithm PBIL is developed, having aimed at to reduce the necessary time for the optimization process. This new algorithm will be used in the nuclear reactor design optimization. The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and materials, in order to minimize the average peak-factor in a 3-enrichment zone reactor, considering some restrictions. In this optimization is used the computational code HAMMER, and the results compared with other methods of optimization by artificial intelligence. (author)

An Adaptive Unified Differential Evolution Algorithm for Global Optimization

Energy Technology Data Exchange (ETDEWEB)

Qiang, Ji; Mitchell, Chad

2014-11-03

In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.

Generalized field-splitting algorithms for optimal IMRT delivery efficiency

Energy Technology Data Exchange (ETDEWEB)

Kamath, Srijit [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States); Sahni, Sartaj [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Li, Jonathan [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States); Ranka, Sanjay [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Palta, Jatinder [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States)

2007-09-21

Intensity-modulated radiation therapy (IMRT) uses radiation beams of varying intensities to deliver varying doses of radiation to different areas of the tissue. The use of IMRT has allowed the delivery of higher doses of radiation to the tumor and lower doses to the surrounding healthy tissue. It is not uncommon for head and neck tumors, for example, to have large treatment widths that are not deliverable using a single field. In such cases, the intensity matrix generated by the optimizer needs to be split into two or three matrices, each of which may be delivered using a single field. Existing field-splitting algorithms used the pre-specified arbitrary split line or region where the intensity matrix is split along a column, i.e., all rows of the matrix are split along the same column (with or without the overlapping of split fields, i.e., feathering). If three fields result, then the two splits are along the same two columns for all rows. In this paper we study the problem of splitting a large field into two or three subfields with the field width as the only constraint, allowing for an arbitrary overlap of the split fields, so that the total MU efficiency of delivering the split fields is maximized. Proof of optimality is provided for the proposed algorithm. An average decrease of 18.8% is found in the total MUs when compared to the split generated by a commercial treatment planning system and that of 10% is found in the total MUs when compared to the split generated by our previously published algorithm. For more information on this article, see medicalphysicsweb.org.

Optimal Intermittent Dose Schedules for Chemotherapy Using Genetic Algorithm

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Nadia ALAM

2013-08-01

Full Text Available In this paper, a design method for optimal cancer chemotherapy schedules via genetic algorithm (GA is presented. The design targets the key objective of chemotherapy to minimize the size of cancer tumor after a predefined time with keeping toxic side effects in limit. This is a difficult target to achieve using conventional clinical methods due to poor therapeutic indices of existing anti-cancer drugs. Moreover, there are clinical limitations in treatment administration to maintain continuous treatment. Besides, carefully decided rest periods are recommended to for patient’s comfort. Three intermittent drug scheduling schemes are presented in this paper where GA is used to optimize the dose quantities and timings by satisfying several treatment constraints. All three schemes are found to be effective in total elimination of cancer tumor after an agreed treatment length. The number of cancer cells is found zero at the end of the treatment for all three cases with tolerable toxicity. Finally, two of the schemes, “Fixed interval variable dose (FIVD and “Periodic dose” that are periodic in characteristic have been emphasized due to their additional simplicity in administration along with friendliness to patients. responses to the designed treatment schedules. Therefore the proposed design method is capable of planning effective, simple, patient friendly and acceptable chemotherapy schedules.

Algorithm 896: LSA: Algorithms for Large-Scale Optimization

Czech Academy of Sciences Publication Activity Database

Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan

2009-01-01

Roč. 36, č. 3 (2009), 16-1-16-29 ISSN 0098-3500 R&D Pro jects: GA AV ČR IAA1030405; GA ČR GP201/06/P397 Institutional research plan: CEZ:AV0Z10300504 Keywords : algorithms * design * large-scale optimization * large-scale nonsmooth optimization * large-scale nonlinear least squares * large-scale nonlinear minimax * large-scale systems of nonlinear equations * sparse pro blems * partially separable pro blems * limited-memory methods * discrete Newton methods * quasi-Newton methods * primal interior-point methods Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.904, year: 2009

A hybrid artificial bee colony algorithm for numerical function optimization

Science.gov (United States)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

A global optimization algorithm inspired in the behavior of selfish herds.

Science.gov (United States)

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimal Power Allocation Algorithm for Radar Network Systems Based on Low Probability of Intercept Optimization(in English

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Shi Chen-guang

2014-08-01

Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.

When do evolutionary algorithms optimize separable functions in parallel?

DEFF Research Database (Denmark)

Doerr, Benjamin; Sudholt, Dirk; Witt, Carsten

2013-01-01

is that evolutionary algorithms make progress on all subfunctions in parallel, so that optimizing a separable function does not take not much longer than optimizing the hardest subfunction-subfunctions are optimized "in parallel." We show that this is only partially true, already for the simple (1+1) evolutionary...... algorithm ((1+1) EA). For separable functions composed of k Boolean functions indeed the optimization time is the maximum optimization time of these functions times a small O(log k) overhead. More generally, for sums of weighted subfunctions that each attain non-negative integer values less than r = o(log1...

Combinatorial optimization theory and algorithms

CERN Document Server

Korte, Bernhard

2018-01-01

This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It is based on numerous courses on combinatorial optimization and specialized topics, mostly at graduate level. This book reviews the fundamentals, covers the classical topics (paths, flows, matching, matroids, NP-completeness, approximation algorithms) in detail, and proceeds to advanced and recent topics, some of which have not appeared in a textbook before. Throughout, it contains complete but concise proofs, and also provides numerous exercises and references. This sixth edition has again been updated, revised, and significantly extended. Among other additions, there are new sections on shallow-light trees, submodular function maximization, smoothed analysis of the knapsack problem, the (ln 4+ɛ)-approximation for Steiner trees, and the VPN theorem. Thus, this book continues to represent the state of the art of combinatorial opti...

FSD-HSO Optimization Algorithm for Closed Fringes Interferogram Demodulation

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Ulises H. Rodriguez-Marmolejo

2016-01-01

Full Text Available Due to the physical nature of the interference phenomenon, extracting the phase of an interferogram is a known sinusoidal modulation problem. In order to solve this problem, a new hybrid mathematical optimization model for phase extraction is established. The combination of frequency guide sequential demodulation and harmony search optimization algorithms is used for demodulating closed fringes patterns in order to find the phase of interferogram applications. The proposed algorithm is tested in four sets of different synthetic interferograms, finding a range of average relative error in phase reconstructions of 0.14–0.39 rad. For reference, experimental results are compared with the genetic algorithm optimization technique, obtaining a reduction in the error up to 0.1448 rad. Finally, the proposed algorithm is compared with a very known demodulation algorithm, using a real interferogram, obtaining a relative error of 1.561 rad. Results are shown in patterns with complex fringes distribution.

Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems

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R. Venkata Rao

2013-01-01

Full Text Available Teaching-Learning-based optimization (TLBO is a recently proposed population based algorithm, which simulates the teaching-learning process of the class room. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. In this paper, the effect of elitism on the performance of the TLBO algorithm is investigated while solving unconstrained benchmark problems. The effects of common control parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. A statistical test is also performed to investigate the results obtained using different algorithms. The results have proved the effectiveness of the proposed elitist TLBO algorithm.

Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

Science.gov (United States)

Jin, Junchen

2016-01-01

The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998

Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

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Jiaxi Wang

2016-01-01

Full Text Available The shunting schedule of electric multiple units depot (SSED is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.

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

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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.

Parallel optimization of IDW interpolation algorithm on multicore platform

Science.gov (United States)

Guan, Xuefeng; Wu, Huayi

2009-10-01

Due to increasing power consumption, heat dissipation, and other physical issues, the architecture of central processing unit (CPU) has been turning to multicore rapidly in recent years. Multicore processor is packaged with multiple processor cores in the same chip, which not only offers increased performance, but also presents significant challenges to application developers. As a matter of fact, in GIS field most of current GIS algorithms were implemented serially and could not best exploit the parallelism potential on such multicore platforms. In this paper, we choose Inverse Distance Weighted spatial interpolation algorithm (IDW) as an example to study how to optimize current serial GIS algorithms on multicore platform in order to maximize performance speedup. With the help of OpenMP, threading methodology is introduced to split and share the whole interpolation work among processor cores. After parallel optimization, execution time of interpolation algorithm is greatly reduced and good performance speedup is achieved. For example, performance speedup on Intel Xeon 5310 is 1.943 with 2 execution threads and 3.695 with 4 execution threads respectively. An additional output comparison between pre-optimization and post-optimization is carried out and shows that parallel optimization does to affect final interpolation result.

2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization

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Seyedeh Elham Eftekharian

2017-11-01

Full Text Available Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA. In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm.

Improved Optimization for Wastewater Treatment and Reuse System Using Computational Intelligence

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Zong Woo Geem

2018-01-01

Full Text Available River water pollution by wastewater can cause significant negative impact on the aquatic sustainability. Hence, accurate modeling of this complicated system and its cost-effective treatment and reuse decision is very important because this optimization process is related to economic expenditure, societal health, and environmental deterioration. In order to optimize this complex system, we may consider three treatment or reuse options such as microscreening filtration, nitrification, and fertilization-oriented irrigation on top of two existing options such as settling and biological oxidation. The objective of this environmental optimization is to minimize the economic expenditure of life cycle costs while satisfying the public health standard in terms of groundwater quality and the environmental standard in terms of river water quality. Particularly, this study improves existing optimization model by pinpointing the critical deficit location of dissolved oxygen sag curve by using analytic differentiation. Also, the proposed formulation considers more practical constraints such as maximal size of irrigation area and minimal amount of filtration treatment process. The results obtained by using an evolutionary algorithm, named a parameter-setting-free harmony search algorithm, show that the proposed model successfully finds optimal solutions while conveniently locating the critical deficit point.

Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

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Feifei Dong

2014-01-01

Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.

Air data system optimization using a genetic algorithm

Science.gov (United States)

Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III

1992-01-01

An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.

An adaptive control algorithm for optimization of intensity modulated radiotherapy considering uncertainties in beam profiles, patient set-up and internal organ motion

International Nuclear Information System (INIS)

Loef, Johan; Lind, Bengt K.; Brahme, Anders

1998-01-01

A new general beam optimization algorithm for inverse treatment planning is presented. It utilizes a new formulation of the probability to achieve complication-free tumour control. The new formulation explicitly describes the dependence of the treatment outcome on the incident fluence distribution, the patient geometry, the radiobiological properties of the patient and the fractionation schedule. In order to account for both measured and non-measured positioning uncertainties, the algorithm is based on a combination of dynamic and stochastic optimization techniques. Because of the difficulty in measuring all aspects of the intra- and interfractional variations in the patient geometry, such as internal organ displacements and deformations, these uncertainties are primarily accounted for in the treatment planning process by intensity modulation using stochastic optimization. The information about the deviations from the nominal fluence profiles and the nominal position of the patient relative to the beam that is obtained by portal imaging during treatment delivery, is used in a feedback loop to automatically adjust the profiles and the location of the patient for all subsequent treatments. Based on the treatment delivered in previous fractions, the algorithm furnishes optimal corrections for the remaining dose delivery both with regard to the fluence profile and its position relative to the patient. By dynamically refining the beam configuration from fraction to fraction, the algorithm generates an optimal sequence of treatments that very effectively reduces the influence of systematic and random set-up uncertainties to minimize and almost eliminate their overall effect on the treatment. Computer simulations have shown that the present algorithm leads to a significant increase in the probability of uncomplicated tumour control compared with the simple classical approach of adding fixed set-up margins to the internal target volume. (author)

Efficient sampling algorithms for Monte Carlo based treatment planning

International Nuclear Information System (INIS)

DeMarco, J.J.; Solberg, T.D.; Chetty, I.; Smathers, J.B.

1998-01-01

Efficient sampling algorithms are necessary for producing a fast Monte Carlo based treatment planning code. This study evaluates several aspects of a photon-based tracking scheme and the effect of optimal sampling algorithms on the efficiency of the code. Four areas were tested: pseudo-random number generation, generalized sampling of a discrete distribution, sampling from the exponential distribution, and delta scattering as applied to photon transport through a heterogeneous simulation geometry. Generalized sampling of a discrete distribution using the cutpoint method can produce speedup gains of one order of magnitude versus conventional sequential sampling. Photon transport modifications based upon the delta scattering method were implemented and compared with a conventional boundary and collision checking algorithm. The delta scattering algorithm is faster by a factor of six versus the conventional algorithm for a boundary size of 5 mm within a heterogeneous geometry. A comparison of portable pseudo-random number algorithms and exponential sampling techniques is also discussed

Introduction to Evolutionary Algorithms

CERN Document Server

Yu, Xinjie

2010-01-01

Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm opti

Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

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Patel G.C.M.

2016-09-01

Full Text Available The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.. It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA, particle swarm optimization (PSO and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.

Algorithms for optimal sequencing of dynamic multileaf collimators

Energy Technology Data Exchange (ETDEWEB)

Kamath, Srijit [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Sahni, Sartaj [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Palta, Jatinder [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States); Ranka, Sanjay [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States)

2004-01-07

Dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is used to deliver intensity modulated beams using a multileaf collimator (MLC), with the leaves in motion. DMLC-IMRT requires the conversion of a radiation intensity map into a leaf sequence file that controls the movement of the MLC while the beam is on. It is imperative that the intensity map delivered using the leaf sequence file be as close as possible to the intensity map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf-sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf-sequencing algorithms for dynamic multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under the most common leaf movement constraints that include leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bi-directional movement of the MLC leaves.

Algorithms for optimal sequencing of dynamic multileaf collimators

International Nuclear Information System (INIS)

Kamath, Srijit; Sahni, Sartaj; Palta, Jatinder; Ranka, Sanjay

2004-01-01

Dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is used to deliver intensity modulated beams using a multileaf collimator (MLC), with the leaves in motion. DMLC-IMRT requires the conversion of a radiation intensity map into a leaf sequence file that controls the movement of the MLC while the beam is on. It is imperative that the intensity map delivered using the leaf sequence file be as close as possible to the intensity map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf-sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf-sequencing algorithms for dynamic multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under the most common leaf movement constraints that include leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bi-directional movement of the MLC leaves

A Degree Distribution Optimization Algorithm for Image Transmission

Science.gov (United States)

Jiang, Wei; Yang, Junjie

2016-09-01

Luby Transform (LT) code is the first practical implementation of digital fountain code. The coding behavior of LT code is mainly decided by the degree distribution which determines the relationship between source data and codewords. Two degree distributions are suggested by Luby. They work well in typical situations but not optimally in case of finite encoding symbols. In this work, the degree distribution optimization algorithm is proposed to explore the potential of LT code. Firstly selection scheme of sparse degrees for LT codes is introduced. Then probability distribution is optimized according to the selected degrees. In image transmission, bit stream is sensitive to the channel noise and even a single bit error may cause the loss of synchronization between the encoder and the decoder. Therefore the proposed algorithm is designed for image transmission situation. Moreover, optimal class partition is studied for image transmission with unequal error protection. The experimental results are quite promising. Compared with LT code with robust soliton distribution, the proposed algorithm improves the final quality of recovered images obviously with the same overhead.

Optimal Design of Pumped Pipeline Systems Using Genetic Algorithm and Mathematical Optimization

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Mohammadhadi Afshar

2007-12-01

Full Text Available In recent years, much attention has been paid to the optimal design of pipeline systems. In this study, the problem of pipeline system optimal design has been solved through genetic algorithm and mathematical optimization. Pipe diameters and their thicknesses are considered as decision variables to be designed in a manner that water column separation and excessive pressures are avoided in the event of pump failure. Capabilities of the genetic algorithm and the mathematical programming method are compared for the problem under consideration. For simulation of transient streams, explicit characteristic method is used in which devices such as pumps are defined as boundary conditions of the equations defining the hydraulic behavior of pipe segments. The problem of optimal design of pipeline systems is a constrained problem which is converted to an unconstrained optimization problem using an external penalty function approach. The efficiency of the proposed approaches is verified in one example and the results are presented.

Food processing optimization using evolutionary algorithms | Enitan ...

African Journals Online (AJOL)

Evolutionary algorithms are widely used in single and multi-objective optimization. They are easy to use and provide solution(s) in one simulation run. They are used in food processing industries for decision making. Food processing presents constrained and unconstrained optimization problems. This paper reviews the ...

Search and optimization by metaheuristics techniques and algorithms inspired by nature

CERN Document Server

Du, Ke-Lin

2016-01-01

This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computin...

Honing process optimization algorithms

Science.gov (United States)

Kadyrov, Ramil R.; Charikov, Pavel N.; Pryanichnikova, Valeria V.

2018-03-01

This article considers the relevance of honing processes for creating high-quality mechanical engineering products. The features of the honing process are revealed and such important concepts as the task for optimization of honing operations, the optimal structure of the honing working cycles, stepped and stepless honing cycles, simulation of processing and its purpose are emphasized. It is noted that the reliability of the mathematical model determines the quality parameters of the honing process control. An algorithm for continuous control of the honing process is proposed. The process model reliably describes the machining of a workpiece in a sufficiently wide area and can be used to operate the CNC machine CC743.

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

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Xiguang Li

2017-01-01

Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.

A Fuzzy Gravitational Search Algorithm to Design Optimal IIR Filters

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Danilo Pelusi

2018-03-01

Full Text Available The goodness of Infinite Impulse Response (IIR digital filters design depends on pass band ripple, stop band ripple and transition band values. The main problem is defining a suitable error fitness function that depends on these parameters. This fitness function can be optimized by search algorithms such as evolutionary algorithms. This paper proposes an intelligent algorithm for the design of optimal 8th order IIR filters. The main contribution is the design of Fuzzy Inference Systems able to tune key parameters of a revisited version of the Gravitational Search Algorithm (GSA. In this way, a Fuzzy Gravitational Search Algorithm (FGSA is designed. The optimization performances of FGSA are compared with those of Differential Evolution (DE and GSA. The results show that FGSA is the algorithm that gives the best compromise between goodness, robustness and convergence rate for the design of 8th order IIR filters. Moreover, FGSA assures a good stability of the designed filters.

Research on application of complex-genetic algorithm in nuclear component optimal design

International Nuclear Information System (INIS)

He Shijing; Yan Changqi; Wang Jianjun; Wang Meng

2010-01-01

Complex algorithm is one of the most commonly used methods in the mechanical design optimization, such as the optimization of nuclear component. An improved method,complex-genetic algorithm(CGA), is developed based on traditional complex algorithm(TCA), in which the disadvantages of TCA have been overcome. An optimal calculation,which represents the pressurizer, is carried out in order to analyze the optimization capability of CGA. The results show that CGA has better optimizing performance than TCA. (authors)

Genetic Algorithm and its Application in Optimal Sensor Layout

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Xiang-Yang Chen

2015-05-01

Full Text Available This paper aims at the problem of multi sensor station distribution, based on multi- sensor systems of different types as the research object, in the analysis of various types of sensors with different application background, different indicators of demand, based on the different constraints, for all kinds of multi sensor station is studied, the application of genetic algorithms as a tool for the objective function of the models optimization, then the optimal various types of multi sensor station distribution plan, improve the performance of the system, and achieved good military effect. In the field of application of sensor radar, track measuring instrument, the satellite, passive positioning equipment of various types, specific problem, use care indicators and station arrangement between the mathematical model of geometry, using genetic algorithm to get the optimization results station distribution, to solve a variety of practical problems provides useful help, but also reflects the improved genetic algorithm in electronic weapon system based on multi sensor station distribution on the applicability and effectiveness of the optimization; finally the genetic algorithm for integrated optimization of multi sensor station distribution using the good to the training exercise tasks based on actual in, and have achieved good military effect.

A hybrid neural network – world cup optimization algorithm for melanoma detection

Directory of Open Access Journals (Sweden)

Razmjooy Navid

2018-03-01

Full Text Available One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN. World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

Deriving the Normalized Min-Sum Algorithm from Cooperative Optimization

OpenAIRE

Huang, Xiaofei

2006-01-01

The normalized min-sum algorithm can achieve near-optimal performance at decoding LDPC codes. However, it is a critical question to understand the mathematical principle underlying the algorithm. Traditionally, people thought that the normalized min-sum algorithm is a good approximation to the sum-product algorithm, the best known algorithm for decoding LDPC codes and Turbo codes. This paper offers an alternative approach to understand the normalized min-sum algorithm. The algorithm is derive...

Investigations into the Optimization of Multi-Source Strength Brachytherapy Treatment Procedures

CERN Document Server

Henderson, D L; Yoo, S

2002-01-01

The goal of this project is to investigate the use of multi-strength and multi-specie radioactive sources in permanent prostate implant brachytherapy. In order to fulfill the requirement for an optimal dose distribution, the prescribed dose should be delivered to the target in a nearly uniform dose distribution while simultaneously sparing sensitive structures. The treatment plan should use a small number of needles and sources while satisfying the treatment requirements. The hypothesis for the use of multi-strength and/or multi-specie sources is that a better treatment plan using fewer sources and needles could be obtained than by treatment plans using single-strength sources could reduce the overall number of sources used for treatment. We employ a recently developed greedy algorithm based on the adjoint concept as the optimization search engine. The algorithm utilizes and ''adjoint ratio'', which provides a means of ranking source positions, as the pseudo-objective function. It ha s been shown that the gre...

Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis

Science.gov (United States)

Muslim, M. A.; Rukmana, S. H.; Sugiharti, E.; Prasetiyo, B.; Alimah, S.

2018-03-01

Data mining has become a basic methodology for computational applications in the field of medical domains. Data mining can be applied in the health field such as for diagnosis of breast cancer, heart disease, diabetes and others. Breast cancer is most common in women, with more than one million cases and nearly 600,000 deaths occurring worldwide each year. The most effective way to reduce breast cancer deaths was by early diagnosis. This study aims to determine the level of breast cancer diagnosis. This research data uses Wisconsin Breast Cancer dataset (WBC) from UCI machine learning. The method used in this research is the algorithm C4.5 and Particle Swarm Optimization (PSO) as a feature option and to optimize the algorithm. C4.5. Ten-fold cross-validation is used as a validation method and a confusion matrix. The result of this research is C4.5 algorithm. The particle swarm optimization C4.5 algorithm has increased by 0.88%.

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.

A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design

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Liu Yan

2018-01-01

Full Text Available The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

A Hybrid Algorithm for Optimizing Multi- Modal Functions

Institute of Scientific and Technical Information of China (English)

Li Qinghua; Yang Shida; Ruan Youlin

2006-01-01

A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.

Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms.

Science.gov (United States)

Sari, Murat; Tuna, Can; Akogul, Serkan

2018-03-28

The aim of this article is to investigate pathological subjects from a population through different physical factors. To achieve this, particle swarm optimization (PSO) and K-means (KM) clustering algorithms have been combined (PSO-KM). Datasets provided by the literature were divided into three clusters based on age and weight parameters and each one of right tibial external rotation (RTER), right tibial internal rotation (RTIR), left tibial external rotation (LTER), and left tibial internal rotation (LTIR) values were divided into three types as Type 1, Type 2 and Type 3 (Type 2 is non-pathological (normal) and the other two types are pathological (abnormal)), respectively. The rotation values of every subject in any cluster were noted. Then the algorithm was run and the produced values were also considered. The values of the produced algorithm, the PSO-KM, have been compared with the real values. The hybrid PSO-KM algorithm has been very successful on the optimal clustering of the tibial rotation types through the physical criteria. In this investigation, Type 2 (pathological subjects) is of especially high predictability and the PSO-KM algorithm has been very successful as an operation system for clustering and optimizing the tibial motion data assessments. These research findings are expected to be very useful for health providers, such as physiotherapists, orthopedists, and so on, in which this consequence may help clinicians to appropriately designing proper treatment schedules for patients.

A Unified Differential Evolution Algorithm for Global Optimization

Energy Technology Data Exchange (ETDEWEB)

Qiang, Ji; Mitchell, Chad

2014-06-24

Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.

A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

Directory of Open Access Journals (Sweden)

Lijin Wang

2015-01-01

Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.

A Hybrid Genetic Algorithm Approach for Optimal Power Flow

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Sydulu Maheswarapu

2011-08-01

Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms

Directory of Open Access Journals (Sweden)

Debkalpa Goswami

2015-03-01

Full Text Available Ultrasonic machining (USM is a mechanical material removal process used to erode holes and cavities in hard or brittle workpieces by using shaped tools, high-frequency mechanical motion and an abrasive slurry. Unlike other non-traditional machining processes, such as laser beam and electrical discharge machining, USM process does not thermally damage the workpiece or introduce significant levels of residual stress, which is important for survival of materials in service. For having enhanced machining performance and better machined job characteristics, it is often required to determine the optimal control parameter settings of an USM process. The earlier mathematical approaches for parametric optimization of USM processes have mostly yielded near optimal or sub-optimal solutions. In this paper, two almost unexplored non-conventional optimization techniques, i.e. gravitational search algorithm (GSA and fireworks algorithm (FWA are applied for parametric optimization of USM processes. The optimization performance of these two algorithms is compared with that of other popular population-based algorithms, and the effects of their algorithm parameters on the derived optimal solutions and computational speed are also investigated. It is observed that FWA provides the best optimal results for the considered USM processes.

Amodified probabilistic genetic algorithm for the solution of complex constrained optimization problems

OpenAIRE

Vorozheikin, A.; Gonchar, T.; Panfilov, I.; Sopov, E.; Sopov, S.

2009-01-01

A new algorithm for the solution of complex constrained optimization problems based on the probabilistic genetic algorithm with optimal solution prediction is proposed. The efficiency investigation results in comparison with standard genetic algorithm are presented.

Optimization in radiotherapy treatment planning thanks to a fast dose calculation method

International Nuclear Information System (INIS)

Yang, Mingchao

2014-01-01

This thesis deals with the radiotherapy treatments planning issue which need a fast and reliable treatment planning system (TPS). The TPS is composed of a dose calculation algorithm and an optimization method. The objective is to design a plan to deliver the dose to the tumor while preserving the surrounding healthy and sensitive tissues. The treatment planning aims to determine the best suited radiation parameters for each patient's treatment. In this thesis, the parameters of treatment with IMRT (Intensity modulated radiation therapy) are the beam angle and the beam intensity. The objective function is multi-criteria with linear constraints. The main objective of this thesis is to demonstrate the feasibility of a treatment planning optimization method based on a fast dose-calculation technique developed by (Blanpain, 2009). This technique proposes to compute the dose by segmenting the patient's phantom into homogeneous meshes. The dose computation is divided into two steps. The first step impacts the meshes: projections and weights are set according to physical and geometrical criteria. The second step impacts the voxels: the dose is computed by evaluating the functions previously associated to their mesh. A reformulation of this technique makes possible to solve the optimization problem by the gradient descent algorithm. The main advantage of this method is that the beam angle parameters could be optimized continuously in 3 dimensions. The obtained results in this thesis offer many opportunities in the field of radiotherapy treatment planning optimization. (author) [fr

Application of mapping crossover genetic algorithm in nuclear power equipment optimization design

International Nuclear Information System (INIS)

Li Guijiang; Yan Changqi; Wang Jianjun; Liu Chengyang

2013-01-01

Genetic algorithm (GA) has been widely applied in nuclear engineering. An improved method, named the mapping crossover genetic algorithm (MCGA), was developed aiming at improving the shortcomings of traditional genetic algorithm (TGA). The optimal results of benchmark problems show that MCGA has better optimizing performance than TGA. MCGA was applied to the reactor coolant pump optimization design. (authors)

Comparison of Greedy Algorithms for Decision Tree Optimization

KAUST Repository

Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

2013-01-01

This chapter is devoted to the study of 16 types of greedy algorithms for decision tree construction. The dynamic programming approach is used for construction of optimal decision trees. Optimization is performed relative to minimal values

Chaotically encoded particle swarm optimization algorithm and its applications

International Nuclear Information System (INIS)

Alatas, Bilal; Akin, Erhan

2009-01-01

This paper proposes a novel particle swarm optimization (PSO) algorithm, chaotically encoded particle swarm optimization algorithm (CENPSOA), based on the notion of chaos numbers that have been recently proposed for a novel meaning to numbers. In this paper, various chaos arithmetic and evaluation measures that can be used in CENPSOA have been described. Furthermore, CENPSOA has been designed to be effectively utilized in data mining applications.

Optimizing Transmission Network Expansion Planning With The Mean Of Chaotic Differential Evolution Algorithm

Directory of Open Access Journals (Sweden)

Ahmed R. Abdelaziz

2015-08-01

Full Text Available This paper presents an application of Chaotic differential evolution optimization approach meta-heuristics in solving transmission network expansion planning TNEP using an AC model associated with reactive power planning RPP. The reliabilityredundancy of network analysis optimization problems implicate selection of components with multiple choices and redundancy levels that produce maximum benefits can be subject to the cost weight and volume constraints is presented in this paper. Classical mathematical methods have failed in handling non-convexities and non-smoothness in optimization problems. As an alternative to the classical optimization approaches the meta-heuristics have attracted lot of attention due to their ability to find an almost global optimal solution in reliabilityredundancy optimization problems. Evolutionary algorithms EAs paradigms of evolutionary computation field are stochastic and robust meta-heuristics useful to solve reliabilityredundancy optimization problems. EAs such as genetic algorithm evolutionary programming evolution strategies and differential evolution are being used to find global or near global optimal solution. The Differential Evolution Algorithm DEA population-based algorithm is an optimal algorithm with powerful global searching capability but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaotic Differential Evolution algorithm CDE based on the cat map is recommended which combines DE and chaotic searching algorithm. Simulation results and comparisons show that the chaotic differential evolution algorithm using Cat map is competitive and stable in performance with other optimization approaches and other maps.

Simulated annealing algorithm for reactor in-core design optimizations

International Nuclear Information System (INIS)

Zhong Wenfa; Zhou Quan; Zhong Zhaopeng

2001-01-01

A nuclear reactor must be optimized for in core fuel management to make full use of the fuel, to reduce the operation cost and to flatten the power distribution reasonably. The author presents a simulated annealing algorithm. The optimized objective function and the punishment function were provided for optimizing the reactor physics design. The punishment function was used to practice the simulated annealing algorithm. The practical design of the NHR-200 was calculated. The results show that the K eff can be increased by 2.5% and the power distribution can be flattened

Artificial root foraging optimizer algorithm with hybrid strategies

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Yang Liu

2017-02-01

Full Text Available In this work, a new plant-inspired optimization algorithm namely the hybrid artificial root foraging optimizion (HARFO is proposed, which mimics the iterative root foraging behaviors for complex optimization. In HARFO model, two innovative strategies were developed: one is the root-to-root communication strategy, which enables the individual exchange information with each other in different efficient topologies that can essentially improve the exploration ability; the other is co-evolution strategy, which can structure the hierarchical spatial population driven by evolutionary pressure of multiple sub-populations that ensure the diversity of root population to be well maintained. The proposed algorithm is benchmarked against four classical evolutionary algorithms on well-designed test function suites including both classical and composition test functions. Through the rigorous performance analysis that of all these tests highlight the significant performance improvement, and the comparative results show the superiority of the proposed algorithm.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

Science.gov (United States)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Optimization Shape of Variable Capacitance Micromotor Using Differential Evolution Algorithm

Directory of Open Access Journals (Sweden)

A. Ketabi

2010-01-01

Full Text Available A new method for optimum shape design of variable capacitance micromotor (VCM using Differential Evolution (DE, a stochastic search algorithm, is presented. In this optimization exercise, the objective function aims to maximize torque value and minimize the torque ripple, where the geometric parameters are considered to be the variables. The optimization process is carried out using a combination of DE algorithm and FEM analysis. Fitness value is calculated by FEM analysis using COMSOL3.4, and the DE algorithm is realized by MATLAB7.4. The proposed method is applied to a VCM with 8 poles at the stator and 6 poles at the rotor. The results show that the optimized micromotor using DE algorithm had higher torque value and lower torque ripple, indicating the validity of this methodology for VCM design.

Optimized Min-Sum Decoding Algorithm for Low Density Parity Check Codes

OpenAIRE

Mohammad Rakibul Islam; Dewan Siam Shafiullah; Muhammad Mostafa Amir Faisal; Imran Rahman

2011-01-01

Low Density Parity Check (LDPC) code approaches Shannon–limit performance for binary field and long code lengths. However, performance of binary LDPC code is degraded when the code word length is small. An optimized min-sum algorithm for LDPC code is proposed in this paper. In this algorithm unlike other decoding methods, an optimization factor has been introduced in both check node and bit node of the Min-sum algorithm. The optimization factor is obtained before decoding program, and the sam...

Setting value optimization method in integration for relay protection based on improved quantum particle swarm optimization algorithm

Science.gov (United States)

Yang, Guo Sheng; Wang, Xiao Yang; Li, Xue Dong

2018-03-01

With the establishment of the integrated model of relay protection and the scale of the power system expanding, the global setting and optimization of relay protection is an extremely difficult task. This paper presents a kind of application in relay protection of global optimization improved particle swarm optimization algorithm and the inverse time current protection as an example, selecting reliability of the relay protection, selectivity, quick action and flexibility as the four requires to establish the optimization targets, and optimizing protection setting values of the whole system. Finally, in the case of actual power system, the optimized setting value results of the proposed method in this paper are compared with the particle swarm algorithm. The results show that the improved quantum particle swarm optimization algorithm has strong search ability, good robustness, and it is suitable for optimizing setting value in the relay protection of the whole power system.

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Directory of Open Access Journals (Sweden)

Rafał Dreżewski

2017-08-01

Full Text Available Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

A homotopy algorithm for digital optimal projection control GASD-HADOC

Science.gov (United States)

Collins, Emmanuel G., Jr.; Richter, Stephen; Davis, Lawrence D.

1993-01-01

The linear-quadratic-gaussian (LQG) compensator was developed to facilitate the design of control laws for multi-input, multi-output (MIMO) systems. The compensator is computed by solving two algebraic equations for which standard closed-loop solutions exist. Unfortunately, the minimal dimension of an LQG compensator is almost always equal to the dimension of the plant and can thus often violate practical implementation constraints on controller order. This deficiency is especially highlighted when considering control-design for high-order systems such as flexible space structures. This deficiency motivated the development of techniques that enable the design of optimal controllers whose dimension is less than that of the design plant. A homotopy approach based on the optimal projection equations that characterize the necessary conditions for optimal reduced-order control. Homotopy algorithms have global convergence properties and hence do not require that the initializing reduced-order controller be close to the optimal reduced-order controller to guarantee convergence. However, the homotopy algorithm previously developed for solving the optimal projection equations has sublinear convergence properties and the convergence slows at higher authority levels and may fail. A new homotopy algorithm for synthesizing optimal reduced-order controllers for discrete-time systems is described. Unlike the previous homotopy approach, the new algorithm is a gradient-based, parameter optimization formulation and was implemented in MATLAB. The results reported may offer the foundation for a reliable approach to optimal, reduced-order controller design.

An Enhanced Memetic Algorithm for Single-Objective Bilevel Optimization Problems.

Science.gov (United States)

Islam, Md Monjurul; Singh, Hemant Kumar; Ray, Tapabrata; Sinha, Ankur

2017-01-01

Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper-level  leader problem, subject to the optimality of a lower-level follower problem. Several problems from the domain of engineering, logistics, economics, and transportation have an inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization. Given the nested nature of bilevel problems, the computational effort (number of function evaluations) required to solve them is often quite high. In this article, we explore the use of a Memetic Algorithm (MA) to solve bilevel optimization problems. While MAs have been quite successful in solving single-level optimization problems, there have been relatively few studies exploring their potential for solving bilevel optimization problems. MAs essentially attempt to combine advantages of global and local search strategies to identify optimum solutions with low computational cost (function evaluations). The approach introduced in this article is a nested Bilevel Memetic Algorithm (BLMA). At both upper and lower levels, either a global or a local search method is used during different phases of the search. The performance of BLMA is presented on twenty-five standard test problems and two real-life applications. The results are compared with other established algorithms to demonstrate the efficacy of the proposed approach.

A dynamic inertia weight particle swarm optimization algorithm

International Nuclear Information System (INIS)

Jiao Bin; Lian Zhigang; Gu Xingsheng

2008-01-01

Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

Science.gov (United States)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

Directory of Open Access Journals (Sweden)

Wei Li

2015-01-01

Full Text Available We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

PSO Algorithm for an Optimal Power Controller in a Microgrid

Science.gov (United States)

Al-Saedi, W.; Lachowicz, S.; Habibi, D.; Bass, O.

2017-07-01

This paper presents the Particle Swarm Optimization (PSO) algorithm to improve the quality of the power supply in a microgrid. This algorithm is proposed for a real-time selftuning method that used in a power controller for an inverter based Distributed Generation (DG) unit. In such system, the voltage and frequency are the main control objectives, particularly when the microgrid is islanded or during load change. In this work, the PSO algorithm is implemented to find the optimal controller parameters to satisfy the control objectives. The results show high performance of the applied PSO algorithm of regulating the microgrid voltage and frequency.

Globally convergent optimization algorithm using conservative convex separable diagonal quadratic approximations

NARCIS (Netherlands)

Groenwold, A.A.; Wood, D.W.; Etman, L.F.P.; Tosserams, S.

2009-01-01

We implement and test a globally convergent sequential approximate optimization algorithm based on (convexified) diagonal quadratic approximations. The algorithm resides in the class of globally convergent optimization methods based on conservative convex separable approximations developed by

An interactive beam-weight optimization tool for three-dimensional radiotherapy treatment planning

International Nuclear Information System (INIS)

Burba, S.; Gardey, K.; Nadobny, J.; Stalling, D.; Seebass, M.; Beier, J.; Wust, P.; Budach, V.; Felix, R.

1997-01-01

Purpose: A computer software tool has been developed to aid the treatment planner in selecting beam weights for three-dimensional radiotherapy treatment planning. An approach to plan optimization has been made that is based on the use of an iterative feasibility search algorithm combined with a quadratic convergence method that seeks a set of beam weights which satisfies all the dose constraints set by the planner. Materials and Methods: A FORTRAN module for dose calculation for radiotherapy (a VOXELPLAN modification) has been integrated into an object-oriented Silicon Graphics TM platform in an IRIS Inventor environment on basis of the OpenGL which up to now has been exclusively used for the calculation of E-field distributions in hyperthermia (HyperPlan TM ). After the successful calculation and representation of the dose distribution in the Silicon Graphics TM platform, an algorithm involving the minimization method according to the principle of quadratic convergence was developed for optimizing beam weights of a number of pre-calculated fields. The verification of the algorithms for dose calculation and dose optimization has been realized by use of a standardized interface to the program VIRTUOS as well as by the collapsed cone algorithm implemented in the commercial treatment planning system Helax TMS TM . Results: The search algorithm allows the planner to incorporate relative importance weightings to target volumes and anatomical structures, specifying, for example, that a dose constraint to the spinal cord is much more crucial to the overall evaluation of a treatment plan than a dose constraint to otherwise uninvolved soft tissue. In most cases the applied minimization method according to the model of Davidon-Fletcher-Powell showed ultimate fast convergence for a general function f(x) with continuous second derivatives and fast convergence for a positive definite quadratic function. In other cases, however, the absence of an acceptable solution may indicate

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.

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.

Science.gov (United States)

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos; Mariani, Viviana Cocco

2007-01-01

Global optimization based on evolutionary algorithms can be used as the important component for many engineering optimization problems. Evolutionary algorithms have yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization problems in the power systems area. Differential evolution (DE) is a simple and efficient evolutionary algorithm for function optimization over continuous spaces. It has reportedly outperformed search heuristics when tested over both benchmark and real world problems. This paper proposes improved DE algorithms for solving economic load dispatch problems that take into account nonlinear generator features such as ramp rate limits and prohibited operating zones in the power system operation. The DE algorithms and its variants are validated for two test systems consisting of 6 and 15 thermal units. Various DE approaches outperforms other state of the art algorithms reported in the literature in solving load dispatch problems with generator constraints

Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

International Nuclear Information System (INIS)

Chaudhary, Kailash; Chaudhary, Himanshu

2015-01-01

In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Chaudhary, Kailash; Chaudhary, Himanshu [Malaviya National Institute of Technology, Jaipur (Malaysia)

2015-11-15

In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slider crank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equipemental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using Teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance

Directory of Open Access Journals (Sweden)

Na Dong

2016-01-01

Full Text Available An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results.

A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

KAUST Repository

Harman, Radoslav; Filová , Lenka; Richtarik, Peter

2018-01-01

We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of data-points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments

KAUST Repository

Harman, Radoslav

2018-01-17

We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of state-of-the-art methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of D-optimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of data-points. For D-optimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of A-optimality. These formulas enable one to use REX for computing A-optimal and I-optimal designs.

Automated Lead Optimization of MMP-12 Inhibitors Using a Genetic Algorithm.

Science.gov (United States)

Pickett, Stephen D; Green, Darren V S; Hunt, David L; Pardoe, David A; Hughes, Ian

2011-01-13

Traditional lead optimization projects involve long synthesis and testing cycles, favoring extensive structure-activity relationship (SAR) analysis and molecular design steps, in an attempt to limit the number of cycles that a project must run to optimize a development candidate. Microfluidic-based chemistry and biology platforms, with cycle times of minutes rather than weeks, lend themselves to unattended autonomous operation. The bottleneck in the lead optimization process is therefore shifted from synthesis or test to SAR analysis and design. As such, the way is open to an algorithm-directed process, without the need for detailed user data analysis. Here, we present results of two synthesis and screening experiments, undertaken using traditional methodology, to validate a genetic algorithm optimization process for future application to a microfluidic system. The algorithm has several novel features that are important for the intended application. For example, it is robust to missing data and can suggest compounds for retest to ensure reliability of optimization. The algorithm is first validated on a retrospective analysis of an in-house library embedded in a larger virtual array of presumed inactive compounds. In a second, prospective experiment with MMP-12 as the target protein, 140 compounds are submitted for synthesis over 10 cycles of optimization. Comparison is made to the results from the full combinatorial library that was synthesized manually and tested independently. The results show that compounds selected by the algorithm are heavily biased toward the more active regions of the library, while the algorithm is robust to both missing data (compounds where synthesis failed) and inactive compounds. This publication places the full combinatorial library and biological data into the public domain with the intention of advancing research into algorithm-directed lead optimization methods.

Bioinspired computation in combinatorial optimization: algorithms and their computational complexity

DEFF Research Database (Denmark)

Neumann, Frank; Witt, Carsten

2012-01-01

Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials...... problems. Classical single objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help...... to speed up bioinspired computation for single-objective optimization problems. The tutorial is based on a book written by the authors with the same title. Further information about the book can be found at www.bioinspiredcomputation.com....

Joint optimization of algorithmic suites for EEG analysis.

Science.gov (United States)

Santana, Eder; Brockmeier, Austin J; Principe, Jose C

2014-01-01

Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

Science.gov (United States)

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-05-04

Particle swarm optimization is a powerful metaheuristic population-based global optimization algorithm. However, when applied to non-separable objective functions its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant particle swarm optimization algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates a superior performance across several nonlinear, multimodal benchmark functions compared to the rotation-invariant Particle Swam Optimization (PSO) algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in ReaxFF-lg reactive force field is carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents a better performance compared to a Genetic Algorithm optimization method in the optimization of a ReaxFF-lg correction model parameters. The computational framework is implemented in a standalone C++ code that allows a straightforward development of ReaxFF reactive force fields.

Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems

International Nuclear Information System (INIS)

Ahmadi, Mohamadreza; Mojallali, Hamed

2012-01-01

Highlights: ► A new meta-heuristic optimization algorithm. ► Integration of invasive weed optimization and chaotic search methods. ► A novel parameter identification scheme for chaotic systems. - Abstract: This paper introduces a novel hybrid optimization algorithm by taking advantage of the stochastic properties of chaotic search and the invasive weed optimization (IWO) method. In order to deal with the weaknesses associated with the conventional method, the proposed chaotic invasive weed optimization (CIWO) algorithm is presented which incorporates the capabilities of chaotic search methods. The functionality of the proposed optimization algorithm is investigated through several benchmark multi-dimensional functions. Furthermore, an identification technique for chaotic systems based on the CIWO algorithm is outlined and validated by several examples. The results established upon the proposed scheme are also supplemented which demonstrate superior performance with respect to other conventional methods.

Optimal recombination in genetic algorithms for combinatorial optimization problems: Part II

Directory of Open Access Journals (Sweden)

Eremeev Anton V.

2014-01-01

Full Text Available This paper surveys results on complexity of the optimal recombination problem (ORP, which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. In Part II, we consider the computational complexity of ORPs arising in genetic algorithms for problems on permutations: the Travelling Salesman Problem, the Shortest Hamilton Path Problem and the Makespan Minimization on Single Machine and some other related problems. The analysis indicates that the corresponding ORPs are NP-hard, but solvable by faster algorithms, compared to the problems they are derived from.

Optimization of MIS/IL solar cells parameters using genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Ahmed, K.A.; Mohamed, E.A.; Alaa, S.H. [Faculty of Engineering, Alexandria Univ. (Egypt); Motaz, M.S. [Institute of Graduate Studies and Research, Alexandria Univ. (Egypt)

2004-07-01

This paper presents a genetic algorithm optimization for MIS/IL solar cell parameters including doping concentration NA, metal work function {phi}m, oxide thickness d{sub ox}, mobile charge density N{sub m}, fixed oxide charge density N{sub ox} and the external back bias applied to the inversion grid V. The optimization results are compared with theoretical optimization and shows that the genetic algorithm can be used for determining the optimum parameters of the cell. (orig.)

Cost optimization model and its heuristic genetic algorithms

International Nuclear Information System (INIS)

Liu Wei; Wang Yongqing; Guo Jilin

1999-01-01

Interest and escalation are large quantity in proportion to the cost of nuclear power plant construction. In order to optimize the cost, the mathematics model of cost optimization for nuclear power plant construction was proposed, which takes the maximum net present value as the optimization goal. The model is based on the activity networks of the project and is an NP problem. A heuristic genetic algorithms (HGAs) for the model was introduced. In the algorithms, a solution is represented with a string of numbers each of which denotes the priority of each activity for assigned resources. The HGAs with this encoding method can overcome the difficulty which is harder to get feasible solutions when using the traditional GAs to solve the model. The critical path of the activity networks is figured out with the concept of predecessor matrix. An example was computed with the HGAP programmed in C language. The results indicate that the model is suitable for the objectiveness, the algorithms is effective to solve the model

Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

International Nuclear Information System (INIS)

Martorell, S.; Carlos, S.; Villanueva, J.F.; Sanchez, A.I; Galvan, B.; Salazar, D.; Cepin, M.

2006-01-01

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability

A Simple But Effective Canonical Dual Theory Unified Algorithm for Global Optimization

OpenAIRE

Zhang, Jiapu

2011-01-01

Numerical global optimization methods are often very time consuming and could not be applied for high-dimensional nonconvex/nonsmooth optimization problems. Due to the nonconvexity/nonsmoothness, directly solving the primal problems sometimes is very difficult. This paper presents a very simple but very effective canonical duality theory (CDT) unified global optimization algorithm. This algorithm has convergence is proved in this paper. More important, for this CDT-unified algorithm, numerous...

Design optimization of brushed permanent magnet D C motor by genetic algorithm

CERN Document Server

Amini, S

2002-01-01

Because of field winding replacement with permanent magnet in brushed permanent magnet D C (PMDC) motors, field losses are eliminated and the structure of the motor is more simple. Efficiency of these motors is therefore increased and the manufacturing process is simplified. Hence, these motors are commonly used in low power applications and their design and optimization is an important consideration. Genetic algorithms are proposed for design optimization of PMD motors because of their independence to objective function structure and its derivative. In this paper genetic algorithms are evaluated for PMDC motor design optimization. an introduction is first presented about PMDC motors, general design procedure and elements of their optimization. Genetic algorithms are then briefly described. Finally results of optimization by genetic algorithms are compared with the one obtained using a conventional method.

Design optimization of brushed permanent magnet D C motor by genetic algorithm

International Nuclear Information System (INIS)

Amini, S.; Oraee, H.

2002-01-01

Because of field winding replacement with permanent magnet in brushed permanent magnet D C (PMDC) motors, field losses are eliminated and the structure of the motor is more simple. Efficiency of these motors is therefore increased and the manufacturing process is simplified. Hence, these motors are commonly used in low power applications and their design and optimization is an important consideration. Genetic algorithms are proposed for design optimization of PMD motors because of their independence to objective function structure and its derivative. In this paper genetic algorithms are evaluated for PMDC motor design optimization. an introduction is first presented about PMDC motors, general design procedure and elements of their optimization. Genetic algorithms are then briefly described. Finally results of optimization by genetic algorithms are compared with the one obtained using a conventional method

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.

Science.gov (United States)

Yuan, Gonglin; Duan, Xiabin; Liu, Wenjie; Wang, Xiaoliang; Cui, Zengru; Sheng, Zhou

2015-01-01

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1) βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.

A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

Science.gov (United States)

Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

2005-01-01

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

Directory of Open Access Journals (Sweden)

Hailong Wang

2018-01-01

Full Text Available The backtracking search optimization algorithm (BSA is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.

Development of GPT-based optimization algorithm

International Nuclear Information System (INIS)

White, J.R.; Chapman, D.M.; Biswas, D.

1985-01-01

The University of Lowell and Westinghouse Electric Corporation are involved in a joint effort to evaluate the potential benefits of generalized/depletion perturbation theory (GPT/DTP) methods for a variety of light water reactor (LWR) physics applications. One part of that work has focused on the development of a GPT-based optimization algorithm for the overall design, analysis, and optimization of LWR reload cores. The use of GPT sensitivity data in formulating the fuel management optimization problem is conceptually straightforward; it is the actual execution of the concept that is challenging. Thus, the purpose of this paper is to address some of the major difficulties, to outline our approach to these problems, and to present some illustrative examples of an efficient GTP-based optimization scheme

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

Science.gov (United States)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

Optimization of multi-stage dynamic treatment regimes utilizing accumulated data.

Science.gov (United States)

Huang, Xuelin; Choi, Sangbum; Wang, Lu; Thall, Peter F

2015-11-20

In medical therapies involving multiple stages, a physician's choice of a subject's treatment at each stage depends on the subject's history of previous treatments and outcomes. The sequence of decisions is known as a dynamic treatment regime or treatment policy. We consider dynamic treatment regimes in settings where each subject's final outcome can be defined as the sum of longitudinally observed values, each corresponding to a stage of the regime. Q-learning, which is a backward induction method, is used to first optimize the last stage treatment then sequentially optimize each previous stage treatment until the first stage treatment is optimized. During this process, model-based expectations of outcomes of late stages are used in the optimization of earlier stages. When the outcome models are misspecified, bias can accumulate from stage to stage and become severe, especially when the number of treatment stages is large. We demonstrate that a modification of standard Q-learning can help reduce the accumulated bias. We provide a computational algorithm, estimators, and closed-form variance formulas. Simulation studies show that the modified Q-learning method has a higher probability of identifying the optimal treatment regime even in settings with misspecified models for outcomes. It is applied to identify optimal treatment regimes in a study for advanced prostate cancer and to estimate and compare the final mean rewards of all the possible discrete two-stage treatment sequences. Copyright © 2015 John Wiley & Sons, Ltd.

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

Directory of Open Access Journals (Sweden)

Leilei Cao

2016-01-01

Full Text Available A Guiding Evolutionary Algorithm (GEA with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Directory of Open Access Journals (Sweden)

Simon Fong

2014-01-01

Full Text Available Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario.

Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms

Science.gov (United States)

Deb, Suash; Yang, Xin-She

2014-01-01

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

Science.gov (United States)

Chen, Wei

2015-07-01

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

Low emittance lattice optimization using a multi-objective evolutionary algorithm

International Nuclear Information System (INIS)

Gao Weiwei; Wang Lin; Li Weimin; He Duohui

2011-01-01

A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure. (authors)

On algorithm for building of optimal α-decision trees

KAUST Repository

Alkhalid, Abdulaziz; Chikalov, Igor; Moshkov, Mikhail

2010-01-01

The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic

Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Li Ran

2017-01-01

Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.

Firefly Mating Algorithm for Continuous Optimization Problems

Directory of Open Access Journals (Sweden)

Amarita Ritthipakdee

2017-01-01

Full Text Available This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA, for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i the mutual attraction between males and females causes them to mate and (ii fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.

Multi-machine power system stabilizers design using chaotic optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Shayeghi, H., E-mail: hshayeghi@gmail.co [Technical Engineering Department, University of Mohaghegh Ardabili, Ardabil (Iran, Islamic Republic of); Shayanfar, H.A. [Center of Excellence for Power System Automation and Operation, Electrical Engineering Department, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Jalilzadeh, S.; Safari, A. [Technical Engineering Department, Zanjan University, Zanjan (Iran, Islamic Republic of)

2010-07-15

In this paper, a multiobjective design of the multi-machine power system stabilizers (PSSs) using chaotic optimization algorithm (COA) is proposed. Chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from the local optimum, is a promising tool for the engineering applications. The PSSs parameters tuning problem is converted to an optimization problem which is solved by a chaotic optimization algorithm based on Lozi map. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed chaotic optimization problem introduces chaos mapping using Lozi map chaotic sequences which increases its convergence rate and resulting precision. Two different objective functions are proposed in this study for the PSSs design problem. The first objective function is the eigenvalues based comprising the damping factor, and the damping ratio of the lightly damped electro-mechanical modes, while the second is the time domain-based multi-objective function. The robustness of the proposed COA-based PSSs (COAPSS) is verified on a multi-machine power system under different operating conditions and disturbances. The results of the proposed COAPSS are demonstrated through eigenvalue analysis, nonlinear time-domain simulation and some performance indices. In addition, the potential and superiority of the proposed method over the classical approach and genetic algorithm is demonstrated.

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN

Science.gov (United States)

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures

Science.gov (United States)

Kaveh, A.; Ilchi Ghazaan, M.

2018-02-01

In this article a hybrid algorithm based on a vibrating particles system (VPS) algorithm, multi-design variable configuration (Multi-DVC) cascade optimization, and an upper bound strategy (UBS) is presented for global optimization of large-scale dome truss structures. The new algorithm is called MDVC-UVPS in which the VPS algorithm acts as the main engine of the algorithm. The VPS algorithm is one of the most recent multi-agent meta-heuristic algorithms mimicking the mechanisms of damped free vibration of single degree of freedom systems. In order to handle a large number of variables, cascade sizing optimization utilizing a series of DVCs is used. Moreover, the UBS is utilized to reduce the computational time. Various dome truss examples are studied to demonstrate the effectiveness and robustness of the proposed method, as compared to some existing structural optimization techniques. The results indicate that the MDVC-UVPS technique is a powerful search and optimization method for optimizing structural engineering problems.

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Meiping Wang

2016-01-01

Full Text Available We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR model-optimized particle swarm optimization (PSO algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR, and artificial neural network (ANN through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.

Directory of Open Access Journals (Sweden)

Gonglin Yuan

Full Text Available Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1 βk ≥ 0 2 the search direction has the trust region property without the use of any line search method 3 the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.

Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

Science.gov (United States)

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and

Optical flow optimization using parallel genetic algorithm

Science.gov (United States)

Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe

2011-06-01

A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.

Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm

Science.gov (United States)

Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang

2017-09-01

Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.

Advanced and flexible genetic algorithms for BWR fuel loading pattern optimization

International Nuclear Information System (INIS)

Martin-del-Campo, Cecilia; Palomera-Perez, Miguel-Angel; Francois, Juan-Luis

2009-01-01

This work proposes advances in the implementation of a flexible genetic algorithm (GA) for fuel loading pattern optimization for Boiling Water Reactors (BWRs). In order to avoid specific implementations of genetic operators and to obtain a more flexible treatment, a binary representation of the solution was implemented; this representation had to take into account that a little change in the genotype must correspond to a little change in the phenotype. An identifier number is assigned to each assembly by means of a Gray Code of 7 bits and the solution (the loading pattern) is represented by a binary chain of 777 bits of length. Another important contribution is the use of a Fitness Function which includes a Heuristic Function and an Objective Function. The Heuristic Function which is defined to give flexibility on the application of a set of positioning rules based on knowledge, and the Objective Function that contains all the parameters which qualify the neutronic and thermal hydraulic performances of each loading pattern. Experimental results illustrating the effectiveness and flexibility of this optimization algorithm are presented and discussed.

A Linearized Relaxing Algorithm for the Specific Nonlinear Optimization Problem

Directory of Open Access Journals (Sweden)

Mio Horai

2016-01-01

Full Text Available We propose a new method for the specific nonlinear and nonconvex global optimization problem by using a linear relaxation technique. To simplify the specific nonlinear and nonconvex optimization problem, we transform the problem to the lower linear relaxation form, and we solve the linear relaxation optimization problem by the Branch and Bound Algorithm. Under some reasonable assumptions, the global convergence of the algorithm is certified for the problem. Numerical results show that this method is more efficient than the previous methods.

Surface roughness optimization in machining of AZ31 magnesium alloy using ABC algorithm

Directory of Open Access Journals (Sweden)

Abhijith

2018-01-01

Full Text Available Magnesium alloys serve as excellent substitutes for materials traditionally used for engine block heads in automobiles and gear housings in aircraft industries. AZ31 is a magnesium alloy finds its applications in orthopedic implants and cardiovascular stents. Surface roughness is an important parameter in the present manufacturing sector. In this work optimization techniques namely firefly algorithm (FA, particle swarm optimization (PSO and artificial bee colony algorithm (ABC which are based on swarm intelligence techniques, have been implemented to optimize the machining parameters namely cutting speed, feed rate and depth of cut in order to achieve minimum surface roughness. The parameter Ra has been considered for evaluating the surface roughness. Comparing the performance of ABC algorithm with FA and PSO algorithm, which is a widely used optimization algorithm in machining studies, the results conclude that ABC produces better optimization when compared to FA and PSO for optimizing surface roughness of AZ 31.

Automatic Circuit Design and Optimization Using Modified PSO Algorithm

Directory of Open Access Journals (Sweden)

Subhash Patel

2016-04-01

Full Text Available In this work, we have proposed modified PSO algorithm based optimizer for automatic circuit design. The performance of the modified PSO algorithm is compared with two other evolutionary algorithms namely ABC algorithm and standard PSO algorithm by designing two stage CMOS operational amplifier and bulk driven OTA in 130nm technology. The results show the robustness of the proposed algorithm. With modified PSO algorithm, the average design error for two stage op-amp is only 0.054% in contrast to 3.04% for standard PSO algorithm and 5.45% for ABC algorithm. For bulk driven OTA, average design error is 1.32% with MPSO compared to 4.70% with ABC algorithm and 5.63% with standard PSO algorithm.

A new improved artificial bee colony algorithm for ship hull form optimization

Science.gov (United States)

Huang, Fuxin; Wang, Lijue; Yang, Chi

2016-04-01

The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

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M. Gorji-Bandpy

2012-01-01

Full Text Available A new method called mutable smart bee (MSB algorithm proposed for cooperative optimizing of the maximum power output (MPO and minimum entropy generation (MEG of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA, improved particle swarm optimization (IPSO, Lukasik firefly algorithm (LFFA, and self-adaptive penalty function genetic algorithm (SAPF-GA. According to obtained results, it can be concluded that Mutable Smart Bee (MSB is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

Science.gov (United States)

Garro, Beatriz A; Vázquez, Roberto A

2015-01-01

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

A new evolutionary algorithm with LQV learning for combinatorial problems optimization

International Nuclear Information System (INIS)

Machado, Marcelo Dornellas; Schirru, Roberto

2000-01-01

Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for combinatorial problems optimization. In this work, a new learning mode, to be used by the population-based incremental learning algorithm, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process known as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors, in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problems. Due to the fact that the reload problem is a combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

Science.gov (United States)

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem. PMID:25243220

Discrete bat algorithm for optimal problem of permutation flow shop scheduling.

Science.gov (United States)

Luo, Qifang; Zhou, Yongquan; Xie, Jian; Ma, Mingzhi; Li, Liangliang

2014-01-01

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

A synthesis/design optimization algorithm for Rankine cycle based energy systems

International Nuclear Information System (INIS)

Toffolo, Andrea

2014-01-01

The algorithm presented in this work has been developed to search for the optimal topology and design parameters of a set of Rankine cycles forming an energy system that absorbs/releases heat at different temperature levels and converts part of the absorbed heat into electricity. This algorithm can deal with several applications in the field of energy engineering: e.g., steam cycles or bottoming cycles in combined/cogenerative plants, steam networks, low temperature organic Rankine cycles. The main purpose of this algorithm is to overcome the limitations of the search space introduced by the traditional mixed-integer programming techniques, which assume that possible solutions are derived from a single superstructure embedding them all. The algorithm presented in this work is a hybrid evolutionary/traditional optimization algorithm organized in two levels. A complex original codification of the topology and the intensive design parameters of the system is managed by the upper level evolutionary algorithm according to the criteria set by the HEATSEP method, which are used for the first time to automatically synthesize a “basic” system configuration from a set of elementary thermodynamic cycles. The lower SQP (sequential quadratic programming) algorithm optimizes the objective function(s) with respect to cycle mass flow rates only, taking into account the heat transfer feasibility constraint within the undefined heat transfer section. A challenging example of application is also presented to show the capabilities of the algorithm. - Highlights: • Energy systems based on Rankine cycles are used in many applications. • A hybrid algorithm is proposed to optimize the synthesis/design of such systems. • The topology of the candidate solutions is not limited by a superstructure. • Topology is managed by the genetic operators of the upper level algorithm. • The effectiveness of the algorithm is proved in a complex test case

Exergetic optimization of turbofan engine with genetic algorithm method

Energy Technology Data Exchange (ETDEWEB)

Turan, Onder [Anadolu University, School of Civil Aviation (Turkey)], e-mail: onderturan@anadolu.edu.tr

2011-07-01

With the growth of passenger numbers, emissions from the aeronautics sector are increasing and the industry is now working on improving engine efficiency to reduce fuel consumption. The aim of this study is to present the use of genetic algorithms, an optimization method based on biological principles, to optimize the exergetic performance of turbofan engines. The optimization was carried out using exergy efficiency, overall efficiency and specific thrust of the engine as evaluation criteria and playing on pressure and bypass ratio, turbine inlet temperature and flight altitude. Results showed exergy efficiency can be maximized with higher altitudes, fan pressure ratio and turbine inlet temperature; the turbine inlet temperature is the most important parameter for increased exergy efficiency. This study demonstrated that genetic algorithms are effective in optimizing complex systems in a short time.

Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system

International Nuclear Information System (INIS)

Berrazouane, S.; Mohammedi, K.

2014-01-01

Highlights: • Optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. • Comparison between optimized fuzzy logic controller based on cuckoo search and swarm intelligent. • Loss of power supply probability and levelized energy cost are introduced. - Abstract: This paper presents the development of an optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. The FLC inputs are batteries state of charge (SOC) and net power flow, FLC outputs are the power rate of batteries, photovoltaic and diesel generator. Data for weekly solar irradiation, ambient temperature and load profile are used to tune the proposed controller by using cuckoo search algorithm. The optimized FLC is able to minimize loss of power supply probability (LPSP), excess energy (EE) and levelized energy cost (LEC). Moreover, the results of CS optimization are better than of particle swarm optimization PSO for fuzzy system controller

Application of Hybrid Optimization Algorithm in the Synthesis of Linear Antenna Array

Directory of Open Access Journals (Sweden)

Ezgi Deniz Ülker

2014-01-01

Full Text Available The use of hybrid algorithms for solving real-world optimization problems has become popular since their solution quality can be made better than the algorithms that form them by combining their desirable features. The newly proposed hybrid method which is called Hybrid Differential, Particle, and Harmony (HDPH algorithm is different from the other hybrid forms since it uses all features of merged algorithms in order to perform efficiently for a wide variety of problems. In the proposed algorithm the control parameters are randomized which makes its implementation easy and provides a fast response. This paper describes the application of HDPH algorithm to linear antenna array synthesis. The results obtained with the HDPH algorithm are compared with three merged optimization techniques that are used in HDPH. The comparison shows that the performance of the proposed algorithm is comparatively better in both solution quality and robustness. The proposed hybrid algorithm HDPH can be an efficient candidate for real-time optimization problems since it yields reliable performance at all times when it gets executed.

Multiobjective generalized extremal optimization algorithm for simulation of daylight illuminants

Science.gov (United States)

Kumar, Srividya Ravindra; Kurian, Ciji Pearl; Gomes-Borges, Marcos Eduardo

2017-10-01

Daylight illuminants are widely used as references for color quality testing and optical vision testing applications. Presently used daylight simulators make use of fluorescent bulbs that are not tunable and occupy more space inside the quality testing chambers. By designing a spectrally tunable LED light source with an optimal number of LEDs, cost, space, and energy can be saved. This paper describes an application of the generalized extremal optimization (GEO) algorithm for selection of the appropriate quantity and quality of LEDs that compose the light source. The multiobjective approach of this algorithm tries to get the best spectral simulation with minimum fitness error toward the target spectrum, correlated color temperature (CCT) the same as the target spectrum, high color rendering index (CRI), and luminous flux as required for testing applications. GEO is a global search algorithm based on phenomena of natural evolution and is especially designed to be used in complex optimization problems. Several simulations have been conducted to validate the performance of the algorithm. The methodology applied to model the LEDs, together with the theoretical basis for CCT and CRI calculation, is presented in this paper. A comparative result analysis of M-GEO evolutionary algorithm with the Levenberg-Marquardt conventional deterministic algorithm is also presented.

A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

Energy Technology Data Exchange (ETDEWEB)

Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

2015-07-01

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

Optimization of magnet sorting in a storage ring using genetic algorithms

International Nuclear Information System (INIS)

Chen Jia; Wang Lin; Li Weimin; Gao Weiwei

2013-01-01

In this paper, the genetic algorithms are applied to the optimization problem of magnet sorting in an electron storage ring, according to which the objectives are set so that the closed orbit distortion and beta beating can be minimized and the dynamic aperture maximized. The sorting of dipole, quadrupole and sextupole magnets is optimized while the optimization results show the power of the application of genetic algorithms in magnet sorting. (authors)

Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

Directory of Open Access Journals (Sweden)

Mi-Yuan Shan

2013-01-01

Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.

New preconditioned conjugate gradient algorithms for nonlinear unconstrained optimization problems

International Nuclear Information System (INIS)

Al-Bayati, A.; Al-Asadi, N.

1997-01-01

This paper presents two new predilection conjugate gradient algorithms for nonlinear unconstrained optimization problems and examines their computational performance. Computational experience shows that the new proposed algorithms generally imp lone the efficiency of Nazareth's [13] preconditioned conjugate gradient algorithm. (authors). 16 refs., 1 tab

Filter Pattern Search Algorithms for Mixed Variable Constrained Optimization Problems

National Research Council Canada - National Science Library

Abramson, Mark A; Audet, Charles; Dennis, Jr, J. E

2004-01-01

.... This class combines and extends the Audet-Dennis Generalized Pattern Search (GPS) algorithms for bound constrained mixed variable optimization, and their GPS-filter algorithms for general nonlinear constraints...

Genetic local search algorithm for optimization design of diffractive optical elements.

Science.gov (United States)

Zhou, G; Chen, Y; Wang, Z; Song, H

1999-07-10

We propose a genetic local search algorithm (GLSA) for the optimization design of diffractive optical elements (DOE's). This hybrid algorithm incorporates advantages of both genetic algorithm (GA) and local search techniques. It appears better able to locate the global minimum compared with a canonical GA. Sample cases investigated here include the optimization design of binary-phase Dammann gratings, continuous surface-relief grating array generators, and a uniform top-hat focal plane intensity profile generator. Two GLSA's whose incorporated local search techniques are the hill-climbing method and the simulated annealing algorithm are investigated. Numerical experimental results demonstrate that the proposed algorithm is highly efficient and robust. DOE's that have high diffraction efficiency and excellent uniformity can be achieved by use of the algorithm we propose.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

International Nuclear Information System (INIS)

Huang, Xiaobiao; Safranek, James

2014-01-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

Energy Technology Data Exchange (ETDEWEB)

Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

Science.gov (United States)

Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun

2017-09-01

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

Optimization of Antennas using a Hybrid Genetic-Algorithm Space-Mapping Algorithm

DEFF Research Database (Denmark)

Pantoja, M.F.; Bretones, A.R.; Meincke, Peter

2006-01-01

A hybrid global-local optimization technique for the design of antennas is presented. It consists of the subsequent application of a Genetic Algorithm (GA) that employs coarse models in the simulations and a space mapping (SM) that refines the solution found in the previous stage. The technique...

SU-F-J-133: Adaptive Radiation Therapy with a Four-Dimensional Dose Calculation Algorithm That Optimizes Dose Distribution Considering Breathing Motion

Energy Technology Data Exchange (ETDEWEB)

Ali, I; Algan, O; Ahmad, S [University of Oklahoma Health Sciences, Oklahoma City, OK (United States); Alsbou, N [University of Central Oklahoma, Edmond, OK (United States)

2016-06-15

Purpose: To model patient motion and produce four-dimensional (4D) optimized dose distributions that consider motion-artifacts in the dose calculation during the treatment planning process. Methods: An algorithm for dose calculation is developed where patient motion is considered in dose calculation at the stage of the treatment planning. First, optimal dose distributions are calculated for the stationary target volume where the dose distributions are optimized considering intensity-modulated radiation therapy (IMRT). Second, a convolution-kernel is produced from the best-fitting curve which matches the motion trajectory of the patient. Third, the motion kernel is deconvolved with the initial dose distribution optimized for the stationary target to produce a dose distribution that is optimized in four-dimensions. This algorithm is tested with measured doses using a mobile phantom that moves with controlled motion patterns. Results: A motion-optimized dose distribution is obtained from the initial dose distribution of the stationary target by deconvolution with the motion-kernel of the mobile target. This motion-optimized dose distribution is equivalent to that optimized for the stationary target using IMRT. The motion-optimized and measured dose distributions are tested with the gamma index with a passing rate of >95% considering 3% dose-difference and 3mm distance-to-agreement. If the dose delivery per beam takes place over several respiratory cycles, then the spread-out of the dose distributions is only dependent on the motion amplitude and not affected by motion frequency and phase. This algorithm is limited to motion amplitudes that are smaller than the length of the target along the direction of motion. Conclusion: An algorithm is developed to optimize dose in 4D. Besides IMRT that provides optimal dose coverage for a stationary target, it extends dose optimization to 4D considering target motion. This algorithm provides alternative to motion management

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.

Parameter optimization of differential evolution algorithm for automatic playlist generation problem

Science.gov (United States)

Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

2017-11-01

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

GENETIC ALGORITHM IN OPTIMIZATION DESIGN OF INTERIOR PERMANENT MAGNET SYNCHRONOUS MOTOR

Directory of Open Access Journals (Sweden)

Phuong Le Ngo

2017-01-01

Full Text Available Classical method of designing electric motors help to achieve functional motor, but doesn’t ensure minimal cost in manufacturing and operating. Recently optimization is becoming an important part in modern electric motor design process. The objective of the optimization process is usually to minimize cost, energy loss, mass, or maximize torque and efficiency. Most of the requirements for electrical machine design are in contradiction to each other (reduction in volume or mass, improvement in efficiency etc.. Optimization in design permanent magnet synchronous motor (PMSM is a multi-objective optimization problem. There are two approaches for solving this problem, one of them is evolution algorithms, which gain a lot of attentions recently. For designing PMSM, evolution algorithms are more attractive approach. Genetic algorithm is one of the most common. This paper presents components and procedures of genetic algorithms, and its implementation on computer. In optimization process, analytical and finite element method are used together for better performance and precision. Result from optimization process is a set of solutions, from which engineer will choose one. This method was used to design a permanent magnet synchronous motor based on an asynchronous motor type АИР112МВ8.

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

Directory of Open Access Journals (Sweden)

N. Sri Madhava Raja

2014-01-01

Full Text Available Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD guided firefly algorithm (FA. A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR, structural similarity (SSIM index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

Solving the wind farm layout optimization problem using random search algorithm

DEFF Research Database (Denmark)

Feng, Ju; Shen, Wen Zhong

2015-01-01

, in which better results than the genetic algorithm (GA) and the old version of the RS algorithm are obtained. Second it is applied to the Horns Rev 1 WF, and the optimized layouts obtain a higher power production than its original layout, both for the real scenario and for two constructed scenarios......Wind farm (WF) layout optimization is to find the optimal positions of wind turbines (WTs) inside a WF, so as to maximize and/or minimize a single objective or multiple objectives, while satisfying certain constraints. In this work, a random search (RS) algorithm based on continuous formulation....... In this application, it is also found that in order to get consistent and reliable optimization results, up to 360 or more sectors for wind direction have to be used. Finally, considering the inevitable inter-annual variations in the wind conditions, the robustness of the optimized layouts against wind condition...

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

Directory of Open Access Journals (Sweden)

Shuqiang Huang

2017-01-01

Full Text Available Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest and the population optimum (gbest; thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-01

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. PMID:28117735

Technical Note: Improving the VMERGE treatment planning algorithm for rotational radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Gaddy, Melissa R., E-mail: mrgaddy@ncsu.edu; Papp, Dávid, E-mail: dpapp@ncsu.edu [Department of Mathematics, North Carolina State University, Raleigh, North Carolina 27695-8205 (United States)

2016-07-15

Purpose: The authors revisit the VMERGE treatment planning algorithm by Craft et al. [“Multicriteria VMAT optimization,” Med. Phys. 39, 686–696 (2012)] for arc therapy planning and propose two changes to the method that are aimed at improving the achieved trade-off between treatment time and plan quality at little additional planning time cost, while retaining other desirable properties of the original algorithm. Methods: The original VMERGE algorithm first computes an “ideal,” high quality but also highly time consuming treatment plan that irradiates the patient from all possible angles in a fine angular grid with a highly modulated beam and then makes this plan deliverable within practical treatment time by an iterative fluence map merging and sequencing algorithm. We propose two changes to this method. First, we regularize the ideal plan obtained in the first step by adding an explicit constraint on treatment time. Second, we propose a different merging criterion that comprises of identifying and merging adjacent maps whose merging results in the least degradation of radiation dose. Results: The effect of both suggested modifications is evaluated individually and jointly on clinical prostate and paraspinal cases. Details of the two cases are reported. Conclusions: In the authors’ computational study they found that both proposed modifications, especially the regularization, yield noticeably improved treatment plans for the same treatment times than what can be obtained using the original VMERGE method. The resulting plans match the quality of 20-beam step-and-shoot IMRT plans with a delivery time of approximately 2 min.

Hooke–Jeeves Method-used Local Search in a Hybrid Global Optimization Algorithm

Directory of Open Access Journals (Sweden)

V. D. Sulimov

2014-01-01

Full Text Available Modern methods for optimization investigation of complex systems are based on development and updating the mathematical models of systems because of solving the appropriate inverse problems. Input data desirable for solution are obtained from the analysis of experimentally defined consecutive characteristics for a system or a process. Causal characteristics are the sought ones to which equation coefficients of mathematical models of object, limit conditions, etc. belong. The optimization approach is one of the main ones to solve the inverse problems. In the main case it is necessary to find a global extremum of not everywhere differentiable criterion function. Global optimization methods are widely used in problems of identification and computation diagnosis system as well as in optimal control, computing to-mography, image restoration, teaching the neuron networks, other intelligence technologies. Increasingly complicated systems of optimization observed during last decades lead to more complicated mathematical models, thereby making solution of appropriate extreme problems significantly more difficult. A great deal of practical applications may have the problem con-ditions, which can restrict modeling. As a consequence, in inverse problems the criterion functions can be not everywhere differentiable and noisy. Available noise means that calculat-ing the derivatives is difficult and unreliable. It results in using the optimization methods without calculating the derivatives.An efficiency of deterministic algorithms of global optimization is significantly restrict-ed by their dependence on the extreme problem dimension. When the number of variables is large they use the stochastic global optimization algorithms. As stochastic algorithms yield too expensive solutions, so this drawback restricts their applications. Developing hybrid algo-rithms that combine a stochastic algorithm for scanning the variable space with deterministic local search

Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data

Science.gov (United States)

Martins, Fabio J. W. A.; Foucaut, Jean-Marc; Thomas, Lionel; Azevedo, Luis F. A.; Stanislas, Michel

2015-08-01

Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time.

Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data

International Nuclear Information System (INIS)

Martins, Fabio J W A; Foucaut, Jean-Marc; Stanislas, Michel; Thomas, Lionel; Azevedo, Luis F A

2015-01-01

Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time. (paper)

SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time

International Nuclear Information System (INIS)

Tiwari, P; Xie, Y; Chen, Y; Deasy, J

2014-01-01

Purpose: The IMRT optimization problem requires substantial computer time to find optimal dose distributions because of the large number of variables and constraints. Voxel sampling reduces the number of constraints and accelerates the optimization process, but usually deteriorates the quality of the dose distributions to the organs. We propose a novel sampling algorithm that accelerates the IMRT optimization process without significantly deteriorating the quality of the dose distribution. Methods: We included all boundary voxels, as well as a sampled fraction of interior voxels of organs in the optimization. We selected a fraction of interior voxels using a clustering algorithm, that creates clusters of voxels that have similar influence matrix signatures. A few voxels are selected from each cluster based on the pre-set sampling rate. Results: We ran sampling and no-sampling IMRT plans for de-identified head and neck treatment plans. Testing with the different sampling rates, we found that including 10% of inner voxels produced the good dose distributions. For this optimal sampling rate, the algorithm accelerated IMRT optimization by a factor of 2–3 times with a negligible loss of accuracy that was, on average, 0.3% for common dosimetric planning criteria. Conclusion: We demonstrated that a sampling could be developed that reduces optimization time by more than a factor of 2, without significantly degrading the dose quality

Optimal design of regional wastewater pipelines and treatment plant systems.

Science.gov (United States)

Brand, Noam; Ostfeld, Avi

2011-01-01

This manuscript describes the application of a genetic algorithm model for the optimal design of regional wastewater systems comprised of transmission gravitational and pumping sewer pipelines, decentralized treatment plants, and end users of reclaimed wastewater. The algorithm seeks the diameter size of the designed pipelines and their flow distribution simultaneously, the number of treatment plants and their size and location, the pump power, and the required excavation work. The model capabilities are demonstrated through a simplified example application using base runs and sensitivity analyses. Scaling of the proposed methodology to real life wastewater collection and treatment plants design problems needs further testing and developments. The model is coded in MATLAB using the GATOOL toolbox and is available from the authors.

Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...

Science.gov (United States)

With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th

Self-adaptive global best harmony search algorithm applied to reactor core fuel management optimization

International Nuclear Information System (INIS)

Poursalehi, N.; Zolfaghari, A.; Minuchehr, A.; Valavi, K.

2013-01-01

Highlights: • SGHS enhanced the convergence rate of LPO using some improvements in comparison to basic HS and GHS. • SGHS optimization algorithm obtained averagely better fitness relative to basic HS and GHS algorithms. • Upshot of the SGHS implementation in the LPO reveals its flexibility, efficiency and reliability. - Abstract: The aim of this work is to apply the new developed optimization algorithm, Self-adaptive Global best Harmony Search (SGHS), for PWRs fuel management optimization. SGHS algorithm has some modifications in comparison with basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms such as dynamically change of parameters. For the demonstration of SGHS ability to find an optimal configuration of fuel assemblies, basic Harmony Search (HS) and Global-best Harmony Search (GHS) algorithms also have been developed and investigated. For this purpose, Self-adaptive Global best Harmony Search Nodal Expansion package (SGHSNE) has been developed implementing HS, GHS and SGHS optimization algorithms for the fuel management operation of nuclear reactor cores. This package uses developed average current nodal expansion code which solves the multi group diffusion equation by employment of first and second orders of Nodal Expansion Method (NEM) for two dimensional, hexagonal and rectangular geometries, respectively, by one node per a FA. Loading pattern optimization was performed using SGHSNE package for some test cases to present the SGHS algorithm capability in converging to near optimal loading pattern. Results indicate that the convergence rate and reliability of the SGHS method are quite promising and practically, SGHS improves the quality of loading pattern optimization results relative to HS and GHS algorithms. As a result, it has the potential to be used in the other nuclear engineering optimization problems

Application of genetic algorithms to in-core nuclear fuel management optimization

International Nuclear Information System (INIS)

Poon, P.W.; Parks, G.T.

1993-01-01

The search for an optimal arrangement of fresh and burnt fuel and control material within the core of a PWR represents a formidable optimization problem. The approach of combining the robust optimization capabilities of the Simulated Annealing (SA) algorithm with the computational speed of a Generalized Perturbation Theory (GPT) based evaluation methodology in the code FORMOSA has proved to be very effective. In this paper, we show that the incorporation of another stochastic search technique, a Genetic Algorithm, results in comparable optimization performance on serial computers and offers substantially superior performance on parallel machines. (orig.)

Bio Inspired Algorithms in Single and Multiobjective Reliability Optimization

DEFF Research Database (Denmark)

Madsen, Henrik; Albeanu, Grigore; Burtschy, Bernard

2014-01-01

Non-traditional search and optimization methods based on natural phenomena have been proposed recently in order to avoid local or unstable behavior when run towards an optimum state. This paper describes the principles of bio inspired algorithms and reports on Migration Algorithms and Bees...

Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

Directory of Open Access Journals (Sweden)

B. Thamaraikannan

2014-01-01

Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

Optimization of image processing algorithms on mobile platforms

Science.gov (United States)

Poudel, Pramod; Shirvaikar, Mukul

2011-03-01

This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM and a DSP core supported by shared memory, is presented with implementation details. The target platform chosen is the popular OMAP 3530 processor for embedded media systems. It has an asymmetric dual-core architecture with an ARM Cortex-A8 and a TMS320C64x Digital Signal Processor (DSP). The development platform was the BeagleBoard with 256 MB of NAND RAM and 256 MB SDRAM memory. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template matching tasks such as face-recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core which runs a popular operating system such as Linux or Windows CE. However, the DSP is architecturally more efficient at handling DFT algorithms. The algorithms are tested on a variety of images and performance results are presented measuring the speedup obtained due to dual-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks, while the DSP addresses performance-hungry algorithms.

Application of particle swarm optimization algorithm in the heating system planning problem.

Science.gov (United States)

Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

2013-01-01

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem.

Effects of systematic phase errors on optimized quantum random-walk search algorithm

International Nuclear Information System (INIS)

Zhang Yu-Chao; Bao Wan-Su; Wang Xiang; Fu Xiang-Qun

2015-01-01

This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover’s algorithm. (paper)

Global structural optimizations of surface systems with a genetic algorithm

International Nuclear Information System (INIS)

Chuang, Feng-Chuan

2005-01-01

Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al n (n up to 23) were performed using a genetic algorithm coupled with a tight-binding potential. Second, a genetic algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT

Directory of Open Access Journals (Sweden)

Xiaohua Nie

2017-01-01

Full Text Available Cat Swarm Optimization (CSO algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO algorithm, the application of CSO is greatly limited by the drawback of “premature convergence,” that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.

Chaos Quantum-Behaved Cat Swarm Optimization Algorithm and Its Application in the PV MPPT.

Science.gov (United States)

Nie, Xiaohua; Wang, Wei; Nie, Haoyao

2017-01-01

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of "premature convergence," that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In order to surmount the shortcomings of CSO, Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed in this paper. Firstly, Quantum-behaved Cat Swarm Optimization (QCSO) algorithm improves the accuracy of the CSO algorithm, because it is easy to fall into the local optimum in the later stage. Chaos Quantum-behaved Cat Swarm Optimization (CQCSO) algorithm is proposed by introducing tent map for jumping out of local optimum in this paper. Secondly, CQCSO has been applied in the simulation of five different test functions, showing higher accuracy and less time consumption than CSO and QCSO. Finally, photovoltaic MPPT model and experimental platform are established and global maximum power point tracking control strategy is achieved by CQCSO algorithm, the effectiveness and efficiency of which have been verified by both simulation and experiment.

Sustainable logistics and transportation optimization models and algorithms

CERN Document Server

Gakis, Konstantinos; Pardalos, Panos

2017-01-01

Focused on the logistics and transportation operations within a supply chain, this book brings together the latest models, algorithms, and optimization possibilities. Logistics and transportation problems are examined within a sustainability perspective to offer a comprehensive assessment of environmental, social, ethical, and economic performance measures. Featured models, techniques, and algorithms may be used to construct policies on alternative transportation modes and technologies, green logistics, and incentives by the incorporation of environmental, economic, and social measures. Researchers, professionals, and graduate students in urban regional planning, logistics, transport systems, optimization, supply chain management, business administration, information science, mathematics, and industrial and systems engineering will find the real life and interdisciplinary issues presented in this book informative and useful.

Time Optimized Algorithm for Web Document Presentation Adaptation

DEFF Research Database (Denmark)

Pan, Rong; Dolog, Peter

2010-01-01

Currently information on the web is accessed through different devices. Each device has its own properties such as resolution, size, and capabilities to display information in different format and so on. This calls for adaptation of information presentation for such platforms. This paper proposes...... content-optimized and time-optimized algorithms for information presentation adaptation for different devices based on its hierarchical model. The model is formalized in order to experiment with different algorithms.......Currently information on the web is accessed through different devices. Each device has its own properties such as resolution, size, and capabilities to display information in different format and so on. This calls for adaptation of information presentation for such platforms. This paper proposes...

Optimized Data Indexing Algorithms for OLAP Systems

Directory of Open Access Journals (Sweden)

Lucian BORNAZ

2010-12-01

Full Text Available The need to process and analyze large data volumes, as well as to convey the information contained therein to decision makers naturally led to the development of OLAP systems. Similarly to SGBDs, OLAP systems must ensure optimum access to the storage environment. Although there are several ways to optimize database systems, implementing a correct data indexing solution is the most effective and less costly. Thus, OLAP uses indexing algorithms for relational data and n-dimensional summarized data stored in cubes. Today database systems implement derived indexing algorithms based on well-known Tree, Bitmap and Hash indexing algorithms. This is because no indexing algorithm provides the best performance for any particular situation (type, structure, data volume, application. This paper presents a new n-dimensional cube indexing algorithm, derived from the well known B-Tree index, which indexes data stored in data warehouses taking in consideration their multi-dimensional nature and provides better performance in comparison to the already implemented Tree-like index types.

A genetic algorithm for the optimization of fiber angles in composite laminates

International Nuclear Information System (INIS)

Hwang, Shun Fa; Hsu, Ya Chu; Chen, Yuder

2014-01-01

A genetic algorithm for the optimization of composite laminates is proposed in this work. The well-known roulette selection criterion, one-point crossover operator, and uniform mutation operator are used in this genetic algorithm to create the next population. To improve the hill-climbing capability of the algorithm, adaptive mechanisms designed to adjust the probabilities of the crossover and mutation operators are included, and the elite strategy is enforced to ensure the quality of the optimum solution. The proposed algorithm includes a new operator called the elite comparison, which compares and uses the differences in the design variables of the two best solutions to find possible combinations. This genetic algorithm is tested in four optimization problems of composite laminates. Specifically, the effect of the elite comparison operator is evaluated. Results indicate that the elite comparison operator significantly accelerates the convergence of the algorithm, which thus becomes a good candidate for the optimization of composite laminates.

Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle–Pock algorithm

DEFF Research Database (Denmark)

Sidky, Emil Y.; Jørgensen, Jakob Heide; Pan, Xiaochuan

2012-01-01

The primal–dual optimization algorithm developed in Chambolle and Pock (CP) (2011 J. Math. Imag. Vis. 40 1–26) is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems...... for the purpose of designing iterative image reconstruction algorithms for CT. The primal–dual algorithm is briefly summarized in this paper, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application...

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Science.gov (United States)

Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

Advanced metaheuristic algorithms for laser optimization

International Nuclear Information System (INIS)

Tomizawa, H.

2010-01-01

A laser is one of the most important experimental tools. In synchrotron radiation field, lasers are widely used for experiments with Pump-Probe techniques. Especially for Xray-FELs, a laser has important roles as a seed light source or photo-cathode-illuminating light source to generate a high brightness electron bunch. The controls of laser pulse characteristics are required for many kinds of experiments. However, the laser should be tuned and customized for each requirement by laser experts. The automatic tuning of laser is required to realize with some sophisticated algorithms. The metaheuristic algorithm is one of the useful candidates to find one of the best solutions as acceptable as possible. The metaheuristic laser tuning system is expected to save our human resources and time for the laser preparations. I have shown successful results on a metaheuristic algorithm based on a genetic algorithm to optimize spatial (transverse) laser profiles and a hill climbing method extended with a fuzzy set theory to choose one of the best laser alignments automatically for each experimental requirement. (author)

Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes

Directory of Open Access Journals (Sweden)

Jaya Shankar Tumuluru

2016-11-01

Full Text Available Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA, which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods.