A HYBRID HEURISTIC ALGORITHM FOR THE CLUSTERED TRAVELING SALESMAN PROBLEM
Directory of Open Access Journals (Sweden)
Mário Mestria
2016-04-01
Full Text Available ABSTRACT This paper proposes a hybrid heuristic algorithm, based on the metaheuristics Greedy Randomized Adaptive Search Procedure, Iterated Local Search and Variable Neighborhood Descent, to solve the Clustered Traveling Salesman Problem (CTSP. Hybrid Heuristic algorithm uses several variable neighborhood structures combining the intensification (using local search operators and diversification (constructive heuristic and perturbation routine. In the CTSP, the vertices are partitioned into clusters and all vertices of each cluster have to be visited contiguously. The CTSP is -hard since it includes the well-known Traveling Salesman Problem (TSP as a special case. Our hybrid heuristic is compared with three heuristics from the literature and an exact method. Computational experiments are reported for different classes of instances. Experimental results show that the proposed hybrid heuristic obtains competitive results within reasonable computational time.
Ding, Zhe; Xu, Zhanqi; Zeng, Xiaodong; Ma, Tao; Yang, Fan
2014-04-01
By adopting the orthogonal frequency division multiplexing technology, spectrum-sliced elastic optical path networks can offer flexible bandwidth to each connection request and utilize the spectrum resources efficiently. The routing and spectrum assignment (RSA) problems in SLICE networks are solved by using heuristic algorithms in most prior studies and addressed by intelligent algorithms in few investigations. The performance of RSA algorithms can be further improved if we could combine such two types of algorithms. Therefore, we propose three hybrid RSA algorithms: DACE-GMSF, DACE-GLPF, and DACE-GEMkPSF, which are the combination of the heuristic algorithm and coevolution based on distance-adaptive policy. In the proposed algorithms, we first groom the connection requests, then sort the connection requests by using the heuristic algorithm (most subcarriers first, longest path first, and extended most k paths' slots first), and finally search the approximately optimal solution with the coevolutionary policy. We present a model of the RSA problem by using integral linear programming, and key elements in the proposed algorithms are addressed in detail. Simulations under three topologies show that the proposed hybrid RSA algorithms can save spectrum resources efficiently.
BF-PSO-TS: Hybrid Heuristic Algorithms for Optimizing Task Schedulingon Cloud Computing Environment
Directory of Open Access Journals (Sweden)
Hussin M. Alkhashai
2016-06-01
Full Text Available Task Scheduling is a major problem in Cloud computing because the cloud provider has to serve many users. Also, a good scheduling algorithm helps in the proper and efficient utilization of the resources. So, task scheduling is considered as one of the major issues on the Cloud computing systems. The objective of this paper is to assign the tasks to multiple computing resources. Consequently, the total cost of execution is to be minimum and load to be shared between these computing resources. Therefore, two hybrid algorithms based on Particle Swarm Optimization (PSO have been introduced to schedule the tasks; Best-Fit-PSO (BFPSO and PSO-Tabu Search (PSOTS. According to BFPSO algorithm, Best-Fit (BF algorithm has been merged into the PSO algorithm to improve the performance. The main principle of the modified BFSOP algorithm is that BF algorithm is used to generate the initial population of the standard PSO algorithm instead of being initiated randomly. According to the proposed PSOTS algorithm, the Tabu-Search (TS has been used to improve the local research by avoiding the trap of the local optimality which could be occurred using the standard PSO algorithm. The two proposed algorithms (i.e., BFPSO and PSOTS have been implemented using Cloudsim and evaluated comparing to the standard PSO algorithm using five problems with different number of independent tasks and resources. The performance parameters have been considered are the execution time (Makspan, cost, and resources utilization. The implementation results prove that the proposed hybrid algorithms (i.e., BFPSO, PSOTS outperform the standard PSO algorithm.
Multi-objective optimization design of bridge piers with hybrid heuristic algorithms
Institute of Scientific and Technical Information of China (English)
Francisco J. MARTINEZ-MARTIN; Femando GONZALEZ-VIDOSA; Antonio HOSPITALER; Victor YEPES
2012-01-01
This paper describes one approach to the design of reinforced concrete (RC) bridge piers,using a three-hybrid multiobjective simulated annealing (SA) algorithm with a neighborhood move based on the mutation operator from the genetic algorithms (GAs),namely MOSAMO1,MOSAMO2 and MOSAMO3.The procedure is applied to three objective functions:the economic cost,the reinforcing steel congestion and the embedded CO2 emissions.Additional results for a random walk and a descent local search multi-objective algorithm are presented.The evaluation of solutions follows the Spanish Code for structural concrete.The methodology was applied to a typical bridge pier of 23,97 m in height.This example involved 110 design variables.Results indicate that algorithm MOSAMO2 outperforms other algorithms regarding the definition of Pareto fronts.Further,the proposed procedure will help structural engineers to enhance their bridge pier designs.
Solving the vehicle routing problem by a hybrid meta-heuristic algorithm
Yousefikhoshbakht, Majid; Khorram, Esmaile
2012-01-01
The vehicle routing problem (VRP) is one of the most important combinational optimization problems that has nowadays received much attention because of its real application in industrial and service problems. The VRP involves routing a fleet of vehicles, each of them visiting a set of nodes such that every node is visited by exactly one vehicle only once. So, the objective is to minimize the total distance traveled by all the vehicles. This paper presents a hybrid two-phase algorithm called s...
Solving the vehicle routing problem by a hybrid meta-heuristic algorithm
Yousefikhoshbakht, Majid; Khorram, Esmaile
2012-08-01
The vehicle routing problem (VRP) is one of the most important combinational optimization problems that has nowadays received much attention because of its real application in industrial and service problems. The VRP involves routing a fleet of vehicles, each of them visiting a set of nodes such that every node is visited by exactly one vehicle only once. So, the objective is to minimize the total distance traveled by all the vehicles. This paper presents a hybrid two-phase algorithm called sweep algorithm (SW) + ant colony system (ACS) for the classical VRP. At the first stage, the VRP is solved by the SW, and at the second stage, the ACS and 3-opt local search are used for improving the solutions. Extensive computational tests on standard instances from the literature confirm the effectiveness of the presented approach.
Directory of Open Access Journals (Sweden)
Mahdi Maktabdar Oghaz
Full Text Available Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.
Maktabdar Oghaz, Mahdi; Maarof, Mohd Aizaini; Zainal, Anazida; Rohani, Mohd Foad; Yaghoubyan, S Hadi
2015-01-01
Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications.
BCI Control of Heuristic Search Algorithms
Cavazza, Marc; Aranyi, Gabor; Charles, Fred
2017-01-01
The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A* algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA*. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid
A Hybrid Heuristics for Irregular Flight Recovery
Institute of Scientific and Technical Information of China (English)
ZHAO Xiu-li; ZHU Jin-fu; GAO Qiang
2010-01-01
Adverse weather conditions, congestion at airports, and mechanical failures often disrupt regular flight schedules. The irregular flight recovery problem aims to recover these schedules through reassignments of flights and cancellations. In this article, we develop the classic resource assignment model for the irregular flight recovery problem, and a new hybrid heuristic procedure based on greedy random adaptive search procedure (GRASP) and simulated annealing algorithm is presented to solve this problem. As compared with the original GRASP method, the proposed algorithm demonstrates quite a high global optimization capability. Computational experiments on large-scale problems show that the proposed procedure is able to generate feasible revised flight schedules of good quality in less than five seconds.
Hybrid Heuristic-Based Artificial Immune System for Task Scheduling
sanei, Masoomeh
2011-01-01
Task scheduling problem in heterogeneous systems is the process of allocating tasks of an application to heterogeneous processors interconnected by high-speed networks, so that minimizing the finishing time of application as much as possible. Tasks are processing units of application and have precedenceconstrained, communication and also, are presented by Directed Acyclic Graphs (DAGs). Evolutionary algorithms are well suited for solving task scheduling problem in heterogeneous environment. In this paper, we propose a hybrid heuristic-based Artificial Immune System (AIS) algorithm for solving the scheduling problem. In this regard, AIS with some heuristics and Single Neighbourhood Search (SNS) technique are hybridized. Clonning and immune-remove operators of AIS provide diversity, while heuristics and SNS provide convergence of algorithm into good solutions, that is balancing between exploration and exploitation. We have compared our method with some state-of-the art algorithms. The results of the experiments...
An Improved Heuristic Ant-Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
Yunfei Chen; Yushu Liu; Jihai Zhao
2004-01-01
An improved heuristic ant-clustering algorithm(HAC)is presented in this paper. A device of ＇memory bank＇ is proposed,which can bring forth heuristic knowledge guiding ant to move in the bi-dimension grid space.The device experiments on real data sets and synthetic data sets.The results demonstrate that HAC has superiority in misclassification error rate and runtime over the classical algorithm.
Tavakkoli-Moghaddam, Reza; Alinaghian, Mehdi; Salamat-Bakhsh, Alireza; Norouzi, Narges
2012-05-01
A vehicle routing problem is a significant problem that has attracted great attention from researchers in recent years. The main objectives of the vehicle routing problem are to minimize the traveled distance, total traveling time, number of vehicles and cost function of transportation. Reducing these variables leads to decreasing the total cost and increasing the driver's satisfaction level. On the other hand, this satisfaction, which will decrease by increasing the service time, is considered as an important logistic problem for a company. The stochastic time dominated by a probability variable leads to variation of the service time, while it is ignored in classical routing problems. This paper investigates the problem of the increasing service time by using the stochastic time for each tour such that the total traveling time of the vehicles is limited to a specific limit based on a defined probability. Since exact solutions of the vehicle routing problem that belong to the category of NP-hard problems are not practical in a large scale, a hybrid algorithm based on simulated annealing with genetic operators was proposed to obtain an efficient solution with reasonable computational cost and time. Finally, for some small cases, the related results of the proposed algorithm were compared with results obtained by the Lingo 8 software. The obtained results indicate the efficiency of the proposed hybrid simulated annealing algorithm.
A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems
Abtahi, Amir-Reza; Bijari, Afsane
2016-09-01
In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.
A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems
Abtahi, Amir-Reza; Bijari, Afsane
2017-09-01
In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.
Quantum heuristic algorithm for traveling salesman problem
Bang, Jeongho; Lim, James; Ryu, Junghee; Lee, Changhyoup; Lee, Jinhyoung
2010-01-01
We propose a quantum heuristic algorithm to solve a traveling salesman problem by generalizing Grover search. Sufficient conditions are derived to greatly enhance the probability of finding the tours with extremal costs, reaching almost to unity and they are shown characterized by statistical properties of tour costs. In particular for a Gaussian distribution of the tours along the cost we show that the quantum algorithm exhibits the quadratic speedup of its classical counterpart, similarly to Grover search.
Directory of Open Access Journals (Sweden)
Fanrong Kong
2017-09-01
Full Text Available To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.
Meta Heuristic Algorithms for Vehicle Routing Problem with Stochastic Demands
Directory of Open Access Journals (Sweden)
Geetha Shanmugam
2011-01-01
Full Text Available Problem statement: The shipment of goods from manufacturer to the consumer is a focal point of distribution logistics. In reality, the demand of consumers is not known a priori. This kind of distribution is dealt by Stochastic Vehicle Routing Problem (SVRP which is a NP-hard problem. In this proposed work, VRP with stochastic demand is considered. A probability distribution is considered as a random variable for stochastic demand of a customer. Approach: In this study, VRPSD is resolved using Meta heuristic algorithms such as Genetic Algorithm (GA, Particle Swarm Optimization (PSO and Hybrid PSO (HPSO. Dynamic Programming (DP is used to find the expected cost of each route generated by GA, PSO and HPSO. Results: The objective is to minimize the total expected cost of a priori route. The fitness value of a priori route is calculated using DP. In proposed HPSO, the initial particles are generated based Nearest Neighbor Heuristic (NNH. Elitism is used in HPSO for updating the particles. The algorithm is implemented using MATLAB7.0 and tested with problems having different number of customers. The results obtained are competitive in terms of execution time and memory usage. Conclusion: The computational time is reduced as polynomial time as O(nKQ time and the memory required is O(nQ. The ANOVA test is performed to compare the proposed HPSO with other heuristic algorithms.
Heuristic Algorithm in Optimal Discrete Structural Designs
Directory of Open Access Journals (Sweden)
Alongkorn Lamom
2008-01-01
Full Text Available This study proposes a Heuristic Algorithm for Material Size Selection (HAMSS. It is developed to handle discrete structural optimization problems. The proposed algorithm (HAMSS, Simulated Annealing Algorithm (SA and the conventional design algorithm obtained from a structural steel design software are studied with three selected examples. The HAMSS, in fact, is the adaptation from the traditional SA. Although the SA is one of the easiest optimization algorithms available, a huge number of function evaluations deter its use in structural optimizations. To obtain the optimum answers by the SA, possible answers are first generated randomly. Many of these possible answers are rejected because they do not pass the constraints. To effectively handle this problem, the behavior of optimal structural design problems is incorporated into the algorithm. The new proposed algorithm is called the HAMSS. The efficiency comparison between the SA and the HAMSS is illustrated in term of number of finite element analysis cycles. Results from the study show that HAMSS can significantly reduce the number of structural analysis cycles while the optimized efficiency is not different.
A Heuristic Genetic Algorithm for No-Wait Flowshop Scheduling Problem
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
No-wait flowshop scheduling problems with the objective to minimize the total flow time is an important sequencing problem in the field of developing production plans and has a wide engineering background.Genetic algorithm (GA) has the capability of global convergence and has been proven effective to solve NP-hard combinatorial optimization problems, while simple heuristics have the advantage of fast local convergence and can be easily implemented.In order to avoid the defect of slow convergence or premature, a heuristic genetic algorithm is proposed by incorporating the simple heuristics and local search into the traditional genetic algorithm.In this hybridized algorithm, the structural information of no-wait flowshops and high-effective heuristics are incorporated to design a new method for generating initial generation and a new crossover operator.The computational results show the developed heuristic genetic algorithm is efficient and the quality of its solution has advantage over the best known algorithm.It is suitable for solving the large scale practical problems and lays a foundation for the application of meta-heuristic algorithms in industrial production.
A quantum heuristic algorithm for the traveling salesman problem
Bang, Jeongho; Ryu, Junghee; Lee, Changhyoup; Yoo, Seokwon; Lim, James; Lee, Jinhyoung
2012-12-01
We propose a quantum heuristic algorithm to solve the traveling salesman problem by generalizing the Grover search. Sufficient conditions are derived to greatly enhance the probability of finding the tours with the cheapest costs reaching almost to unity. These conditions are characterized by the statistical properties of tour costs and are shown to be automatically satisfied in the large-number limit of cities. In particular for a continuous distribution of the tours along the cost, we show that the quantum heuristic algorithm exhibits a quadratic speedup compared to its classical heuristic algorithm.
A Heuristic Algorithm for Resource Allocation/Reallocation Problem
Directory of Open Access Journals (Sweden)
S. Raja Balachandar
2011-01-01
Full Text Available This paper presents a 1-opt heuristic approach to solve resource allocation/reallocation problem which is known as 0/1 multichoice multidimensional knapsack problem (MMKP. The intercept matrix of the constraints is employed to find optimal or near-optimal solution of the MMKP. This heuristic approach is tested for 33 benchmark problems taken from OR library of sizes upto 7000, and the results have been compared with optimum solutions. Computational complexity is proved to be (2 of solving heuristically MMKP using this approach. The performance of our heuristic is compared with the best state-of-art heuristic algorithms with respect to the quality of the solutions found. The encouraging results especially for relatively large-size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems.
Hybrid Experiential-Heuristic Cognitive Radio Engine Architecture and Implementation
Directory of Open Access Journals (Sweden)
Ashwin Amanna
2012-01-01
Full Text Available The concept of cognitive radio (CR focuses on devices that can sense their environment, adapt configuration parameters, and learn from past behaviors. Architectures tend towards simplified decision-making algorithms inspired by human cognition. Initial works defined cognitive engines (CEs founded on heuristics, such as genetic algorithms (GAs, and case-based reasoning (CBR experiential learning algorithms. This hybrid architecture enables both long-term learning, faster decisions based on past experience, and capability to still adapt to new environments. This paper details an autonomous implementation of a hybrid CBR-GA CE architecture on a universal serial radio peripheral (USRP software-defined radio focused on link adaptation. Details include overall process flow, case base structure/retrieval method, estimation approach within the GA, and hardware-software lessons learned. Unique solutions to realizing the concept include mechanisms for combining vector distance and past fitness into an aggregate quantification of similarity. Over-the-air performance under several interference conditions is measured using signal-to-noise ratio, packet error rate, spectral efficiency, and throughput as observable metrics. Results indicate that the CE is successfully able to autonomously change transmit power, modulation/coding, and packet size to maintain the link while a non-cognitive approach loses connectivity. Solutions to existing shortcomings are proposed for improving case-base searching and performance estimation methods.
А heuristic algorithm for two-dimensional strip packing problem
Dayong, Cao; Kotov, V.M.
2011-01-01
In this paper, we construct an improved best-fit heuristic algorithm for two-dimensional rectangular strip packing problem (2D-RSPP), and compare it with some heuristic and metaheuristic algorithms from literatures. The experimental results show that BFBCC could produce satisfied packing layouts than these methods, especially for the large problem of 50 items or more, BFBCC could get better results in shorter time.
Petri nets SM-cover-based on heuristic coloring algorithm
Tkacz, Jacek; Doligalski, Michał
2015-09-01
In the paper, coloring heuristic algorithm of interpreted Petri nets is presented. Coloring is used to determine the State Machines (SM) subnets. The present algorithm reduces the Petri net in order to reduce the computational complexity and finds one of its possible State Machines cover. The proposed algorithm uses elements of interpretation of Petri nets. The obtained result may not be the best, but it is sufficient for use in rapid prototyping of logic controllers. Found SM-cover will be also used in the development of algorithms for decomposition, and modular synthesis and implementation of parallel logic controllers. Correctness developed heuristic algorithm was verified using Gentzen formal reasoning system.
A Direct Heuristic Algorithm for Linear Programming
Indian Academy of Sciences (India)
S K Sen; A Ramful
2000-02-01
An (3) mathematically non-iterative heuristic procedure that needs no artificial variable is presented for solving linear programming problems. An optimality test is included. Numerical experiments depict the utility/scope of such a procedure.
An Improved Heuristic Algorithm of Attribute Reduction in Rough Set
Institute of Scientific and Technical Information of China (English)
ShunxiangWu; MaoqingLi; WentingHuang; SifengLiu
2004-01-01
This paper introduces background of rough set theory, then proposes a new algorithm for finding optimal reduction and make comparison between the original algorithm and the improved one by the experiment about the nine standard data set in UL database to explain the validity of the improved heuristic algorithm.
A global heuristically search algorithm for DNA encoding
Institute of Scientific and Technical Information of China (English)
Zhang Kai; Pan Linqiang; Xu Jin
2007-01-01
A new efficient algorithm is developed to design DNA words with equal length for DNA computing. The algorithm uses a global heuristic optimizing search approach and converts constraints to a carry number to accelerate the convergence, which can generate a DNA words set satisfying some thermodynamic and combinatorial constraints. Based on the algorithm, a software for DNA words design is developed.
Heuristic Reduction Algorithm Based on Pairwise Positive Region
Institute of Scientific and Technical Information of China (English)
QI Li; LIU Yu-shu
2007-01-01
To guarantee the optimal reduct set, a heuristic reduction algorithm is proposed, which considers the distinguishing information between the members of each pair decision classes. Firstly the pairwise positive region is defined, based on which the pairwise significance measure is calculated between the members of each pair classes. Finally the weighted pairwise significance of attribute is used as the attribute reduction criterion, which indicates the necessity of attributes very well. By introducing the noise tolerance factor, the new algorithm can tolerate noise to some extent. Experimental results show the advantages of our novel heuristic reduction algorithm over the traditional attribute dependency based algorithm.
Heuristic and algorithmic processing in English, mathematics, and science education.
Sharps, Matthew J; Hess, Adam B; Price-Sharps, Jana L; Teh, Jane
2008-01-01
Many college students experience difficulties in basic academic skills. Recent research suggests that much of this difficulty may lie in heuristic competency--the ability to use and successfully manage general cognitive strategies. In the present study, the authors evaluated this possibility. They compared participants' performance on a practice California Basic Educational Skills Test and on a series of questions in the natural sciences with heuristic and algorithmic performance on a series of mathematics and reading comprehension exercises. Heuristic competency in mathematics was associated with better scores in science and mathematics. Verbal and algorithmic skills were associated with better reading comprehension. These results indicate the importance of including heuristic training in educational contexts and highlight the importance of a relatively domain-specific approach to questions of cognition in higher education.
Energy Technology Data Exchange (ETDEWEB)
Sadjadi, Seyed Jafar [Department of Industrial Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)], E-mail: sjsadjadi@iust.ac.ir; Soltani, R. [Department of Industrial Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)
2009-11-15
We present a heuristic approach to solve a general framework of serial-parallel redundancy problem where the reliability of the system is maximized subject to some general linear constraints. The complexity of the redundancy problem is generally considered to be NP-Hard and the optimal solution is not normally available. Therefore, to evaluate the performance of the proposed method, a hybrid genetic algorithm is also implemented whose parameters are calibrated via Taguchi's robust design method. Then, various test problems are solved and the computational results indicate that the proposed heuristic approach could provide us some promising reliabilities, which are fairly close to optimal solutions in a reasonable amount of time.
Institute of Scientific and Technical Information of China (English)
柴获; 何瑞春; 马昌喜; 代存杰
2016-01-01
This paper presents a univariate marginal distribution algorithm hybridized with insertion heuristics for the vehicle routing problem with hard time windows (VRPHTW). In the VRPHTW,a fleet of vehicles must deliver goods to a set of customers,time window constraints of the customers must be respected and the fact that the travel time between two points depends on the time of departure has to be taken into account. The latter assumption is particularly important in an urban context where the traffic plays a significant role. A shortcoming of univariate marginal distribution algorithm for vehicle routing problems is that,customers are not independent events in probabilistic model. Hence,we propose a novel probabilistic model that probability of the distribution of customers delivered by the same vehicle. Moreover,the new population is generated by two phase insertion heuristics method. Computational results with 56 Solomon benchmark problems confirm the benefits of other algorithms,the resulting algorithm turns out to be competitive,matching or improving the best known results.%针对带硬时间窗的车辆路径问题(VRPHTW)求解，提出了一种混合单变量边缘分布算法(hybrid UDMA，hUDMA)，改进了基本UMDA的概率模型.统计节点按路径分布的概率，使其能够在解空间上找到节点—路径的分布关系，提高了UMDA的全局搜索能力.采用两阶段插入法进行最佳节点搜索和路径分配完成UMDA采样操作，通过种群进化来获取最优解.计算Solomon 100客户的6类问题56个算例的实验结果表明：在最优解的取得方面，C类算例能够全部取得最优解，R、RC类算例能以50%左右概率取得最优解；在平均误差方面，C类算例计算结果与已知最优解一致，R、RC类算例计算误差率与已知最优解比较接近，平均误差率为1.03%.
A new heuristic algorithm for general integer linear programming problems
Institute of Scientific and Technical Information of China (English)
GAO Pei-wang; CAI Ying
2006-01-01
A new heuristic algorithm is proposed for solving general integer linear programming problems.In the algorithm,the objective function hyperplane is used as a cutting plane,and then by introducing a special set of assistant sets,an efficient heuristic search for the solution to the integer linear program is carried out in the sets on the objective function hyperplane.A simple numerical example shows that the algorithm is efficient for some problems,and therefore,of practical interest.
Meta-heuristic algorithms as tools for hydrological science
Yoo, Do Guen; Kim, Joong Hoon
2014-12-01
In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives. Simulation-based meta-heuristic methods such as Genetic algorithms (GAs) and Harmony Search (HS) have powerful searching abilities, which can occasionally overcome the several drawbacks of traditional mathematical methods. For example, HS algorithms can be conceptualized from a musical performance process and used to achieve better harmony; such optimization algorithms seek a near global optimum determined by the value of an objective function, providing a more robust determination of musical performance than can be achieved through typical aesthetic estimation. In this paper, meta-heuristic algorithms and their applications (focus on GAs and HS) in hydrological science are discussed by subject, including a review of existing literature in the field. Then, recent trends in optimization are presented and a relatively new technique such as Smallest Small World Cellular Harmony Search (SSWCHS) is briefly introduced, with a summary of promising results obtained in previous studies. As a result, previous studies have demonstrated that meta-heuristic algorithms are effective tools for the development of hydrological models and the management of water resources.
Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites
Directory of Open Access Journals (Sweden)
Maocai Wang
2014-01-01
Full Text Available Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.
Hybridization of Meta-heuristics for Optimizing Routing protocol in VANETs
Directory of Open Access Journals (Sweden)
R.R Sedamkar
2016-02-01
Full Text Available The goal of VANET is to establish a vehicular communication system which is reliable and fast which caters to road safety and road safety. In VANET where network fragmentation is frequent with no central control, routing becomes a challenging task. Planning an optimal routing plan for tuning parameter configuration of routing protocol for setting up VANET is very crucial. This is done by defining an optimization problem where hybridization of meta-heuristics is defined. The paper contributes the idea of combining meta-heuristic algorithm to enhance the performance of individual search method for optimization problem
A Hybrid Algorithm for Strip Packing Problem with Rotation Constraint
Directory of Open Access Journals (Sweden)
Chen Huan
2016-01-01
Full Text Available Strip packing is a well-known NP-hard problem and it was widely applied in engineering fields. This paper considers a two-dimensional orthogonal strip packing problem. Until now some exact algorithm and mainly heuristics were proposed for two-dimensional orthogonal strip packing problem. While this paper proposes a two-stage hybrid algorithm for it. In the first stage, a heuristic algorithm based on layering idea is developed to construct a solution. In the second stage, a great deluge algorithm is used to further search a better solution. Computational results on several classes of benchmark problems have revealed that the hybrid algorithm improves the results of layer-heuristic, and can compete with other heuristics from the literature.
A Hybrid Algorithm for Satellite Data Transmission Schedule Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
LI Yun-feng; WU Xiao-yue
2008-01-01
A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission. At first, based on description of satellite data transmission request, satellite data transmission task modal and satellite data transmission scheduling problem model are established. Secondly, the conflicts in scheduling are discussed. According to the meaning of possible conflict, the method to divide possible conflict task set is given. Thirdly, a hybrid algorithm which consists of genetic algorithm and heuristic information is presented. The heuristic information comes from two concepts, conflict degree and conflict number. Finally, an example shows the algorithm's feasibility and performance better than other traditional algorithms.
A novel heuristic algorithm for capacitated vehicle routing problem
Kır, Sena; Yazgan, Harun Reşit; Tüncel, Emre
2017-02-01
The vehicle routing problem with the capacity constraints was considered in this paper. It is quite difficult to achieve an optimal solution with traditional optimization methods by reason of the high computational complexity for large-scale problems. Consequently, new heuristic or metaheuristic approaches have been developed to solve this problem. In this paper, we constructed a new heuristic algorithm based on the tabu search and adaptive large neighborhood search (ALNS) with several specifically designed operators and features to solve the capacitated vehicle routing problem (CVRP). The effectiveness of the proposed algorithm was illustrated on the benchmark problems. The algorithm provides a better performance on large-scaled instances and gained advantage in terms of CPU time. In addition, we solved a real-life CVRP using the proposed algorithm and found the encouraging results by comparison with the current situation that the company is in.
New Heuristic Distributed Parallel Algorithms for Searching and Planning
Institute of Scientific and Technical Information of China (English)
无
1995-01-01
This paper proposes new heuristic distributed parallel algorithms for searching and planning,which are based on the concepts of wave concurrent propagations and competitive activation mechanisms.These algorithms are characterized by simplicity and clearness of control strategies for earching,and distinguished abilities in many aspects,such as high speed processing,wide suitability for searching AND/OR implicit graphs,and ease in hardware implementation.
Deterministic oscillatory search: a new meta-heuristic optimization algorithm
Indian Academy of Sciences (India)
N ARCHANA; R VIDHYAPRIYA; ANTONY BENEDICT; KARTHIK CHANDRAN
2017-06-01
The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. Theresults show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices.
A Heuristic Algorithm for QoS Multicast Routing
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In recent years, QoS multicast routing has continued to be a very important research topic in the areas of net-works. This paper presents a heuristic algorithm for the QoS multicast routing (HAQMR). This heuristic algorithmdeals with delay and bandwidth constraints and has low cost. The HAQMR attempts to significantly reduce the overheadfor constructing a multicast tree. the proof for correctness of the HAQMR is given, and the performance of the HAQMRis evaluated by simulations. The study shows that HAQMR provides an available approach to QoS multicast routing.
Heuristic algorithm for off-lattice protein folding problem
Institute of Scientific and Technical Information of China (English)
CHEN Mao; HUANG Wen-qi
2006-01-01
Enlightened by the law of interactions among objects in the physical world, we propose a heuristic algorithm for solving the three-dimensional (3D) off-lattice protein folding problem. Based on a physical model, the problem is converted from a nonlinear constraint-satisfied problem to an unconstrained optimization problem which can be solved by the well-known gradient method. To improve the efficiency of our algorithm, a strategy was introduced to generate initial configuration. Computational results showed that this algorithm could find states with lower energy than previously proposed ground states obtained by nPERM algorithm for all chains with length ranging from 13 to 55.
Adapting Heuristic Mastermind Strategies to Evolutionary Algorithms
Runarsson, Tomas Philip
2009-01-01
The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than 30 years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we try to suggest a strategy that will allow nature-inspired algorithms to obtain results as good as those based on exhaustive search strategies; in order to do that, we first review, compare and improve current approaches to solving the puzzle; then we test one of these strategies with an estimation of distribution algorithm. Finally, we try to find a strategy that falls short of being exhaustive, and is then amenable for inclusion in nature inspired algorithms (such as evolutionary or particle swarm algorithms). This paper proves that by the incorporation of local entropy into the fitness function of the evolutionary algorithm it becomes a better player than a random one, and gives a rule of thumb on how to incorporate t...
Assessment of a Heuristic Algorithm for Scheduling Theater Security Cooperation Naval Missions
2009-03-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS ASSESSMENT OF A HEURISTIC ALGORITHM FOR SCHEDULING THEATER SECURITY...blank) 2. REPORT DATE March 2009 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE Assessment of a Heuristic Algorithm for...INTENTIONALLY LEFT BLANK iii Approved for public release; distribution is unlimited ASSESSMENT OF A HEURISTIC ALGORITHM FOR SCHEDULING THEATER
Heuristic-based scheduling algorithm for high level synthesis
Mohamed, Gulam; Tan, Han-Ngee; Chng, Chew-Lye
1992-01-01
A new scheduling algorithm is proposed which uses a combination of a resource utilization chart, a heuristic algorithm to estimate the minimum number of hardware units based on operator mobilities, and a list-scheduling technique to achieve fast and near optimal schedules. The schedule time of this algorithm is almost independent of the length of mobilities of operators as can be seen from the benchmark example (fifth order digital elliptical wave filter) presented when the cycle time was increased from 17 to 18 and then to 21 cycles. It is implemented in C on a SUN3/60 workstation.
Heuristic Based Adaptive Step Size CLMS Algorithms for Smart Antennas
Directory of Open Access Journals (Sweden)
Y Rama Krishna
2013-05-01
Full Text Available A smart antenna system combines multiple antenna elements with a signal processing capability to optimize its radiation and/or reception pattern automatically in response to the signal environment through complex weight selection. The weight selection process to get suitable Array factor with low Half Power Beam Width (HPBW and Side Lobe Level (SLL is a complex method. The aim of this task is to design a new approach for smart antennas to minimize the noise and interference effects from external sources with least number of iterations. This paper presents Heuristics based adaptive step size Complex Least Mean Square (CLMS model for Smart Antennas to speedup convergence. In this process Benveniste and Mathews algorithms are used as heuristics with CLMS and the improvement of performance of Smart Antenna System in terms of convergence rate and array factor are discussed and compared with the performance of CLMS and Augmented CLMS (ACLMS algorithms.
A Heuristic Algorithm for Core Selection in Multicast Routing
Institute of Scientific and Technical Information of China (English)
Manas Ranjan Kabat; Manoj Kumar Patel; Chita Ranjan Tripathy
2011-01-01
With the development of network multimedia technology,more and more real-time multimedia applications need to transmit information using multicast.The basis of multicast data transmission is to construct a multicast tree.The main problem concerning the construction of a shared multicast tree is selection of a root of the shared tree or the core point.In this paper,we propose a heuristic algorithm for core selection in multicast routing.The proposed algorithm selects core point by considering both delay and inter-destination delay variation.The simulation results show that the proposed algorithm performs better than the existing algorithms in terms of delay variation subject to the end-to-end delay bound.The mathematical time complexity and the execution time of the proposed algorithm are comparable to those of the existing algorithms.
Directory of Open Access Journals (Sweden)
Orhan TÜRKBEY
2002-02-01
Full Text Available Memetic algorithms, which use local search techniques, are hybrid structured algorithms like genetic algorithms among evolutionary algorithms. In this study, for Quadratic Assignment Problem (QAP, a memetic structured algorithm using a local search heuristic like 2-opt is developed. Developed in the algorithm, a crossover operator that has not been used before for QAP is applied whereas, Eshelman procedure is used in order to increase thesolution variability. The developed memetic algorithm is applied on test problems taken from QAP-LIB, the results are compared with the present techniques in the literature.
Hybrid Heuristic Approaches for Tactical Berth Allocation Problem
DEFF Research Database (Denmark)
Iris, Cagatay; Larsen, Allan; Pacino, Dario;
Tactical berth allocation problem deals with: the berth allocation (as- signs and schedules vessels to berth-positions), and the quay crane (QC) assignment (finds number of QCs that will serve). In this work, we strengthen the current mathematical models (MM) with novel lower bounds and valid ine...... inequalities. And, we propose a hybrid heuristic which combines MM with greedy and search heuristics. Results show that problem can be solved efficiently respect to optimality and computational time.......Tactical berth allocation problem deals with: the berth allocation (as- signs and schedules vessels to berth-positions), and the quay crane (QC) assignment (finds number of QCs that will serve). In this work, we strengthen the current mathematical models (MM) with novel lower bounds and valid...
Hybrid heuristic algorithm for heterogeneous open vehicle routing problem%多车型开放式车辆路线问题的混合启发式算法
Institute of Scientific and Technical Information of China (English)
王晓博; 任春玉; 李海晨
2013-01-01
多车型开放式车辆路线问题，是物流配送优化中不可缺少的环节。针对标准遗传算法存在收敛速度慢，局部搜索能力差，易早熟的缺点，采用混合启发式算法进行优化求解。采用实数序列编码，使问题变得更简洁；有针对性地构建初始解，提高了解的可行性；用基于排序的选择与最佳保留相结合策略，保证群体的多样性；引入部分算术交叉算子，加强染色体的全局搜索能力；利用模拟退火算法的 Boltzmann 机制，控制遗传算法的交叉、变异操作，提高了算法的收敛速度和搜索效率。仿真结果表明混合启发式算法在求解质量和计算效率上好于标准遗传算法。%Heterogeneous open vehicle routing problem is logistics optimization indispensable part. According to the standard genetic algorithm shortcomings of slowly convergent speed, weakly partial searching ability and easily premature, hybrid heuristic algorithm is used to optimize the solution. The paper uses sequence of real numbers coding so as to simplify the problem, con-structs the targeted initial solution to improve the feasibility, adopts a choice based on sort of a combination with the best reten-tion strategies to ensure the diversity of population, and uses some arithmetic crossover operator to enhance whole search ability of the chromosome. Using Boltzmann simulated annealing mechanism for controlling genetic algorithm crossover and mutation operations, it improves the convergence speed and search efficiency. Finally, the good performance can be proved by experiment calculation and concrete examples.
A Heuristic Algorithm for optimizing Page Selection Instructions
Li, Qing'an; Chen, Yong; Wu, Wei; Xu, Wenwen
2010-01-01
Page switching is a technique that increases the memory in microcontrollers without extending the address buses. This technique is widely used in the design of 8-bit MCUs. In this paper, we present an algorithm to reduce the overhead of page switching. To pursue small code size, we place the emphasis on the allocation of functions into suitable pages with a heuristic algorithm, thereby the cost-effective placement of page selection instructions. Our experimental results showed the optimization achieved a reduction in code size of 13.2 percent.
A Heuristic Algorithm on QoS Routing
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
This paper focuses on solving the delay-constrained least-cost ro uting problem, and propose a simple,distributed heuristic solution, called dist ributed recursive delay constrained leastcost (DR-DCLC) unicast routing algo rithm. DR-DCLC only requires local information to find the near optimal solut io n.The correctness of DR-DCLC is proued by showing that it is always capable of constr ucting a loop-free-delay-constrained path wthin finite time, if such a path e xi sts. Simulation is also used to compare DR-DCLC to the optimal DCLC algorithm a nd other algorithms.
Identifying multiple influential spreaders by a heuristic clustering algorithm
Energy Technology Data Exchange (ETDEWEB)
Bao, Zhong-Kui [School of Mathematical Science, Anhui University, Hefei 230601 (China); Liu, Jian-Guo [Data Science and Cloud Service Research Center, Shanghai University of Finance and Economics, Shanghai, 200133 (China); Zhang, Hai-Feng, E-mail: haifengzhang1978@gmail.com [School of Mathematical Science, Anhui University, Hefei 230601 (China); Department of Communication Engineering, North University of China, Taiyuan, Shan' xi 030051 (China)
2017-03-18
The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.
General heuristics algorithms for solving capacitated arc routing problem
Fadzli, Mohammad; Najwa, Nurul; Masran, Hafiz
2015-05-01
In this paper, we try to determine the near-optimum solution for the capacitated arc routing problem (CARP). In general, NP-hard CARP is a special graph theory specifically arises from street services such as residential waste collection and road maintenance. By purpose, the design of the CARP model and its solution techniques is to find optimum (or near-optimum) routing cost for a fleet of vehicles involved in operation. In other words, finding minimum-cost routing is compulsory in order to reduce overall operation cost that related with vehicles. In this article, we provide a combination of various heuristics algorithm to solve a real case of CARP in waste collection and benchmark instances. These heuristics work as a central engine in finding initial solutions or near-optimum in search space without violating the pre-setting constraints. The results clearly show that these heuristics algorithms could provide good initial solutions in both real-life and benchmark instances.
A Modularity Degree Based Heuristic Community Detection Algorithm
Directory of Open Access Journals (Sweden)
Dongming Chen
2014-01-01
Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.
New Hybrid Genetic Algorithm for Vertex Cover Problems
Institute of Scientific and Technical Information of China (English)
霍红卫; 许进
2003-01-01
This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are used to perform global exploration in a population, while neighborhood search methods are used to perform local exploitation around the chromosomes. The experimental results indicate that hybrid genetic algorithms can obtain solutions of excellent quality to the problem instances with different sizes. The pure genetic algorithms are outperformed by the neighborhood search heuristics procedures combined with genetic algorithms.
Hybrid Heuristic Algorithm for the Large-scale VRP Optimizaton Problem%混合超启发式法求解大规模VRP的优化研究
Institute of Scientific and Technical Information of China (English)
杜玲玲
2011-01-01
Vehicle routing is a NP-complete problem. It is of theoretical and practical significance to study good quality heuristic algorithm for solving the vehicle routing problem. In order to solve the vehicle routing problem with capacity constraint, the paper presents a new and effective hybrid rnetaheuristic algorithm which combines the strengths of the well-known nearest neighbor search and tabu search. Nearest neighbor search is used to construet initial routes in the first stage, and then tabu search is utilized to optimize the intra-route and the inter-route in the second stage. The computational experiments are carried out on a standard benchmark and a real dataset with 6772 tobacco customers. The results demonstrate that the suggested method is highly competitive in reducing the total distance. It provides a new idea to solve the large scale vehicle routing problem.%车辆路径是一类NP(non-deteministic polynomial)完全问题,研究解决车辆路径问题的高质量启发式算法有着重要理论价值和现实意义.提出一种将最近邻搜索法和禁忌搜索法优势相结合的混合超启发式算法,用来解决带容量约束的车辆路径问题.先利用最近邻搜索法构建初步路线,再利用禁忌搜索法对内部线路和互跨线路进行优化.通过对基于标准数据集和6 772个烟草客户真实数据集进行应用验证,新算法在减少线路的总路程上具有显著效果,为大规模车辆路径问题的求解提供了新的求解思路.
Two-Stage Heuristic Algorithm for Aircraft Recovery Problem
Directory of Open Access Journals (Sweden)
Cheng Zhang
2017-01-01
Full Text Available This study focuses on the aircraft recovery problem (ARP. In real-life operations, disruptions always cause schedule failures and make airlines suffer from great loss. Therefore, the main objective of the aircraft recovery problem is to minimize the total recovery cost and solve the problem within reasonable runtimes. An aircraft recovery model (ARM is proposed herein to formulate the ARP and use feasible line of flights as the basic variables in the model. We define the feasible line of flights (LOFs as a sequence of flights flown by an aircraft within one day. The number of LOFs exponentially grows with the number of flights. Hence, a two-stage heuristic is proposed to reduce the problem scale. The algorithm integrates a heuristic scoring procedure with an aggregated aircraft recovery model (AARM to preselect LOFs. The approach is tested on five real-life test scenarios. The computational results show that the proposed model provides a good formulation of the problem and can be solved within reasonable runtimes with the proposed methodology. The two-stage heuristic significantly reduces the number of LOFs after each stage and finally reduces the number of variables and constraints in the aircraft recovery model.
A Heuristic Algorithm for U.S. Naval Mission Resource Allocation
2008-09-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited A HEURISTIC ALGORITHM FOR...DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE A Heuristic Algorithm for U.S. Naval Mission Allocation 6. AUTHOR(S) Derek T. Dwyer 5...release; distribution is unlimited. A HEURISTIC ALGORITHM FOR U.S. NAVAL MISSION RESOURCE ALLOCATION Derek T. Dwyer Lieutenant Commander
A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters
Directory of Open Access Journals (Sweden)
Weiwei Lin
2016-01-01
Full Text Available Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS. As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.
Afzalirad, Mojtaba; Rezaeian, Javad
2016-04-01
This study involves an unrelated parallel machine scheduling problem in which sequence-dependent set-up times, different release dates, machine eligibility and precedence constraints are considered to minimize total late works. A new mixed-integer programming model is presented and two efficient hybrid meta-heuristics, genetic algorithm and ant colony optimization, combined with the acceptance strategy of the simulated annealing algorithm (Metropolis acceptance rule), are proposed to solve this problem. Manifestly, the precedence constraints greatly increase the complexity of the scheduling problem to generate feasible solutions, especially in a parallel machine environment. In this research, a new corrective algorithm is proposed to obtain the feasibility in all stages of the algorithms. The performance of the proposed algorithms is evaluated in numerical examples. The results indicate that the suggested hybrid ant colony optimization statistically outperformed the proposed hybrid genetic algorithm in solving large-size test problems.
Hybrid Heuristic Online Planning for POMDPs%杂合启发式在线POMDP规划*
Institute of Scientific and Technical Information of China (English)
章宗长; 陈小平
2013-01-01
Lots of planning tasks of autonomous robots under uncertain environments can be modeled as a partially observable Markov decision processes (POMDPs). Although researchers have made impressive progress in designing approximation techniques, developing an efficient planning algorithm for POMDPs is still considered as a challenging problem. Previous research has indicated that online planning approaches are promising approximate methods for handling large-scale POMDP domains efficiently as they make decisions“on demand”, instead of proactively for the entire state space. This paper aims to further speed up the POMDP online planning process by designing a novel hybrid heuristic function, which provides a feasible way to take full advantage of some ignored heuristics in current algorithms. The research implements a new method called hybrid heuristic online planning (HHOP). HHOP substantially outperformes state-of-the-art online heuristic search approaches on a suite of POMDP benchmark problems.% 许多不确定环境下的自主机器人规划任务都可以用部分可观察的马氏决策过程(partially observable Markov decision process,简称POMDP)建模。尽管研究者们在近似求解技术的设计方面已经取得了显著的进展,开发高效的 POMDP 规划算法依然是一个具有挑战性的问题。以前的研究结果表明：在线规划方法能够高效地处理大规模的 POMDP 问题,因而是一类具有研究前景的近似求解方法。这归因于它们采取的是“按需”作决策而不是预前对整个状态空间作决策的方式。旨在通过设计一个新颖的杂合启发式函数来进一步加速 POMDP 在线规划过程,该函数能够充分利用现有算法里一些被忽略掉的启发式信息。实现了一个新的杂合启发式在线规划(hybrid heuristic online planning,简称HHOP)算法。在一组POMDP基准问题上,HHOP有明显优于现有在线启发式搜索算法的实验性能。
Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants
Institute of Scientific and Technical Information of China (English)
DE LA CRUZ J.M.; HERR(A)N-GONZ(A)LEZ A.; RISCO-MART(I)N J.L.; ANDR(E)S-TORO B.
2005-01-01
We solve the problem of petroleum products distribution through oil pipelines networks. This problem is modelled and solved using two techniques: A heuristic method like a multiobjective evolutionary algorithm and Mathematical Programming. In the multiobjective evolutionary algorithm, several objective functions are defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some are evaluated through a repairing function to avoid infeasible solutions. In the Mathematical Programming approach the multiobjective optimization is solved using the Constraint Method in Mixed Integer Linear Programming. Some constraints of the mathematical model are nonlinear, so they are linearized. The results obtained with both methods for one concrete network are presented. They are compared with a hybrid solution, where we use the results obtained by Mathematical Programming as the seed of the evolutionary algorithm.
A Heuristic for Two-Stage No-Wait Hybrid Flowshop Scheduling with a Single Machine in Either Stage
Institute of Scientific and Technical Information of China (English)
刘志新; 谢金星; 李建国; 董杰方
2003-01-01
This paper studies the hybrid flow-shop scheduling problem with no-wait restrictions. The production process consists of two machine centers, one has a single machine and the other has more than one parallel machine. A greedy heuristic named least deviation algorithm is designed and its worst case performance is analyzed. Computational results are also given to show the algorithm's average performance compared with some other algorithms. The least deviation algorithm outperforms the others in most cases tested here, and it is of low computational complexity and is easy to carry out,thus it is of favorable application value.
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
Directory of Open Access Journals (Sweden)
Gamal Abd El-Nasser A. Said
2014-01-01
Full Text Available Quadratic Assignment Problem (QAP is an NP-hard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life (QAP and analyze their performance in terms of both runtime efficiency and solution quality. The results show that Genetic Algorithm has a better solution quality while Tabu Search has a faster execution time in comparison with other Meta-heuristic algorithms for solving QAP.
A personification heuristic Genetic Algorithm for Digital Microfluidics-based Biochips Placement
Directory of Open Access Journals (Sweden)
Jingsong Yang
2013-06-01
Full Text Available A personification heuristic Genetic Algorithm is established for the placement of digital microfluidics-based biochips, in which, the personification heuristic algorithm is used to control the packing process, while the genetic algorithm is designed to be used in multi-objective placement results optimizing. As an example, the process of microfluidic module physical placement in multiplexed in-vitro diagnostics on human physiological fluids is simulated. The experiment results show that personification heuristic genetic algorithm can achieve better results in multi-objective optimization, compare to the parallel recombinative simulated annealing algorithm.
Page, Andrew J; Keane, Thomas M; Naughton, Thomas J
2010-07-01
We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.
A Novel Heuristic Algorithm Based on Clark and Wright Algorithm for Green Vehicle Routing Problem
Mehdi Alinaghian; Zahra Kaviani; Siyavash Khaledan
2015-01-01
A significant portion of Gross Domestic Production (GDP) in any country belongs to the transportation system. Transportation equipment, in the other hand, is supposed to be great consumer of oil products. Many attempts have been assigned to the vehicles to cut down Greenhouse Gas (GHG). In this paper a novel heuristic algorithm based on Clark and Wright Algorithm called Green Clark and Wright (GCW) for Vehicle Routing Problem regarding to fuel consumption is presented. The objective function ...
Hybrid Ant Algorithm and Applications for Vehicle Routing Problem
Xiao, Zhang; Jiang-qing, Wang
Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.
Runway Operations Planning: A Two-Stage Heuristic Algorithm
Anagnostakis, Ioannis; Clarke, John-Paul
2003-01-01
The airport runway is a scarce resource that must be shared by different runway operations (arrivals, departures and runway crossings). Given the possible sequences of runway events, careful Runway Operations Planning (ROP) is required if runway utilization is to be maximized. From the perspective of departures, ROP solutions are aircraft departure schedules developed by optimally allocating runway time for departures given the time required for arrivals and crossings. In addition to the obvious objective of maximizing throughput, other objectives, such as guaranteeing fairness and minimizing environmental impact, can also be incorporated into the ROP solution subject to constraints introduced by Air Traffic Control (ATC) procedures. This paper introduces a two stage heuristic algorithm for solving the Runway Operations Planning (ROP) problem. In the first stage, sequences of departure class slots and runway crossings slots are generated and ranked based on departure runway throughput under stochastic conditions. In the second stage, the departure class slots are populated with specific flights from the pool of available aircraft, by solving an integer program with a Branch & Bound algorithm implementation. Preliminary results from this implementation of the two-stage algorithm on real-world traffic data are presented.
Solving SAT problem by heuristic polarity decision-making algorithm
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper presents a heuristic polarity decision-making algorithm for solving Boolean satisfiability (SAT). The algorithm inherits many features of the current state-of-the-art SAT solvers, such as fast BCP, clause recording, restarts, etc. In addition, a preconditioning step that calculates the polarities of variables according to the cover distribution of Karnaugh map is introduced into DPLL procedure, which greatly reduces the number of conflicts in the search process. The proposed approach is implemented as a SAT solver named DiffSat. Experiments show that DiffSat can solve many "real-life" instances in a reasonable time while the best existing SAT solvers, such as Zchaff and MiniSat, cannot. In particular, DiffSat can solve every instance of Bart benchmark suite in less than 0.03 s while Zchaff and MiniSat fail under a 900 s time limit. Furthermore, DiffSat even outperforms the outstanding incomplete algorithm DLM in some instances.
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
Gamal Abd El-Nasser A. Said; Mahmoud, Abeer M.; El-Horbaty, El-Sayed M.
2014-01-01
Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life (QAP) and analyze their performance in terms of both runtime efficiency and solution quality. The results show that Genetic Algorithm has a better solution quality wh...
Heuristic Algorithms Applied to Train Station Parking using information of Transponders
2013-01-01
Train Station Parking has received increasing concentration as Platform Screen Doors (PSDs) are widely used in Urban Rail Transit. Aiming to enhance the accuracy and robustness of Train Station Parking, we proposed three algorithms which are Newton Dynamics based Algorithm (NDA), Heuristic Learning based Algorithm (HLA) and Heuristic Algorithm based on deceleration deviations Sequences (HAS) by using the information of transponders, essential locating equipments in subway. Then we verify the ...
A Hybrid Demon Algorithm for the Two-Dimensional Orthogonal Strip Packing Problem
Directory of Open Access Journals (Sweden)
Bili Chen
2015-01-01
Full Text Available This paper develops a hybrid demon algorithm for a two-dimensional orthogonal strip packing problem. This algorithm combines a placement procedure based on an improved heuristic, local search, and demon algorithm involved in setting one parameter. The hybrid algorithm is tested on a wide set of benchmark instances taken from the literature and compared with other well-known algorithms. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm.
A set-covering based heuristic algorithm for the periodic vehicle routing problem.
Cacchiani, V; Hemmelmayr, V C; Tricoire, F
2014-01-30
We present a hybrid optimization algorithm for mixed-integer linear programming, embedding both heuristic and exact components. In order to validate it we use the periodic vehicle routing problem (PVRP) as a case study. This problem consists of determining a set of minimum cost routes for each day of a given planning horizon, with the constraints that each customer must be visited a required number of times (chosen among a set of valid day combinations), must receive every time the required quantity of product, and that the number of routes per day (each respecting the capacity of the vehicle) does not exceed the total number of available vehicles. This is a generalization of the well-known vehicle routing problem (VRP). Our algorithm is based on the linear programming (LP) relaxation of a set-covering-like integer linear programming formulation of the problem, with additional constraints. The LP-relaxation is solved by column generation, where columns are generated heuristically by an iterated local search algorithm. The whole solution method takes advantage of the LP-solution and applies techniques of fixing and releasing of the columns as a local search, making use of a tabu list to avoid cycling. We show the results of the proposed algorithm on benchmark instances from the literature and compare them to the state-of-the-art algorithms, showing the effectiveness of our approach in producing good quality solutions. In addition, we report the results on realistic instances of the PVRP introduced in Pacheco et al. (2011) [24] and on benchmark instances of the periodic traveling salesman problem (PTSP), showing the efficacy of the proposed algorithm on these as well. Finally, we report the new best known solutions found for all the tested problems.
Evaluation of Meta-Heuristic Algorithms for Stable Feature Selection
Directory of Open Access Journals (Sweden)
Maysam Toghraee
2016-07-01
Full Text Available Now a days, developing the science and technology and technology tools, the ability of reviewing and saving the important data has been provided. It is needed to have knowledge for searching the data to reach the necessary useful results. Data mining is searching for big data sources automatically to find patterns and dependencies which are not done by simple statistical analysis. The scope is to study the predictive role and usage domain of data mining in medical science and suggesting a frame for creating, assessing and exploiting the data mining patterns in this field. As it has been found out from previous researches that assessing methods can not be used to specify the data discrepancies, our suggestion is a new approach for assessing the data similarities to find out the relations between the variation in data and stability in selection. Therefore we have chosen meta heuristic methods to be able to choose the best and the stable algorithms among a set of algorithms
Identifying multiple influential spreaders by a heuristic clustering algorithm
Bao, Zhong-Kui; Liu, Jian-Guo; Zhang, Hai-Feng
2017-03-01
The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very "negligible". Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant.
A Heuristic Clustering Algorithm for Mining Communities in Signed Networks
Institute of Scientific and Technical Information of China (English)
Bo Yang; Da-You Liu
2007-01-01
Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks.So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures.Finally, it is insensitive to its built-in parameters and requires no prior knowledge.
2008-06-01
A HEURISTIC ALGORITHM FOR...SUBTITLE: A Heuristic Algorithm for Optimized Routing of Unmanned Aerial Systems for the Interdiction of Improvised Explosive Devices 6. AUTHOR(S...INTENTIONALLY LEFT BLANK iii Approved for public release; distribution is unlimited A HEURISTIC ALGORITHM FOR OPTIMIZED ROUTING OF UNMANNED
The planning of order picking in a warehouse by heuristic algorithms
Uršič, Jakob
2014-01-01
Planning of order picking is essential process in every warehouse. In this thesis, we developed a simple warehouse simulator, which allows us to do various searches on path finding for a certain amount of items for one or more robots, using the A* algorithm. Heuristic guidance of search is mainly based on heuristic evaluation. We have implemented five different heuristic estimates, which we tested experimentally on examples with different warehouse configurations and with different numbers of...
A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem
Gonçalves, José Fernando; Mendes, J. J. M.; Resende, Maurício G. C.
2005-01-01
This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set o...
Directory of Open Access Journals (Sweden)
M. Saravanan
2014-03-01
Full Text Available A Hybrid flow shop scheduling is characterized ‘n’ jobs ‘m’ machines with ‘M’ stages by unidirectional flow of work with a variety of jobs being processed sequentially in a single-pass manner. The paper addresses the multi-stage hybrid flow shop scheduling problems with missing operations. It occurs in many practical situations such as stainless steel manufacturing company. The essential complexity of the problem necessitates the application of meta-heuristics to solve hybrid flow shop scheduling. The proposed Simulated Annealing algorithm (SA compared with Particle Swarm Optimization (PSO with the objective of minimization of makespan. It is show that the SA algorithm is efficient in finding out good quality solutions for the hybrid flow shop problems with missing operations.
A Fault—Tolerant and Heuristic Routing Algorithm for Faulty Hypercubes
Institute of Scientific and Technical Information of China (English)
闵有力; 闵应骅
1995-01-01
A fault-tolerant and heuristic routing algorithm for faulty hypercube systems is described.To improve the efficiency,the algorithm adopts a heuristic backtracking strategy and each node has an array to record its all neighbors' faulty link information to avoid unnecessary searching for the known faulty links.Furthermore,the faulty link information is dynamically accumulated and the technique of heuristically searching for optimal link is used.The algorithm routes messages through the minimum feasible path between the sender and receiver if at least one such path exists,and takes the optimal path with higher probability when faulty links exist in the faulty hypercube.
Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Abdulhamid, Shafi'i Muhammad; Usman, Mohammed Joda
2017-01-01
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.
Directory of Open Access Journals (Sweden)
D. A. Viattchenin
2009-01-01
Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible clusterization with partial training is proposed in the paper. The method is based on data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.
A known-plaintext heuristic attack on the Fourier plane encryption algorithm
Gopinathan, Unnikrishnan; Monaghan, David S.; Naughton, Thomas J.; Sheridan, John T.
2006-04-01
The Fourier plane encryption algorithm is subjected to a known-plaintext attack. The simulated annealing heuristic algorithm is used to estimate the key, using a known plaintext-ciphertext pair, which decrypts the ciphertext with arbitrarily low error. The strength of the algorithm is tested by using this estimated key to decrypt a different ciphertext which was also encrypted using the same original key. We assume that the plaintext is amplitude-encoded real-valued image, and analyze only the mathematical algorithm rather than a real optical system that can be more secure. The Fourier plane encryption algorithm is found to be susceptible to a known-plaintext heuristic attack.
Directory of Open Access Journals (Sweden)
Nelishia Pillay
2012-06-01
Full Text Available Hyper-heuristics are aimed at providing a generalized solution to optimization problems rather than producing the best result for one or more problem instances. This paper examines the use of evolutionary algorithm (EA selection hyper-heuristics to solve the offline one-dimensional bin-packing problem. Two EA hyper-heuristics are evaluated. The first (EA-HH1 searches a heuristic space of combinations of low-level construction heuristics for bin selection. The second (EA-HH2 explores a space of combinations of both item selection and bin selection heuristic combinations. These EA hyper-heuristics use tournament selection to choose parents, and mutation and crossover with hill-climbing to create the offspring of each generation. The performance of the hyper-heuristics is compared to that of each of the low-level heuristics applied independently to solve this problem. Furthermore, the performance of both hyper-heuristics is also compared. The comparisons revealed that hyper-heuristics in general perform better than any single low-level construction heuristic in solving the problem. In addition to this it was found that the hyper-heuristic exploring a space of both item selection and bin selection heuristic combinations is more effective than the hyper-heuristic searching a space of just bin selection heuristic combinations. The performance of this hyper-heuristic was found to be comparable to other methods applied to the same benchmark sets of problems.
Efficiency Improvements in Meta-Heuristic Algorithms to Solve the Optimal Power Flow Problem
Reddy, S. Surender; Bijwe, P. R.
2016-12-01
This paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained optimization problem. The main drawback of meta-heuristic algorithm based OPF is the excessive execution time required due to the large number of power flows needed in the solution process. The proposed efficient approaches uses the lower and upper bounds of objective function values. By using this approach, the number of power flows to be performed are reduced substantially, resulting in the solution speed up. The efficiently generated objective function bounds can result in the faster solutions of meta-heuristic algorithms. The original advantages of meta-heuristic algorithms, such as ability to handle complex non-linearities, discontinuities in the objective function, discrete variables handling, and multi-objective optimization, etc., are still available in the proposed efficient approaches. The proposed OPF formulation includes the active and reactive power generation limits, Valve Point Loading (VPL) and Prohibited Operating Zones (POZs) effects of generating units. The effectiveness of proposed approach is examined on IEEE 30, 118 and 300 bus test systems, and the simulation results confirm the efficiency and superiority of the proposed approaches over the other meta-heuristic algorithms. The proposed efficient approach is generic enough to use with any type of meta-heuristic algorithm based OPF.
A Novel Heuristic Algorithm Based on Clark and Wright Algorithm for Green Vehicle Routing Problem
Directory of Open Access Journals (Sweden)
Mehdi Alinaghian
2015-08-01
Full Text Available A significant portion of Gross Domestic Production (GDP in any country belongs to the transportation system. Transportation equipment, in the other hand, is supposed to be great consumer of oil products. Many attempts have been assigned to the vehicles to cut down Greenhouse Gas (GHG. In this paper a novel heuristic algorithm based on Clark and Wright Algorithm called Green Clark and Wright (GCW for Vehicle Routing Problem regarding to fuel consumption is presented. The objective function is fuel consumption, drivers, and the usage of vehicles. Being compared to exact methods solutions for small-sized problems and to Differential Evolution (DE algorithm solutions for large-scaled problems, the results show efficient performance of the proposed GCW algorithm.
Heuristic for Critical Machine Based a Lot Streaming for Two-Stage Hybrid Production Environment
Vivek, P.; Saravanan, R.; Chandrasekaran, M.; Pugazhenthi, R.
2017-03-01
Lot streaming in Hybrid flowshop [HFS] is encountered in many real world problems. This paper deals with a heuristic approach for Lot streaming based on critical machine consideration for a two stage Hybrid Flowshop. The first stage has two identical parallel machines and the second stage has only one machine. In the second stage machine is considered as a critical by valid reasons these kind of problems is known as NP hard. A mathematical model developed for the selected problem. The simulation modelling and analysis were carried out in Extend V6 software. The heuristic developed for obtaining optimal lot streaming schedule. The eleven cases of lot streaming were considered. The proposed heuristic was verified and validated by real time simulation experiments. All possible lot streaming strategies and possible sequence under each lot streaming strategy were simulated and examined. The heuristic consistently yielded optimal schedule consistently in all eleven cases. The identification procedure for select best lot streaming strategy was suggested.
A Heuristic and Hybrid Method for the Tank Allocation Problem in Maritime Bulk Shipping
DEFF Research Database (Denmark)
Vilhelmsen, Charlotte; Larsen, Jesper; Lusby, Richard Martin
and strength as well as other operational constraints. The problem of finding a feasible solution to this tank allocation problem has been shown to be NP-Complete. We approach the problem on a tactical level where requirements for computation time are strict while solution quality is less important than simply...... have created a hybrid method that first runs the heuristic and if the heuristic fails to solve the problem, then runs the modified optimality based method on the parts of the problem that the heuristic did not solve. This hybrid method cuts between 90% and 94% of the average running times compared...... finding a feasible solution. We have developed a heuristic that can efficiently find feasible cargo allocations. Computational results show that it can solve 99% of the considered instances within 0.4 seconds and all of them if allowed longer time. We have also modified an optimality based method from...
Conceptual space systems design using meta-heuristic algorithms
Kim, Byoungsoo
criteria. Two meta-heuristic optimization algorithms, Genetic Algorithms (GAs) and Simulated Annealing (SA), were used to optimize the formulated (simply bounded) Constrained Combinatorial Conceptual Space Systems Design Model. GAs and SA were demonstrated on the SAMPEX (Solar Anomalous & Magnetospheric Particle Explorer) Space System. The Conceptual Space Systems Design Model developed in this thesis can be used as an assessment tool to evaluate and validate Space System proposals.
A Simplicial Algorithm for Concave Minimization and Its Performance as a Heuristic Tool
Kuno, Takahito; Shiguro, Yoshiyuki
2007-01-01
In this paper, we develop a kind of branch-and-bound algorithm for solving concaveminimization problems. We show that the algorithm converges to an optimalsolution of this multiextremal global optimization problem, and that it generatesa high-quality heuristic solution even if it is forced to terminate. Therefore, thealgorithm can be used in two ways, as an exact algorithm and as a heuristic tool.We also report some numerical results of a comparison with an existing algorithm,and show the per...
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...
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...
A heuristic algorithm for scheduling in a flow shop environment to minimize makespan
Directory of Open Access Journals (Sweden)
Arun Gupta
2015-04-01
Full Text Available Scheduling ‘n’ jobs on ‘m’ machines in a flow shop is NP- hard problem and places itself at prominent place in the area of production scheduling. The essence of any scheduling algorithm is to minimize the makespan in a flowshop environment. In this paper an attempt has been made to develop a heuristic algorithm, based on the reduced weightage of ma-chines at each stage to generate different combination of ‘m-1’ sequences. The proposed heuristic has been tested on several benchmark problems of Taillard (1993 [Taillard, E. (1993. Benchmarks for basic scheduling problems. European Journal of Operational Research, 64, 278-285.]. The performance of the proposed heuristic is compared with three well-known heuristics, namely Palmer’s heuristic, Campbell’s CDS heuristic, and Dannenbring’s rapid access heuristic. Results are evaluated with the best-known upper-bound solutions and found better than the above three.
two phase heuristic algorithm for the university course timetabling ...
African Journals Online (AJOL)
Mgina
Furthermore, the method compares well with previous work on Tabu Search but with faster execution time and ... good results given a careful selection of parameters. ... especially in educational institutions. ... This paper presents another heuristic ... Each day is ... Bad solutions .... widely used schedules involves geometric.
Directory of Open Access Journals (Sweden)
Stanimirović Ivan
2009-01-01
Full Text Available We introduce a heuristic method for the single resource constrained project scheduling problem, based on the dynamic programming solution of the knapsack problem. This method schedules projects with one type of resources, in the non-preemptive case: once started an activity is not interrupted and runs to completion. We compare the implementation of this method with well-known heuristic scheduling method, called Minimum Slack First (known also as Gray-Kidd algorithm, as well as with Microsoft Project.
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Maryam Ashouri
2017-07-01
Full Text Available Vehicle routing problem (VRP is a Nondeterministic Polynomial Hard combinatorial optimization problem to serve the consumers from central depots and returned back to the originated depots with given vehicles. Furthermore, two of the most important extensions of the VRPs are the open vehicle routing problem (OVRP and VRP with simultaneous pickup and delivery (VRPSPD. In OVRP, the vehicles have not return to the depot after last visit and in VRPSPD, customers require simultaneous delivery and pick-up service. The aim of this paper is to present a combined effective ant colony optimization (CEACO which includes sweep and several local search algorithms which is different with common ant colony optimization (ACO. An extensive numerical experiment is performed on benchmark problem instances addressed in the literature. The computational result shows that suggested CEACO approach not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms in the literature for solving VRP, OVRP and VRPSPD problems. Keywords: Meta-heuristic algorithms, Vehicle Routing Problem, Open Vehicle Routing Problem, Simultaneously Pickup and Delivery, Ant Colony Optimization.
Analysis of Petri net model and task planning heuristic algorithms for product reconfiguration
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Reconfiguration planning is recognized as an important factor for reducing the cost of manufacturing reconfigurable products, and the associated main task is to generate a set of optimal or near-optimal reconfiguration sequences using some effect algorithms. A method is developed to generate a Petri net as the reconfiguration tree to represent two-state-transit of product, which solved the representation problem of reconfiguring interfaces replacement. Relating with this method, two heuristic algorithms are proposed to generate task sequences which considering economics to search reconfiguration paths effectively. At last,an objective evaluation is applied to compare these two heuristic algorithms to other ones. The developed reconfiguration task planning heuristic algorithms can generate better strategies and plans for reconfiguration. The research finds are exemplified with struts reconfiguration of reconfigurable parallel kinematics machine (RPKM).
Influence of rounding errors on the quality of heuristic optimization algorithms
Ransberger, Martin; Morgenstern, Ingo; Schneider, Johannes J.
2011-07-01
Search space smoothing and related heuristic optimization algorithms provide an alternative approach to simulated annealing and its variants: while simulated annealing traverses barriers in the energy landscape at finite temperatures, search space smoothing intends to remove these barriers, so that a greedy algorithm is sufficient to find the global minimum. Several formulas for smoothing the energy landscape have already been applied, one of them making use of the finite numerical precision on a computer. In this paper, we thoroughly investigate the effect of finite numerical accuracy on the quality of results achieved with heuristic optimization algorithms. We present computational results for the traveling salesman problem.
TripNet: A Heuristic Algorithm for Constructing Rooted Phylogenetic Networks from Triplets
Poormohammadi, Hadi; Tusserkani, Ruzbeh
2012-01-01
The problem of constructing an optimal rooted phylogenetic network from a set of rooted triplets is an NP-hard problem. In this paper, we present a heuristic algorithm called TripNet which tries to construct an optimal rooted phylogenetic network from an arbitrary set of triplets. We prove some theorems to justify the performance of the algorithm.
A Heuristic Algorithm for P-Cycles Configuration in WDM Optical Networks
Institute of Scientific and Technical Information of China (English)
Zhenrong Zhang; Wen-De Zhong; Biswanath Mukherjee
2003-01-01
Aiming at minimizing spare capacity for optical WDM networks, we propose a new heuristic algorithm for preconfigured protection cycle (p-cycle) design. Numerical results show that the spare capacity obtained by our new algorithm is very close to the optimal solution.
Study of the Artificial Fish Swarm Algorithm for Hybrid Clustering
Directory of Open Access Journals (Sweden)
Hongwei Zhao
2015-06-01
Full Text Available The basic Artificial Fish Swarm (AFS Algorithm is a new type of an heuristic swarm intelligence algorithm, but it is difficult to optimize to get high precision due to the randomness of the artificial fish behavior, which belongs to the intelligence algorithm. This paper presents an extended AFS algorithm, namely the Cooperative Artificial Fish Swarm (CAFS, which significantly improves the original AFS in solving complex optimization problems. K-medoids clustering algorithm is being used to classify data, but the approach is sensitive to the initial selection of the centers with low quality of the divided cluster. A novel hybrid clustering method based on the CAFS and K-medoids could be used for solving clustering problems. In this work, first, CAFS algorithm is used for optimizing six widely-used benchmark functions, coming up with comparative results produced by AFS and CAFS, then Particle Swarm Optimization (PSO is studied. Second, the hybrid algorithm with K-medoids and CAFS algorithms is used for data clustering on several benchmark data sets. The performance of the hybrid algorithm based on K-medoids and CAFS is compared with AFS and CAFS algorithms on a clustering problem. The simulation results show that the proposed CAFS outperforms the other two algorithms in terms of accuracy and robustness.
Heuristic Genetic Algorithm for Discretization of Continuous Attributes in Rough Set Theory
Institute of Scientific and Technical Information of China (English)
CAO Yun-feng; WANG Yao-cai; WANG Jun-wei
2006-01-01
Discretization based on rough set theory aims to seek the possible minimum number of the cut set without weakening the indiscernibility of the original decision system. Optimization of discretization is an NP-complete problem and the genetic algorithm is an appropriate method to solve it. In order to achieve optimal discretization, first the choice of the initial set of cut set is discussed, because a good initial cut set can enhance the efficiency and quality of the follow-up algorithm. Second, an effective heuristic genetic algorithm for discretization of continuous attributes of the decision table is proposed, which takes the significance of cut dots as heuristic information and introduces a novel operator to maintain the indiscernibility of the original decision system and enhance the local research ability of the algorithm. So the algorithm converges quickly and has global optimizing ability. Finally, the effectiveness of the algorithm is validated through experiment.
A Heuristic Algorithm for Task Scheduling Based on Mean Load on Grid
Institute of Scientific and Technical Information of China (English)
Li-Na Ni; Jin-Quan Zhang; Chun-Gang Yan; Chang-Jun Jiang
2006-01-01
Efficient task scheduling is critical to achieving high performance on grid computing environment. The task scheduling on grid is studied as optimization problem in this paper. A heuristic task scheduling algorithm satisfying resources load balancing on grid environment is presented. The algorithm schedules tasks by employing mean load based on task predictive execution time as heuristic information to obtain an initial scheduling strategy. Then an optimal scheduling strategy is achieved by selecting two machines satisfying condition to change their loads via reassigning their tasks under the heuristic of their mean load. Methods of selecting machines and tasks are given in this paper to increase the throughput of the system and reduce the total waiting time. The efficiency of the algorithm is analyzed and the performance of the proposed algorithm is evaluated via extensive simulation experiments. Experimental results show that the heuristic algorithm performs significantly to ensure high load balancing and achieve an optimal scheduling strategy almost all the time. Furthermore, results show that our algorithm is high efficient in terms of time complexity.
Directory of Open Access Journals (Sweden)
Nur Ariffin Mohd Zin
2012-01-01
Full Text Available This paper discusses on a comparative study towards solution for solving Travelling Salesman Problem based on three techniques proposed namely exhaustive, heuristic and genetic algorithm. Each solution is to cater on finding an optimal path of available 25 contiguous cities in England whereby solution is written in Prolog. Comparisons were made with emphasis against time consumed and closeness to optimal solutions. Based on the experimental, we found that heuristic is very promising in terms of time taken, while on the other hand, Genetic Algorithm manages to be outstanding on big number of traversal by resulting the shortest path among the others.
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M. Kandasamy
2014-12-01
Full Text Available A comparison study of Firefly Algorithm (FA and Bacterial Foraging Algorithm (BFO optimization is carried out by applying them to a Non Linear pH neutralization process. In process control engineering, the Proportional, Derivative, Integral controller tuning parameters are deciding the performance of the controller to ensure the good performance of the plant. The FA and BFO algorithms are applied to obtain the optimum values of controller parameters. The performance indicators such as servo response and regulatory response tests are carried out to evaluate the efficiency of the heuristic algorithm based controllers. The error minimization criterion such as Integral Absolute Error (IAE, Integral Square Error (ISE, Integral Time Square Error (ITSE, Integral Time Absolute Error (ITAE and Time domain specifications – rise time, Peak Overshoot and settling time are considered for the study of the performance of the controllers. The study indicates that, FA tuned PID controller provides marginally better set point tracking, load disturbance rejection, time domain specifications and error minimization for the Non Linear pH neutralization process compared to BFO tuned PID controller.
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
Greedy heuristic algorithm for solving series of eee components classification problems*
Kazakovtsev, A. L.; Antamoshkin, A. N.; Fedosov, V. V.
2016-04-01
Algorithms based on using the agglomerative greedy heuristics demonstrate precise and stable results for clustering problems based on k- means and p-median models. Such algorithms are successfully implemented in the processes of production of specialized EEE components for using in space systems which include testing each EEE device and detection of homogeneous production batches of the EEE components based on results of the tests using p-median models. In this paper, authors propose a new version of the genetic algorithm with the greedy agglomerative heuristic which allows solving series of problems. Such algorithm is useful for solving the k-means and p-median clustering problems when the number of clusters is unknown. Computational experiments on real data show that the preciseness of the result decreases insignificantly in comparison with the initial genetic algorithm for solving a single problem.
Directory of Open Access Journals (Sweden)
Washington Alves de Oliveira
Full Text Available ABSTRACT In this work we propose a heuristic algorithm for the layout optimization for disks installed in a rotating circular container. This is a unequal circle packing problem with additional balance constraints. It proved to be an NP-hard problem, which justifies heuristics methods for its resolution in larger instances. The main feature of our heuristic is based on the selection of the next circle to be placed inside the container according to the position of the system's center of mass. Our approach has been tested on a series of instances up to 55 circles and compared with the literature. Computational results show good performance in terms of solution quality and computational time for the proposed algorithm.
Heuristic algorithm for RCPSP with the objective of minimizing activities' cost
Institute of Scientific and Technical Information of China (English)
Liu Zhenyuan; Wang Hongwei
2006-01-01
Resource-constrained project scheduling problem(RCPSP) is an important problem in research on project management. But there has been little attention paid to the objective of minimizing activities' cost with the resource constraints that is a critical sub-problem in partner selection of construction supply chain management because the capacities of the renewable resources supplied by the partners will effect on the project scheduling. Its mathematic model is presented firstly, and analysis on the characteristic of the problem shows that the objective function is non-regular and the problem is NP-complete following which the basic idea for solution is clarified. Based on a definition of preposing activity cost matrix, a heuristic algorithm is brought forward. Analyses on the complexity of the heuristics and the result of numerical studies show that the heuristic algorithm is feasible and relatively effective.
Inference from matrix products: a heuristic spin glass algorithm
Energy Technology Data Exchange (ETDEWEB)
Hastings, Matthew B [Los Alamos National Laboratory
2008-01-01
We present an algorithm for finding ground states of two-dimensional spin-glass systems based on ideas from matrix product states in quantum information theory. The algorithm works directly at zero temperature and defines an approximation to the energy whose accuracy depends on a parameter k. We test the algorithm against exact methods on random field and random bond Ising models, and we find that accurate results require a k which scales roughly polynomially with the system size. The algorithm also performs well when tested on small systems with arbitrary interactions, where no fast, exact algorithms exist. The time required is significantly less than Monte Carlo schemes.
A SAA-based Novel Hybrid Intelligent Evolutionary Algorithm for Job Shop Scheduling Problem
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Through systematic analysis and comparison of the common features of SAA, ES and traditional LS (local search) algorithm, a new hybrid strategy of mixing SA, ES with LS, namely HIEA (Hybrid Intelligent Evolutionary Algorithm), is proposed in this paper. Viewed as a whole, the hybrid strategy is also an intelligent heuristic searching procedure. But it has some characteristics such as generality, robustness, etc., because it synthesizes advantages of SA, ES and LS, while the shortages of the three methods are overcome. This paper applies Markov chain theory to describe the hybrid strategy mathematically, and proves that the algorithm possesses the global asymptotical convergence and analyzes the performance of HIEA.
Improving Multi-Component Maintenance Acquisition with a Greedy Heuristic Local Algorithm
2013-04-01
heuristic based on a genetic algorithm was applied in train maintenance scheduling problems by Sriskandarajah, Jardine , and Chan (1998), primarily...interdependent. OR Spectrum, 27(1), 63–84. Sriskandarajah, C., Jardine , A., & Chan, C. (1998). Maintenance scheduling of rolling stock using a genetic
A Heuristic Algorithm for the Two-Machine Flowshop Group Scheduling Problem
Institute of Scientific and Technical Information of China (English)
王秀利; 吴惕华
2002-01-01
This paper presents the two-machine flowshop group scheduling problem with the optimal objective ofmaximum lateness. A dominance rule within group and a dominance rule between groups are established. Thesedominance rules along with a previously established dominance rule are used to develop a heuristic algorithm.Experimental results are given and analyzed.
A Survey on Meta-Heuristic Global Optimization Algorithms
Directory of Open Access Journals (Sweden)
Mohammad Khajehzadeh
2011-06-01
Full Text Available Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Recently, metaheuristic global optimization algorithms have become a popular choice for solving complex and intricate problems, which are otherwise difficult to solve by traditional methods. In the present study, an attempt is made to review the most popular and well known metaheuristic global optimization algorithms introduced during the past decades.
Efficient heuristic algorithm used for optimal capacitor placement in distribution systems
Energy Technology Data Exchange (ETDEWEB)
Segura, Silvio; Rider, Marcos J. [Department of Electric Energy Systems, University of Campinas, Campinas, Sao Paulo (Brazil); Romero, Ruben [Faculty of Engineering of Ilha Solteira, Paulista State University, Ilha Solteira, Sao Paulo (Brazil)
2010-01-15
An efficient heuristic algorithm is presented in this work in order to solve the optimal capacitor placement problem in radial distribution systems. The proposal uses the solution from the mathematical model after relaxing the integrality of the discrete variables as a strategy to identify the most attractive bus to add capacitors to each step of the heuristic algorithm. The relaxed mathematical model is a non-linear programming problem and is solved using a specialized interior point method. The algorithm still incorporates an additional strategy of local search that enables the finding of a group of quality solutions after small alterations in the optimization strategy. Proposed solution methodology has been implemented and tested in known electric systems getting a satisfactory outcome compared with metaheuristic methods. The tests carried out in electric systems known in specialized literature reveal the satisfactory outcome of the proposed algorithm compared with metaheuristic methods. (author)
Heuristic based data scheduling algorithm for OFDMA wireless network
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks.A distributed scheduling algorithm (DSA) for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links.For the global fairness improvement of algorithms,a global power control scheduling algorithm (GPCSA) based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio.Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.
A Heuristic Clustering Algorithm for Intrusion Detection Based on Information Entropy
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This paper studied on the clustering problem for intrusion detection with the theory of information entropy, it was put forward that the clustering problem for exact intrusion detection based on information entropy is NP-complete, therefore, the heuristic algorithm to solve the clustering problem for intrusion detection was designed, this algorithm has the characteristic of incremental development, it can deal with the database with large connection records from the internet.
Directory of Open Access Journals (Sweden)
Masoud Shirzadi
2015-11-01
Full Text Available Since the dynamicity and inhomogeneity of resources complicates scheduling, it is not possible to use accurate scheduling algorithms. Therefore, many studies focus on heuristic algorithms like the artificial bee colony algorithm. Since, the artificial bee colony algorithm searches the problem space locally and has a poor performance in global search; global search algorithms like genetic algorithms should also be used to overcome this drawback. This study proposes a scheduling algorithm, which is combination of the genetic and artificial bee colony algorithms for the independent scheduling problem in a computing grid. This study aims to reduce the maximum total scheduling time. Simulation results indicate that the proposed algorithm reduces the maximum execution time (makespan by 10% in comparison to the compared methods.
Zheng, Jun-Xi; Zhang, Ping; Li, Fang; Du, Guang-Long
2016-09-01
Although the sequence-dependent setup times flowshop problem with the total weighted tardiness minimization objective exists widely in industry, work on the problem has been scant in the existing literature. To the authors' best knowledge, the NEH?EWDD heuristic and the Iterated Greedy (IG) algorithm with descent local search have been regarded as the high performing heuristic and the state-of-the-art algorithm for the problem, which are both based on insertion search. In this article firstly, an efficient backtracking algorithm and a novel heuristic (HPIS) are presented for insertion search. Accordingly, two heuristics are introduced, one is NEH?EWDD with HPIS for insertion search, and the other is the combination of NEH?EWDD and both the two methods. Furthermore, the authors improve the IG algorithm with the proposed methods. Finally, experimental results show that both the proposed heuristics and the improved IG (IG*) significantly outperform the original ones.
A genetic algorithm selection perturbative hyper-heuristic for solving ...
African Journals Online (AJOL)
http://dx.doi.org/10.5784/31-1-158. 39 ... in solving other combinatorial optimisation problems, this paper investigates the use of a genetic ...... [16] Goldberg D, 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-.
A heuristic algorithm for a multi-product four-layer capacitated location-routing problem
Directory of Open Access Journals (Sweden)
Mohsen Hamidi
2014-01-01
Full Text Available The purpose of this study is to solve a complex multi-product four-layer capacitated location-routing problem (LRP in which two specific constraints are taken into account: 1 plants have limited production capacity, and 2 central depots have limited capacity for storing and transshipping products. The LRP represents a multi-product four-layer distribution network that consists of plants, central depots, regional depots, and customers. A heuristic algorithm is developed to solve the four-layer LRP. The heuristic uses GRASP (Greedy Randomized Adaptive Search Procedure and two probabilistic tabu search strategies of intensification and diversification to tackle the problem. Results show that the heuristic solves the problem effectively.
Akhmedova, Sh; Semenkin, E.
2017-02-01
Previously, a meta-heuristic approach, called Co-Operation of Biology-Related Algorithms or COBRA, for solving real-parameter optimization problems was introduced and described. COBRA’s basic idea consists of a cooperative work of five well-known bionic algorithms such as Particle Swarm Optimization, the Wolf Pack Search, the Firefly Algorithm, the Cuckoo Search Algorithm and the Bat Algorithm, which were chosen due to the similarity of their schemes. The performance of this meta-heuristic was evaluated on a set of test functions and its workability was demonstrated. Thus it was established that the idea of the algorithms’ cooperative work is useful. However, it is unclear which bionic algorithms should be included in this cooperation and how many of them. Therefore, the five above-listed algorithms and additionally the Fish School Search algorithm were used for the development of five different modifications of COBRA by varying the number of component-algorithms. These modifications were tested on the same set of functions and the best of them was found. Ways of further improving the COBRA algorithm are then discussed.
Directory of Open Access Journals (Sweden)
Kristian M. Lien
1990-01-01
Full Text Available This paper presents a new algorithm based on the heuristic tearing algorithm by Gundersen and Hertzberg (1983. The basic idea in both the original and the proposed algorithm is sequential tearing of strong components which have been identified by an algorithm proposed by Targan (1972. The new algorithm has two alternative options for selection of tear streams, and alternative precedence orderings may be generated for the selected set of tear streams. The algorithm has been tested on several problems. It has identified minimal (optimal tear sets for all of them, including the four problems presented in Gundersen and Hertzberg (1983 where the original algorithm could not find a minimal tear set. A Lisp implementation of the algorithm is described, and example problems arc presented.
Institute of Scientific and Technical Information of China (English)
Xinchao ZHAO; Junling HAO
2007-01-01
In order to tradeoff exploration/exploitation and inspired by cell genetic algorithm a cellshift crossover operator for evolutionary algorithm(EA) is proposed in this paper.The definition domain is divided into n-dimension cubic sub-domains(cell) and each individual locates at an ndimensional cube.Cell-shift crossover first exchanges the cell numbers of the crossover pair if they are in the different cells(exploration)and subsequently shift the first individual from its initial place to the other individual's cell place.If they are already in the same cell heuristic crossover(exploitation) is used.Cell-shift/heuristic crossover adaptively executes exploration/exploitation search with the vary of genetic diversity.The cell-shift EA has excellent performance in terms of efficiency and efficacy on ten usually used optimization benchmarks when comparing with the recent well-known FEP evolutionary algorithm.
Delay-Constrained Multicast Routing Algorithm Based on Average Distance Heuristic
Ling, Zhou; Yu-xi, Zhu; 10.5121/ijcnc.2010.2212
2010-01-01
Multicast is the ability of a communication network to accept a single message from an application and to deliver copies of the message to multiple recipients at different location. With the development of Internet, Multicast is widely applied in all kinds of multimedia real-time application: distributed multimedia systems, collaborative computing, video-conferencing, distance education, etc. In order to construct a delay-constrained multicast routing tree, average distance heuristic (ADH) algorithm is analyzed firstly. Then a delay-constrained algorithm called DCADH (delay-constrained average distance heuristic) is presented. By using ADH a least cost multicast routing tree can be constructed; if the path delay can't meet the delay upper bound, a shortest delay path which is computed by Dijkstra algorithm will be merged into the existing multicast routing tree to meet the delay upper bound. Simulation experiments show that DCADH has a good performance in achieving a low-cost multicast routing tree.
Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks
Directory of Open Access Journals (Sweden)
Yuquan Guo
2017-01-01
Full Text Available Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.
A Heuristics-Based Parthenogenetic Algorithm for the VRP with Potential Demands and Time Windows
Directory of Open Access Journals (Sweden)
Chenghua Shi
2016-01-01
Full Text Available We present the vehicle routing problem with potential demands and time windows (VRP-PDTW, which is a variation of the classical VRP. A homogenous fleet of vehicles originated in a central depot serves customers with soft time windows and deliveries from/to their locations, and split delivery is considered. Also, besides the initial demand in the order contract, the potential demand caused by conformity consuming behavior is also integrated and modeled in our problem. The objective of minimizing the cost traveled by the vehicles and penalized cost due to violating time windows is then constructed. We propose a heuristics-based parthenogenetic algorithm (HPGA for successfully solving optimal solutions to the problem, in which heuristics is introduced to generate the initial solution. Computational experiments are reported for instances and the proposed algorithm is compared with genetic algorithm (GA and heuristics-based genetic algorithm (HGA from the literature. The comparison results show that our algorithm is quite competitive by considering the quality of solutions and computation time.
Heuristic Algorithms for Solving the Slot Planning Problem
DEFF Research Database (Denmark)
Parreno, Francisco; Pacino, Dario; Alvarez-Valdes, Ramon
In the Slot Planning Problem, for each location of the container ship we are given a list of containers to be loaded, and the problem is to assign each container to a feasible position, satisfying the specific packing constraints associated to the ship locations and to the different types...... of containers involved. We have developed a GRASP algorithm in which the constructive randomized phase packs as many containers as possible and the improvement phase tries several moves in order to minimize the number of containers left out. The algorithm has been tested on a set of real-world instances....
A Hybrid Architecture Approach for Quantum Algorithms
Directory of Open Access Journals (Sweden)
Mohammad R.S. Aghaei
2009-01-01
Full Text Available Problem statement: In this study, a general plan of hybrid architecture for quantum algorithms is proposed. Approach: Analysis of the quantum algorithms shows that these algorithms were hybrid with two parts. First, the relationship of classical and quantum parts of the hybrid algorithms was extracted. Then a general plan of hybrid structure was designed. Results: This plan was illustrated the hybrid architecture and the relationship of classical and quantum parts of the algorithms. This general plan was used to increase implementation performance of quantum algorithms. Conclusion/Recommendations: Moreover, simulation results of quantum algorithms on the hybrid architecture proved that quantum algorithms can be implemented on the general plan as well.
Heuristic algorithms for scheduling heat-treatment furnaces of steel casting industries
Indian Academy of Sciences (India)
M Mathirajan; V Chandru; A I Sivakumar
2007-10-01
This paper addresses a research problem of scheduling parallel, nonidentical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. We were led to this problem through a realworld application involving the scheduling of heat-treatment operations of steel casting. The scheduling of furnaces for heat-treatment of castings is of considerable interest as a large proportion of the total production time is the processing times of these operations. In view of the computational intractability of this type of problem, a few heuristic algorithms have been designed for maximizing the utilization of heat-treatment furnaces of steel casting manufacturing. Extensive computational experiments were carried out to compare the performance of the heuristics with the estimated optimal value (using the Weibull technique) and for relative effectiveness among the heuristics. Further, the computational experiments show that the heuristic algorithms proposed in this paper are capable of obtaining near (statistically estimated) optimal utilization of heat-treatment furnaces and are also capable of solving any large size real-life problems with a relatively low computational effort.
The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm
Directory of Open Access Journals (Sweden)
Zakir Hussain Ahmed
2014-01-01
Full Text Available The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.
The ordered clustered travelling salesman problem: a hybrid genetic algorithm.
Ahmed, Zakir Hussain
2014-01-01
The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.
A Heuristic Algorithm for Solving Triangle Packing Problem
Directory of Open Access Journals (Sweden)
Ruimin Wang
2013-01-01
Full Text Available The research on the triangle packing problem has important theoretic significance, which has broad application prospects in material processing, network resource optimization, and so forth. Generally speaking, the orientation of the triangle should be limited in advance, since the triangle packing problem is NP-hard and has continuous properties. For example, the polygon is not allowed to rotate; then, the approximate solution can be obtained by optimization method. This paper studies the triangle packing problem by a new kind of method. Such concepts as angle region, corner-occupying action, corner-occupying strategy, and edge-conjoining strategy are presented in this paper. In addition, an edge-conjoining and corner-occupying algorithm is designed, which is to obtain an approximate solution. It is demonstrated that the proposed algorithm is highly efficient, and by the time complexity analysis and the analogue experiment result is found.
HYBRID HEURISTIC-BASED ARTIFICIAL IMMUNE SYSTEM FOR TASK SCHEDULING
2011-01-01
Task scheduling problem in heterogeneous systems is the process of allocating tasks of an application to heterogeneous processors interconnected by high-speed networks, so that minimizing the finishing time of application as much as possible. Tasks are processing units of application and have precedenceconstrained, communication and also, are presented by Directed Acyclic Graphs (DAGs). Evolutionary algorithms are well suited for solving task scheduling problem in heterogeneous environment. I...
Application of Fuzzy Sets for the Improvement of Routing Optimization Heuristic Algorithms
Directory of Open Access Journals (Sweden)
Mattas Konstantinos
2016-12-01
Full Text Available The determination of the optimal circular path has become widely known for its difficulty in producing a solution and for the numerous applications in the scope of organization and management of passenger and freight transport. It is a mathematical combinatorial optimization problem for which several deterministic and heuristic models have been developed in recent years, applicable to route organization issues, passenger and freight transport, storage and distribution of goods, waste collection, supply and control of terminals, as well as human resource management. Scope of the present paper is the development, with the use of fuzzy sets, of a practical, comprehensible and speedy heuristic algorithm for the improvement of the ability of the classical deterministic algorithms to identify optimum, symmetrical or non-symmetrical, circular route. The proposed fuzzy heuristic algorithm is compared to the corresponding deterministic ones, with regard to the deviation of the proposed solution from the best known solution and the complexity of the calculations needed to obtain this solution. It is shown that the use of fuzzy sets reduced up to 35% the deviation of the solution identified by the classical deterministic algorithms from the best known solution.
A Hybrid Genetic Algorithm for the Multiple Crossdocks Problem
Directory of Open Access Journals (Sweden)
Zhaowei Miao
2012-01-01
Full Text Available We study a multiple crossdocks problem with supplier and customer time windows, where any violation of time windows will incur a penalty cost and the flows through the crossdock are constrained by fixed transportation schedules and crossdock capacities. We prove this problem to be NP-hard in the strong sense and therefore focus on developing efficient heuristics. Based on the problem structure, we propose a hybrid genetic algorithm (HGA integrating greedy technique and variable neighborhood search method to solve the problem. Extensive experiments under different scenarios were conducted, and results show that HGA outperforms CPLEX solver, providing solutions in realistic timescales.
Aligning multiple protein sequences by parallel hybrid genetic algorithm.
Nguyen, Hung Dinh; Yoshihara, Ikuo; Yamamori, Kunihito; Yasunaga, Moritoshi
2002-01-01
This paper presents a parallel hybrid genetic algorithm (GA) for solving the sum-of-pairs multiple protein sequence alignment. A new chromosome representation and its corresponding genetic operators are proposed. A multi-population GENITOR-type GA is combined with local search heuristics. It is then extended to run in parallel on a multiprocessor system for speeding up. Experimental results of benchmarks from the BAliBASE show that the proposed method is superior to MSA, OMA, and SAGA methods with regard to quality of solution and running time. It can be used for finding multiple sequence alignment as well as testing cost functions.
Hybrid Genetic Algorithm with PSO Effect for Combinatorial Optimisation Problems
Directory of Open Access Journals (Sweden)
M. H. Mehta
2012-12-01
Full Text Available In engineering field, many problems are hard to solve in some definite interval of time. These problems known as “combinatorial optimisation problems” are of the category NP. These problems are easy to solve in some polynomial time when input size is small but as input size grows problems become toughest to solve in some definite interval of time. Long known conventional methods are not able to solve the problems and thus proper heuristics is necessary. Evolutionary algorithms based on behaviours of different animals and species have been invented and studied for this purpose. Genetic Algorithm is considered a powerful algorithm for solving combinatorial optimisation problems. Genetic algorithms work on these problems mimicking the human genetics. It follows principle of “survival of the fittest” kind of strategy. Particle swarm optimisation is a new evolutionary approach that copies behaviour of swarm in nature. However, neither traditional genetic algorithms nor particle swarm optimisation alone has been completely successful for solving combinatorial optimisation problems. Here a hybrid algorithm is proposed in which strengths of both algorithms are merged and performance of proposed algorithm is compared with simple genetic algorithm. Results show that proposed algorithm works definitely better than the simple genetic algorithm.
Heuristic and Exact Algorithms for the Two-Machine Just in Time Job Shop Scheduling Problem
Directory of Open Access Journals (Sweden)
Mohammed Al-Salem
2016-01-01
Full Text Available The problem addressed in this paper is the two-machine job shop scheduling problem when the objective is to minimize the total earliness and tardiness from a common due date (CDD for a set of jobs when their weights equal 1 (unweighted problem. This objective became very significant after the introduction of the Just in Time manufacturing approach. A procedure to determine whether the CDD is restricted or unrestricted is developed and a semirestricted CDD is defined. Algorithms are introduced to find the optimal solution when the CDD is unrestricted and semirestricted. When the CDD is restricted, which is a much harder problem, a heuristic algorithm is proposed to find approximate solutions. Through computational experiments, the heuristic algorithms’ performance is evaluated with problems up to 500 jobs.
Heuristic file sorted assignment algorithm of parallel I/O on cluster computing system
Institute of Scientific and Technical Information of China (English)
CHEN Zhi-gang; ZENG Bi-qing; XIONG Ce; DENG Xiao-heng; ZENG Zhi-wen; LIU An-feng
2005-01-01
A new file assignment strategy of parallel I/O, which is named heuristic file sorted assignment algorithm was proposed on cluster computing system. Based on the load balancing, it assigns the files to the same disk according to the similar service time. Firstly, the files were sorted and stored at the set I in descending order in terms of their service time, then one disk of cluster node was selected randomly when the files were to be assigned, and at last the continuous files were taken orderly from the set I to the disk until the disk reached its load maximum. The experimental results show that the new strategy improves the performance by 20.2% when the load of the system is light and by 31.6% when the load is heavy. And the higher the data access rate, the more evident the improvement of the performance obtained by the heuristic file sorted assignment algorithm.
A heuristic path-estimating algorithm for large-scale real-time traffic information calculating
Institute of Scientific and Technical Information of China (English)
2008-01-01
As the original Global Position System (GPS) data in Floating Car Data have the accuracy problem,this paper proposes a heuristic path-estimating algorithm for large-scale real-time traffic information calculating. It uses the heuristic search method,imports the restriction with geometric operation,and makes comparison between the vectors composed of the vehicular GPS points and the special road network model to search the set of vehicular traveling route candidates. Finally,it chooses the most optimal one according to weight. Experimental results indicate that the algorithm has considerable efficiency in accuracy (over 92.7%) and com-putational speed (max 8000 GPS records per second) when handling the GPS tracking data whose sampling rate is larger than 1 min even under complex road network conditions.
Directory of Open Access Journals (Sweden)
Muhammad Farhan Ausaf
2015-12-01
Full Text Available Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven’t been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA. PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources.
Jawarneh, Sana; Abdullah, Salwani
2015-01-01
This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.
A heuristic approach to possibilistic clustering algorithms and applications
Viattchenin, Dmitri A
2013-01-01
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects. The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover, a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientist...
Directory of Open Access Journals (Sweden)
T.A. Yakovleva
2011-05-01
Full Text Available This paper is dealing with the vehicle routing problem, where different types of vehicles are managing to deliver different types of products. Three step heuristic with genetic algorithm is proposed for solving the problem.
National Research Council Canada - National Science Library
Chen, Jeng-Fung; Hsieh, Ho-Nien; Do, Quang
2014-01-01
.... In this study, an approach to the problem based on the artificial neural network (ANN) with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS...
Nuclear Power Plant Maintenance Optimization with Heuristic Algorithm
Directory of Open Access Journals (Sweden)
Andrija Volkanovski
2014-01-01
Full Text Available The test and maintenance activities are conducted in the nuclear power plants in order to prevent or limit failures resulting from the ageing or deterioration. The components and systems are partially or fully unavailable during the maintenance activities. This is especially important for the safety systems and corresponding equipment because they are important contributors to the overall nuclear power plant safety. A novel method for optimization of the maintenance activities in the nuclear power plant considering the plant safety is developed and presented. The objective function of the optimization is the mean value of the selected risk measure. The risk measure is assessed from the minimal cut sets identified in the Probabilistic Safety Assessment. The optimal solution of the objective function is estimated with genetic algorithm. The proposed method is applied on probabilistic safety analysis model of the selected safety system of the reference nuclear power plant. Obtained results show that optimization of maintenance decreases the risk and thus improves the plant safety. The implications of the consideration of different constraints on the obtained results are investigated and presented. The future prospects for the optimization of the maintenance activities in the nuclear power plants with the presented method are discussed.
Directory of Open Access Journals (Sweden)
Jorge A. Ruiz-Vanoye
2012-07-01
Full Text Available In this paper, we show a survey of meta-heuristics algorithms based on grouping of animals by social behavior for the Traveling Salesman Problem, and propose a new classification of meta-heuristics algorithms (not based on swarm intelligence theory based on grouping of animals: swarm algorithms, schools algorithms, flocks algorithms and herds algorithms: a The swarm algorithms (inspired by the insect swarms and zooplankton swarms: Ant Colony Optimization algorithm – ACO (inspired by the research on the behavior of ant colonies, Firefly Algorithm (based on fireflies, Marriage in Honey Bees Optimization Algorithm - MBO algorithm (inspired by the Honey Bee, Wasp Swarm Algorithm (inspired on the Parasitic wasps, Termite Algorithm (inspired by the termites, Mosquito swarms Algorithm – MSA (inspired by mosquito swarms, zooplankton swarms Algorithm - ZSA (inspired by the Zooplankton and Bumblebees Swarms Algorithm – BSA (inspired by Bumblebees. b The school algorithms (inspired by the fish schools: The Particle Swarm Optimization algorithm – PSO (inspired by social behavior and movement dynamics of fish or schooling. c The flock algorithms (inspired by the bird flocks: the flocking algorithm, and the Particle Swarm Optimization algorithm (inspired on the dynamics of the birds, d The herd and pack Algorithms (inspired by the mammal herds and packs: bat algorithm (inspired by bat, wolf pack search algorithm - WPS (inspired by wolfs, Rats herds algorithm - RATHA (inspired by Rats, Dolphins Herds Algorithm - DHA (inspired by Dolphins and the feral-dogs herd algorithm - FDHA (inspired by feral-dogs herd.
Energy Technology Data Exchange (ETDEWEB)
Bustamante-Cedeno, Enrique; Arora, Sant [Industrial and Systems Engineering Division, Mechanical Engineering Department, University of Minnesota, 111 Church Street, S.E., Minneapolis, MN 55455 (United States)
2009-04-15
In this paper, a Constructive Heuristic Algorithm (CHA) is presented to solve the Transmission Network Expansion Planning Problem (TNEP), a complex non-convex Mixed Integer Non-Linear Programming (MINLP) problem with multiple local minima. In the proposed algorithm, the non-linearities are resolved through the following feature: when discrete decision variables are given, the model becomes linear in the continuous variables. A CHA is developed which improves the current solution by implementing multiple step simultaneous changes over a number of saturated transmission lines, in contrast to the approach traditionally followed, which implements one change at a time. Solutions to test problems are computed. (author)
A Heuristic Algorithm for Multicast Routing with Delay and Bandwidth Constrains
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
An improved heuristic algorithm is developed which can optimize the mu lticast routing under the condition that both delay and bandwidth are constraine d. Performance analysis and computer simulation show that the routing mechanism can successfully solve the QoS problem in the case of many-to-many cast sessio n. The scheme can make the cost of routing tree optimized and the bandwidth and en d-to-end delay guaranteed. Because complexity of algorithm is limited, it is suitable to deal with networks of large size.
Symbolic Algorithmic Analysis of Rectangular Hybrid Systems
Institute of Scientific and Technical Information of China (English)
Hai-Bin Zhang; Zhen-Hua Duan
2009-01-01
This paper investigates symbolic algorithmic analysis of rectangular hybrid systems. To deal with the symbolic reachability problem, a restricted constraint system called hybrid zone is formalized for the representation and manipulation of rectangular automata state-spaces. Hybrid zones are proved to be closed over symbolic reachability operations of rectangular hybrid systems. They are also applied to model-checking procedures for verifying some important classes of timed computation tree logic formulas. To represent hybrid zones, a data structure called difference constraint matrix is defined.These enable us to deal with the symbolic algorithmic analysis of rectangular hybrid systems in an efficient way.
An effective Mixed Annealing/Heuristic Algorithm for problems in kinematic mechanical design
Ogot, Madara M.; Alag, Satnam S.
1993-04-01
The wide application of stochastic optimization methods in mechanical design has been partially hindered due to (a) the relatively long computation time required, and (b) discretization of the design space at the onset of the optimization process. This work proposes a new stochastic algorithm, the Mixed Annealing/Heuristic Algorithm (MAH), which addresses both these issues. It is based on the Simulated Annealing algorithm (SA) and the Heuristic Optimization Technique (HOT). Both these algorithms have been successfully applied to problems in mechanical design and up to now have been considered as competing algorithms. MAH capitalizes on each of their individual strengths and addresses their weaknesses, thereby considerably reducing the computational effort required to attain the final solution. A pseudo-continuous approach for configuration generation is employed, making the discretization of the design space no longer necessary. The effectiveness of MAH is demonstrated via three problems in kinematic synthesis. Comparison of the results with other stochastic optimization methods illustrates the potential of this technique.
Directory of Open Access Journals (Sweden)
Yahong Zheng
2014-05-01
Full Text Available Purpose: This paper focuses on a classic optimization problem in operations research, the flexible job shop scheduling problem (FJSP, to discuss the method to deal with uncertainty in a manufacturing system.Design/methodology/approach: In this paper, condition based maintenance (CBM, a kind of preventive maintenance, is suggested to reduce unavailability of machines. Different to the simultaneous scheduling algorithm (SSA used in the previous article (Neale & Cameron,1979, an inserting algorithm (IA is applied, in which firstly a pre-schedule is obtained through heuristic algorithm and then maintenance tasks are inserted into the pre-schedule scheme.Findings: It is encouraging that a new better solution for an instance in benchmark of FJSP is obtained in this research. Moreover, factually SSA used in literature for solving normal FJSPPM (FJSP with PM is not suitable for the dynamic FJSPPM. Through application in the benchmark of normal FJSPPM, it is found that although IA obtains inferior results compared to SSA used in literature, it performs much better in executing speed.Originality/value: Different to traditional scheduling of FJSP, uncertainty of machines is taken into account, which increases the complexity of the problem. An inserting algorithm (IA is proposed to solve the dynamic scheduling problem. It is stated that the quality of the final result depends much on the quality of the pre-schedule obtained during the procedure of solving a normal FJSP. In order to find the best solution of FJSP, a comparative study of three heuristics is carried out, the integrated GA, ACO and ABC. In the comparative study, we find that GA performs best in the three heuristic algorithms. Meanwhile, a new better solution for an instance in benchmark of FJSP is obtained in this research.
Adaptive and Reliable Control Algorithm for Hybrid System Architecture
Directory of Open Access Journals (Sweden)
Osama Abdel Hakeem Abdel Sattar
2012-01-01
Full Text Available A stand-alone system is defined as an autonomous system that supplies electricity without being connected to the electric grid. Hybrid systems combined renewable energy source, that are never depleted (such solar (photovoltaic (PV, wind, hydroelectric, etc. , With other sources of energy, like Diesel. If these hybrid systems are optimally designed, they can be more cost effective and reliable than single systems. However, the design of hybrid systems is complex because of the uncertain renewable energy supplies, load demands and the non-linear characteristics of some components, so the design problem cannot be solved easily by classical optimisation methods. The use of heuristic techniques, such as the genetic algorithms, can give better results than classical methods. This paper presents to a hybrid system control algorithm and also dispatches strategy design in which wind is the primary energy resource with photovoltaic cells. The dimension of the design (max. load is 2000 kW and the sources is implemented as flow 1500 kw from wind, 500 kw from solar and diesel 2000 kw. The main task of the preposed algorithm is to take full advantage of the wind energy and solar energy when it is available and to minimize diesel fuel consumption.
A Heuristic Optimal Discrete Bit Allocation Algorithm for Margin Maximization in DMT Systems
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Dong Shi-Wei
2007-01-01
Full Text Available A heuristic optimal discrete bit allocation algorithm is proposed for solving the margin maximization problem in discrete multitone (DMT systems. Starting from an initial equal power assignment bit distribution, the proposed algorithm employs a multistaged bit rate allocation scheme to meet the target rate. If the total bit rate is far from the target rate, a multiple-bits loading procedure is used to obtain a bit allocation close to the target rate. When close to the target rate, a parallel bit-loading procedure is used to achieve the target rate and this is computationally more efficient than conventional greedy bit-loading algorithm. Finally, the target bit rate distribution is checked, if it is efficient, then it is also the optimal solution; else, optimal bit distribution can be obtained only by few bit swaps. Simulation results using the standard asymmetric digital subscriber line (ADSL test loops show that the proposed algorithm is efficient for practical DMT transmissions.
A hybrid adaptive large neighborhood search heuristic for lot-sizing with setup times
DEFF Research Database (Denmark)
Muller, Laurent Flindt; Spoorendonk, Simon; Pisinger, David
2012-01-01
This paper presents a hybrid of a general heuristic framework and a general purpose mixed-integer programming (MIP) solver. The framework is based on local search and an adaptive procedure which chooses between a set of large neighborhoods to be searched. A mixed integer programming solver and its......, and the upper bounds found by the commercial MIP solver ILOG CPLEX using state-of-the-art MIP formulations. Furthermore, we improve the best known solutions on 60 out of 100 and improve the lower bound on all 100 instances from the literature...
A Hybrid Chaotic Quantum Evolutionary Algorithm
DEFF Research Database (Denmark)
Cai, Y.; Zhang, M.; Cai, H.
2010-01-01
A hybrid chaotic quantum evolutionary algorithm is proposed to reduce amount of computation, speed up convergence and restrain premature phenomena of quantum evolutionary algorithm. The proposed algorithm adopts the chaotic initialization method to generate initial population which will form...... and enhance the global search ability. A large number of tests show that the proposed algorithm has higher convergence speed and better optimizing ability than quantum evolutionary algorithm, real-coded quantum evolutionary algorithm and hybrid quantum genetic algorithm. Tests also show that when chaos...... is introduced to quantum evolutionary algorithm, the hybrid chaotic search strategy is superior to the carrier chaotic strategy, and has better comprehensive performance than the chaotic mutation strategy in most of cases. Especially, the proposed algorithm is the only one that has 100% convergence rate in all...
Directory of Open Access Journals (Sweden)
Antonio H. Escobar Z.
2011-01-01
Full Text Available This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found in less time when applied to low complexity electricity distribution networks and better quality solutions for highly complex networks when compared to a genetic algorithm using random initial populations.
Directory of Open Access Journals (Sweden)
Jeng-Fung Chen
2017-03-01
Full Text Available Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA and Cuckoo Optimization Algorithm (COA, are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods.
Heuristic algorithm for planning and scheduling of forged pieces heat treatment
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R. Lenort
2012-04-01
Full Text Available The paper presents a heuristic algorithm for planning and scheduling of forged pieces heat treatment which allows maximizing the capacity exploitation of the heat treatment process and the entire forging process. Five Focusing Steps continuous improvement process was selected as a methodological basis for the algorithm design. Its application was supported by simulation experiments performed on a dynamic computer model of the researched process. The experimental work has made it possible to elicit the general rules for planning and scheduling of the heat treatment process of forged pieces which reduce losses caused by equipment conversion and setup times, and which increase the throughput of this process. The HIPO diagram was used to design the algorithm.
Double Four-Bar Crank-Slider Mechanism Dynamic Balancing by Meta-Heuristic Algorithms
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Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by meta-heuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism.Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and run time values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental double four-bar crank-slider mechanism was provided for validating the proposedbalancing method practicall
Interpreting quantifier scope ambiguity: evidence of heuristic first, algorithmic second processing.
Directory of Open Access Journals (Sweden)
Veena D Dwivedi
Full Text Available The present work suggests that sentence processing requires both heuristic and algorithmic processing streams, where the heuristic processing strategy precedes the algorithmic phase. This conclusion is based on three self-paced reading experiments in which the processing of two-sentence discourses was investigated, where context sentences exhibited quantifier scope ambiguity. Experiment 1 demonstrates that such sentences are processed in a shallow manner. Experiment 2 uses the same stimuli as Experiment 1 but adds questions to ensure deeper processing. Results indicate that reading times are consistent with a lexical-pragmatic interpretation of number associated with context sentences, but responses to questions are consistent with the algorithmic computation of quantifier scope. Experiment 3 shows the same pattern of results as Experiment 2, despite using stimuli with different lexical-pragmatic biases. These effects suggest that language processing can be superficial, and that deeper processing, which is sensitive to structure, only occurs if required. Implications for recent studies of quantifier scope ambiguity are discussed.
Structure optimization by heuristic algorithm in a coarse-grained off-lattice model
Institute of Scientific and Technical Information of China (English)
Liu Jing-Fa
2009-01-01
A heuristic algorithm is presented for a three-dimensional off-lattice AB model consisting of hydrophobic (A) and hydrophilic (B) residues in Fibonacci sequences. By incorporating extra energy contributions into the original potential function, we convert the constrained optimization problem of AB model into an unconstrained optimization problem which can be solved by the gradient method. After the gradient minimization leads to the basins of the local energy minima, the heuristic off-trap strategy and subsequent neighborhood search mechanism are then proposed to get out of local minima and search for the lower-energy configurations. Furthermore, in order to improve the efficiency of the proposed algorithm, we apply the improved version called the new PERM with importance sampling (nPERMis) of the chain-growth algorithm, pruned-enriched-Rosenbluth method (PERM), to face-centered-cubic (FCC)-lattice to produce the initial configurations. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we found the ground state energies are lower than the best values reported in the present literature.
Availability Allocation of Networked Systems Using Markov Model and Heuristics Algorithm
Directory of Open Access Journals (Sweden)
Ruiying Li
2014-01-01
Full Text Available It is a common practice to allocate the system availability goal to reliability and maintainability goals of components in the early design phase. However, the networked system availability is difficult to be allocated due to its complex topology and multiple down states. To solve these problems, a practical availability allocation method is proposed. Network reliability algebraic methods are used to derive the availability expression of the networked topology on the system level, and Markov model is introduced to determine that on the component level. A heuristic algorithm is proposed to obtain the reliability and maintainability allocation values of components. The principles applied in the AGREE reliability allocation method, proposed by the Advisory Group on Reliability of Electronic Equipment, and failure rate-based maintainability allocation method persist in our allocation method. A series system is used to verify the new algorithm, and the result shows that the allocation based on the heuristic algorithm is quite accurate compared to the traditional one. Moreover, our case study of a signaling system number 7 shows that the proposed allocation method is quite efficient for networked systems.
Robot navigation in unknown terrains: Introductory survey of non-heuristic algorithms
Energy Technology Data Exchange (ETDEWEB)
Rao, N.S.V. [Oak Ridge National Lab., TN (US); Kareti, S.; Shi, Weimin [Old Dominion Univ., Norfolk, VA (US). Dept. of Computer Science; Iyengar, S.S. [Louisiana State Univ., Baton Rouge, LA (US). Dept. of Computer Science
1993-07-01
A formal framework for navigating a robot in a geometric terrain by an unknown set of obstacles is considered. Here the terrain model is not a priori known, but the robot is equipped with a sensor system (vision or touch) employed for the purpose of navigation. The focus is restricted to the non-heuristic algorithms which can be theoretically shown to be correct within a given framework of models for the robot, terrain and sensor system. These formulations, although abstract and simplified compared to real-life scenarios, provide foundations for practical systems by highlighting the underlying critical issues. First, the authors consider the algorithms that are shown to navigate correctly without much consideration given to the performance parameters such as distance traversed, etc. Second, they consider non-heuristic algorithms that guarantee bounds on the distance traversed or the ratio of the distance traversed to the shortest path length (computed if the terrain model is known). Then they consider the navigation of robots with very limited computational capabilities such as finite automata, etc.
A heuristic approach based on Clarke-Wright algorithm for open vehicle routing problem.
Pichpibul, Tantikorn; Kawtummachai, Ruengsak
2013-01-01
We propose a heuristic approach based on the Clarke-Wright algorithm (CW) to solve the open version of the well-known capacitated vehicle routing problem in which vehicles are not required to return to the depot after completing service. The proposed CW has been presented in four procedures composed of Clarke-Wright formula modification, open-route construction, two-phase selection, and route postimprovement. Computational results show that the proposed CW is competitive and outperforms classical CW in all directions. Moreover, the best known solution is also obtained in 97% of tested instances (60 out of 62).
A Genetic Algorithm-based Heuristic for Part-Feeding Mobile Robot Scheduling Problem
DEFF Research Database (Denmark)
Dang, Vinh Quang; Nielsen, Izabela Ewa; Bocewicz, Grzegorz
2012-01-01
This present study deals with the problem of sequencing feeding tasks of a single mobile robot with manipulation arm which is able to provide parts or components for feeders of machines in a manufacturing cell. The mobile robot has to be scheduled in order to keep machines within the cell produci....... A genetic algorithm-based heuristic is developed to find the near optimal solution for the problem. A case study is implemented at an impeller production line in a factory to demonstrate the result of the proposed approach....
A Genetic Algorithm-based Heuristic for Part-Feeding Mobile Robot Scheduling Problem
DEFF Research Database (Denmark)
Dang, Vinh Quang; Nielsen, Izabela Ewa; Bocewicz, Grzegorz
2012-01-01
This present study deals with the problem of sequencing feeding tasks of a single mobile robot with manipulation arm which is able to provide parts or components for feeders of machines in a manufacturing cell. The mobile robot has to be scheduled in order to keep machines within the cell produci....... A genetic algorithm-based heuristic is developed to find the near optimal solution for the problem. A case study is implemented at an impeller production line in a factory to demonstrate the result of the proposed approach....
Multithreaded Implementation of Hybrid String Matching Algorithm
Directory of Open Access Journals (Sweden)
Akhtar Rasool
2012-03-01
Full Text Available Reading and taking reference from many books and articles, and then analyzing the Navies algorithm, Boyer Moore algorithm and Knuth Morris Pratt (KMP algorithm and a variety of improved algorithms, summarizes various advantages and disadvantages of the pattern matching algorithms. And on this basis, a new algorithm – Multithreaded Hybrid algorithm is introduced. The algorithm refers to Boyer Moore algorithm, KMP algorithm and the thinking of improved algorithms. Utilize the last character of the string, the next character and the method to compare from side to side, and then advance a new hybrid pattern matching algorithm. And it adjusted the comparison direction and the order of the comparison to make the maximum moving distance of each time to reduce the pattern matching time. The algorithm reduces the comparison number and greatlyreduces the moving number of the pattern and improves the matching efficiency. Multithreaded implementation of hybrid, pattern matching algorithm performs the parallel string searching on different text data by executing a number of threads simultaneously. This approach is advantageous from all other string-pattern matching algorithm in terms of time complexity. This again improves the overall string matching efficiency.
The Rational Hybrid Monte Carlo Algorithm
Clark, M A
2006-01-01
The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.
The Rational Hybrid Monte Carlo algorithm
Clark, Michael
2006-12-01
The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.
DEFF Research Database (Denmark)
Soleimani, Hamed; Kannan, Govindan
2015-01-01
-heuristic algorithms are considered to develop a new elevated hybrid algorithm: the genetic algorithm (GA) and particle swarm optimization (PSO). Analyzing the above-mentioned algorithms' strengths and weaknesses leads us to attempt to improve the GA using some aspects of PSO. Therefore, a new hybrid algorithm...... is proposed and a complete validation process is undertaken using CPLEX and MATLAB software. In small instances, the global optimum points of CPLEX for the proposed hybrid algorithm are compared to genetic algorithm, and particle swarm optimization. Then, in small, mid, and large-size instances, performances...... of the proposed meta-heuristics are analyzed and evaluated. Finally, a case study involving an Iranian hospital furniture manufacturer is used to evaluate the proposed solution approach. The results reveal the superiority of the proposed hybrid algorithm when compared to the GA and PSO....
Cell Assignment in Hybrid CMOS/Nanodevices Architecture Using a PSO/SA Hybrid Algorithm
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Sadiq M. Sait
2013-10-01
anowire\\MOLecular Hybrid, higher circuit densities are possible. In CMOL there is an additional layer of nanofabric on top of CMOS stack. Nanodevices that lie between overlapping nanowires are programmable and can implement any combinational logic using a netlist of NOR gates. The limitation on the length of nanowires put a constraint on the connectivity domain of a circuit. The gates connected to each other must be within a connectivity radius; otherwise an extra buffer is inserted to connect them. Particle swarm optimization (PSO has been used in a variety of problems that are NP- hard. PSO compared to the other iterative heuristic techniques is simpler to implement. Besides, it delivers comparable results. In this paper, a hybrid of PSO and simulated annealing (SA for solving the cell assignment in CMOL, an NP-hard problem, is proposed. The proposed method takes advantage of the exploration and exploitation factors of PSO and the intrinsic hill climbing feature of SA to reduce the number of buffers to be inserted. Experiments conducted on ISCAS'89 benchmark circuits and a comparison with other heuristic techniques, are presented. Results showed that the proposed hybrid algorithm achieved better solution in terms of buffer count in reasonable time.
Directory of Open Access Journals (Sweden)
Thang Trung Nguyen
2016-01-01
Full Text Available This paper proposes an efficient Cuckoo-Inspired Meta-Heuristic Algorithm (CIMHA for solving multi-objective short-term hydrothermal scheduling (ST-HTS problem. The objective is to simultaneously minimize the total cost and emission of thermal units while all constraints such as power balance, water discharge, and generation limitations must be satisfied. The proposed CIMHA is a newly developed meta-heuristic algorithm inspired by the intelligent reproduction strategy of the cuckoo bird. It is efficient for solving optimization problems with complicated objective and constraints because the method has few control parameters. The proposed method has been tested on different systems with various numbers of objective functions, and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is more efficient than many other methods for the test systems in terms of total cost, total emission, and computational time. Therefore, the proposed CIMHA can be a favorable method for solving the multi-objective ST-HTS problems.
A hybrid ACO algorithm for the full truckload transportation problem
Doerner, Karl; Hartl, Richard F.; Reimann, Marc
2001-01-01
In this paper we propose a hybrid ACO approach to solve a full truckload transportation problem. Hybridization is achieved through the use of a problem specific heuristic. This heuristic is utilized both, to initialize the pheromone information and to construct solutions in the ACO pro-cedure. The main idea is to use information about the required fleetsize, by initializing the system with a number of vehicles rather than opening vehicles one at a time as needed. Our results show the advantag...
Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul
2015-01-01
In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems. PMID:25811858
Directory of Open Access Journals (Sweden)
Suheel Abdullah Malik
Full Text Available In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE through substitution is converted into a nonlinear ordinary differential equation (NODE. The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM, homotopy perturbation method (HPM, and optimal homotopy asymptotic method (OHAM, show that the suggested scheme is fairly accurate and viable for solving such problems.
ENHANCED HYBRID PSO – ACO ALGORITHM FOR GRID SCHEDULING
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P. Mathiyalagan
2010-07-01
Full Text Available Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. In this, heuristic approaches based on particle swarm optimization and ant colony optimization algorithms are adopted for solving task scheduling problems in grid environment. Particle Swarm Optimization (PSO is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The ACO algorithm is improved by modifying the pheromone updating rule. ACO algorithm is hybridized with PSO algorithm for efficient result and better convergence in PSO algorithm.
Exact and Heuristic Algorithms for Routing AGV on Path with Precedence Constraints
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Liang Xu
2016-01-01
Full Text Available A new problem arises when an automated guided vehicle (AGV is dispatched to visit a set of customers, which are usually located along a fixed wire transmitting signal to navigate the AGV. An optimal visiting sequence is desired with the objective of minimizing the total travelling distance (or time. When precedence constraints are restricted on customers, the problem is referred to as traveling salesman problem on path with precedence constraints (TSPP-PC. Whether or not it is NP-complete has no answer in the literature. In this paper, we design dynamic programming for the TSPP-PC, which is the first polynomial-time exact algorithm when the number of precedence constraints is a constant. For the problem with number of precedence constraints, part of the input can be arbitrarily large, so we provide an efficient heuristic based on the exact algorithm.
Directory of Open Access Journals (Sweden)
Puneet Rai
2014-02-01
Full Text Available Ant Colony Optimization (ACO is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.
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Sherin Zafar
2014-09-01
Full Text Available The paper comprehends an impending accost of intensifying biometric stationed authentication protocol(BSAP bestowing meta-heuristic genetic algorithm for securing MANET. Biometric authentication using fingerprint, facial, iris scan, voice recognition etc. have gain a lot of importance in recent years to provide security in MANET. Biometrics are more advantageous and secure as compared to prevailing data security techniques like password or token mechanisms. A higher level of security is achieved in our impending approach using genetic algorithm to overcome the security and privacy concerns that exist in biometric technology. The foremost requirement of our protocol is to overcome various data attacks such as wormhole, cache poisoning, invisible node attack etc. that are confronted by MANET and make the network more secure.
Directory of Open Access Journals (Sweden)
Qi Hu
2013-04-01
Full Text Available State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data’s real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.
求解全局优化问题的三角进化算法%Triangle Evolution-A Hybrid Heuristic for Global Optimization
Institute of Scientific and Technical Information of China (English)
罗长童; 于波
2009-01-01
This paper presents a hybrid heuristic-triangle evolution(TE)for global optimization.It is a real coded evolutionary algorithm.As in differential evolution(DE),TE targets each individual in current population and attempts to replace it by a new better individual.However,the way of generating new individuals is different.TE generates new individuals in a NelderMead way,while the simplices used in TE is 1 or 2 dimensional.The proposed algorithm is very easy to use and efficient for global optimization problems with continuous variables.Moreover.it requires only one(explicit)control parameter.Numerical results show that the new algorithm is comparable with DE for low dimensional problems but it outperforms DE for high dimensional problems.
A SCALABLE HYBRID MODULAR MULTIPLICATION ALGORITHM
Institute of Scientific and Technical Information of China (English)
Meng Qiang; Chen Tao; Dai Zibin; Chen Quji
2008-01-01
Based on the analysis of several familiar large integer modular multiplication algorithms,this paper proposes a new Scalable Hybrid modular multiplication (SHyb) algorithm which has scalable operands, and presents an RSA algorithm model with scalable key size. Theoretical analysis shows that SHyb algorithm requires m2n/2+2m iterations to complete an mn-bit modular multiplication with the application of an n-bit modular addition hardware circuit. The number of the required iterations can be reduced to a half of that of the scalable Montgomery algorithm. Consequently, the application scope of the RSA cryptosystem is expanded and its operation speed is enhanced based on SHyb algorithm.
A hybrid nested partitions algorithm for banking facility location problems
Xia, Li
2010-07-01
The facility location problem has been studied in many industries including banking network, chain stores, and wireless network. Maximal covering location problem (MCLP) is a general model for this type of problems. Motivated by a real-world banking facility optimization project, we propose an enhanced MCLP model which captures the important features of this practical problem, namely, varied costs and revenues, multitype facilities, and flexible coverage functions. To solve this practical problem, we apply an existing hybrid nested partitions algorithm to the large-scale situation. We further use heuristic-based extensions to generate feasible solutions more efficiently. In addition, the upper bound of this problem is introduced to study the quality of solutions. Numerical results demonstrate the effectiveness and efficiency of our approach. © 2010 IEEE.
Fitting PAC spectra with a hybrid algorithm
Energy Technology Data Exchange (ETDEWEB)
Alves, M. A., E-mail: mauro@sepn.org [Instituto de Aeronautica e Espaco (Brazil); Carbonari, A. W., E-mail: carbonar@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (Brazil)
2008-01-15
A hybrid algorithm (HA) that blends features of genetic algorithms (GA) and simulated annealing (SA) was implemented for simultaneous fits of perturbed angular correlation (PAC) spectra. The main characteristic of the HA is the incorporation of a selection criterion based on SA into the basic structure of GA. The results obtained with the HA compare favorably with fits performed with conventional methods.
Impulse denoising using Hybrid Algorithm
Directory of Open Access Journals (Sweden)
Ms.Arumugham Rajamani
2015-03-01
Full Text Available Many real time images facing a problem of salt and pepper noise contaminated,due to poor illumination and environmental factors. Many filters and algorithms are used to remove salt and pepper noise from the image, but it also removes image information. This paper proposes a new effective algorithm for diagnosing and removing salt and pepper noise is presented. The existing standard algorithms like Median Filter (MF, Weighted Median Filter (WMF, Standard Median Filter (SMF and so on, will yield poor performance particularly at high noise density. The suggested algorithm is compared with the above said standard algorithms using the metrics Mean Square Error (MSE and Peak Signal to Noise Ratio (PSNR value.The proposed algorithm exhibits more competitive performance results at all noise densities. The joint sorting and diagonal averaging algorithm has lower computational time,better quantitative results and improved qualitative result by a better visual appearance at all noise densities.
Heuristic Algorithm with Simulation Model for Searching Optimal Reservoir Rule Curves
Directory of Open Access Journals (Sweden)
Anongrit Kangrang
2009-01-01
Full Text Available This study proposes a heuristic algorithm to connect with simulation model for searching the optimal reservoir rule curves. The proposed model was applied to determine the optimal rule curves of the Ubolratana reservoir (the Chi River Basin, Thailand. The results showed that the pattern of the obtained rule curves similar to the existing rule curve. Then the obtained rule curves were used to simulate the Ubolratana reservoir system with the synthetic inflows. The results indicated that the frequency of water shortage and the average water shortage are reduced to 44.31 and 43.75% respectively, the frequency of excess release and the average excess release are reduced to 24.08% and 22.81%.
A Multi-Inner-World Genetic Algorithm Using Multiple Heuristics to Optimize Delivery Schedule
Sakurai, Yoshitaka; Onoyama, Takashi; Tsukamoto, Natsuki; Takada, Kouhei; Tsuruta, Setsuo
A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires to solve several tens to hundreds cities Traveling Salesman Problems (TSP) (1)(2) within interactive response time, with expert-level accuracy (less than about 3% of error rate). To meet these requirements, a multi-inner-world Genetic Algorithm (Miw-GA) method is developed. This method combines several types of GA's inner worlds. Each world of this method uses a different type of heuristics such as a 2-opt type mutation world and a block (Nearest Insertion) type mutation world. Comparison based on the results of experiments proved the method is superior to others and our previously proposed method.
Institute of Scientific and Technical Information of China (English)
Xiao LIU; Jia-wei YE
2011-01-01
We present a new algorithm for nesting problems.Many equally spaced points are set on a sheet,and a piece is moved to one of the points and rotated by an angle.Both the point and the rotation angle constitute the packing attitude of the piece.We propose a new algorithm named HAPE(Heuristic Algorithm based on the principle of minimum total Potential Energy)to find the optimal packing attitude at which the piece has the lowest center of gravity.In addition,a new technique for polygon overlap testing is proposed which avoids the time-consuming calculation of no-fit-polygon(NFP).The detailed implementation of HAPE is presented and two computational experiments are described.The first experiment is based on a real industrial problem and the second on 11 published benchmark problems.Using a hill-climbing(HC)search method,the proposed algorithm performs well in comparison with other published solutions.
Directory of Open Access Journals (Sweden)
Farahmand-Mehr Mohammad
2014-01-01
Full Text Available In this paper, a hybrid flow shop scheduling problem with a new approach considering time lags and sequence-dependent setup time in realistic situations is presented. Since few works have been implemented in this field, the necessity of finding better solutions is a motivation to extend heuristic or meta-heuristic algorithms. This type of production system is found in industries such as food processing, chemical, textile, metallurgical, printed circuit board, and automobile manufacturing. A mixed integer linear programming (MILP model is proposed to minimize the makespan. Since this problem is known as NP-Hard class, a meta-heuristic algorithm, named Genetic Algorithm (GA, and three heuristic algorithms (Johnson, SPTCH and Palmer are proposed. Numerical experiments of different sizes are implemented to evaluate the performance of presented mathematical programming model and the designed GA in compare to heuristic algorithms and a benchmark algorithm. Computational results indicate that the designed GA can produce near optimal solutions in a short computational time for different size problems.
de Leeuw, L.
Sixty-four fifth and sixth-grade pupils were taught number series extrapolation by either an algorithm, fully prescribed problem-solving method or a heuristic, less prescribed method. The trained problems were within categories of two degrees of complexity. There were 16 subjects in each cell of the 2 by 2 design used. Aptitude Treatment…
Damage identification of a TLP floating wind turbine by meta-heuristic algorithms
Ettefagh, M. M.
2015-12-01
Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring (SHM). In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP (Tension-Leg Platform) floating wind turbine structure. The Genetic Algorithms (GA), Artificial Immune System (AIS), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine (TLPFWT) is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.
A Heuristic Scheduling Algorithm for Minimizing Makespan and Idle Time in a Nagare Cell
Directory of Open Access Journals (Sweden)
M. Muthukumaran
2012-01-01
Full Text Available Adopting a focused factory is a powerful approach for today manufacturing enterprise. This paper introduces the basic manufacturing concept for a struggling manufacturer with limited conventional resources, providing an alternative solution to cell scheduling by implementing the technique of Nagare cell. Nagare cell is a Japanese concept with more objectives than cellular manufacturing system. It is a combination of manual and semiautomatic machine layout as cells, which gives maximum output flexibility for all kind of low-to-medium- and medium-to-high- volume productions. The solution adopted is to create a dedicated group of conventional machines, all but one of which are already available on the shop floor. This paper focuses on the development of heuristic scheduling algorithm in step-by-step method. The algorithm states that the summation of processing time of all products on each machine is calculated first and then the sum of processing time is sorted by the shortest processing time rule to get the assignment schedule. Based on the assignment schedule Nagare cell layout is arranged for processing the product. In addition, this algorithm provides steps to determine the product ready time, machine idle time, and product idle time. And also the Gantt chart, the experimental analysis, and the comparative results are illustrated with five (1×8 to 5×8 scheduling problems. Finally, the objective of minimizing makespan and idle time with greater customer satisfaction is studied through.
A Hybrid Evolutionary Algorithm for Discrete Optimization
Directory of Open Access Journals (Sweden)
J. Bhuvana
2015-03-01
Full Text Available Most of the real world multi-objective problems demand us to choose one Pareto optimal solution out of a finite set of choices. Flexible job shop scheduling problem is one such problem whose solutions are required to be selected from a discrete solution space. In this study we have designed a hybrid genetic algorithm to solve this scheduling problem. Hybrid genetic algorithms combine both the aspects of the search, exploration and exploitation of the search space. Proposed algorithm, Hybrid GA with Discrete Local Search, performs global search through the GA and exploits the locality through discrete local search. Proposed hybrid algorithm not only has the ability to generate Pareto optimal solutions and also identifies them with less computation. Five different benchmark test instances are used to evaluate the performance of the proposed algorithm. Results observed shown that the proposed algorithm has produced the known Pareto optimal solutions through exploration and exploitation of the search space with less number of functional evaluations.
Institute of Scientific and Technical Information of China (English)
Hun-Ki Chung; Kyu-Won Kim; Jong-Wook Chung; Jung-Ro Lee; Sok-Young Lee; Anupam Dixit; Hee-Kyoung Kang; Weiguo Zhao; Kenneth L. McNally; Ruraidh S. Hamilton; Jae-Gyun Gwag; Yong-Jin Park
2009-01-01
A new heuristic approach was undertaken for the establishment of a core set for the diversity research of rice. As a result,107 entries were selected from the 10 368 characterized accessions. The core set derived using this new approach provideda good representation of the characterized accessions present in the entire collection. No significant differences for the mean, range, standard deviation and coefficient of variation of each trait were observed between the core and existing collections. We also compared the diversity of core sets established using this Heuristic Core Collection (HCC) approach with those of core sets established using the conventional clustering methods. This modified heuristic algorithm can also be used to select genotype data with allelic richness and reduced redundancy, and to facilitate management and use of large collections of plant genetic resources in a more efficient way.
Index Fund Optimization Using a Genetic Algorithm and a Heuristic Local Search
Orito, Yukiko; Inoguchi, Manabu; Yamamoto, Hisashi
It is well known that index funds are popular passively managed portfolios and have been used very extensively for the hedge trading. Index funds consist of a certain number of stocks of listed companies on a stock market such that the fund's return rates follow a similar path to the changing rates of the market indices. However it is hard to make a perfect index fund consisting of all companies included in the given market index. Thus, the index fund optimization can be viewed as a combinatorial optimization for portfolio managements. In this paper, we propose an optimization method that consists of a genetic algorithm and a heuristic local search algorithm to make strong linear association between the fund's return rates and the changing rates of market index. We apply the method to the Tokyo Stock Exchange and make index funds whose return rates follow a similar path to the changing rates of Tokyo Stock Price Index (TOPIX). The results show that our proposal method makes the index funds with strong linear association to the market index by small computing time.
Optimized LTE cell planning for multiple user density subareas using meta-heuristic algorithms
Ghazzai, Hakim
2014-09-01
Base station deployment in cellular networks is one of the most fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation 4G-LTE cellular networks using meta heuristic algorithms. In this approach, we aim to satisfy both coverage and cell capacity constraints simultaneously by formulating a practical optimization problem. We start by performing a typical coverage and capacity dimensioning to identify the initial required number of base stations. Afterwards, we implement a Particle Swarm Optimization algorithm or a recently-proposed Grey Wolf Optimizer to find the optimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We have also performed Monte Carlo simulations to study the performance of the proposed scheme and computed the average number of users in outage. Results show that our proposed approach respects in all cases the desired network quality of services even for large-scale dimension problems.
Hybrid Genetic Algorithms with Fuzzy Logic Controller
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.``
Multicast Routing Based on Hybrid Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
CAO Yuan-da; CAI Gui
2005-01-01
A new multicast routing algorithm based on the hybrid genetic algorithm (HGA) is proposed. The coding pattern based on the number of routing paths is used. A fitness function that is computed easily and makes algorithm quickly convergent is proposed. A new approach that defines the HGA's parameters is provided. The simulation shows that the approach can increase largely the convergent ratio, and the fitting values of the parameters of this algorithm are different from that of the original algorithms. The optimal mutation probability of HGA equals 0.50 in HGA in the experiment, but that equals 0.07 in SGA. It has been concluded that the population size has a significant influence on the HGA's convergent ratio when it's mutation probability is bigger. The algorithm with a small population size has a high average convergent rate. The population size has little influence on HGA with the lower mutation probability.
Directory of Open Access Journals (Sweden)
Igor Stojanović
2017-01-01
Full Text Available The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.
The Novel Heuristic for Data Transmission Dynamic Scheduling Problems
Directory of Open Access Journals (Sweden)
Hao Xu
2013-01-01
Full Text Available The data transmission dynamic scheduling is a process that allocates the ground stations and available time windows to the data transmission tasks dynamically for improving the resource utilization. A novel heuristic is proposed to solve the data transmission dynamic scheduling problem. The characteristic of this heuristic is the dynamic hybridization of simple rules. Experimental results suggest that the proposed algorithm is correct, feasible, and available. The dynamic hybridization of simple rules can largely improve the efficiency of scheduling.
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Pickup and Delivery
Institute of Scientific and Technical Information of China (English)
Fang-Geng Zhao; Jiang-Sheng Sun; Su-Jian Li; Wei-Min Liu
2009-01-01
In this paper,a hybrid genetic algorithm (CA) is proposed for the traveling salesman problem (TSP) with pickup and delivery (TSPPD).In our algorithm,a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring.In addition,a local search procedure is integrated into the GA to accelerate convergence.The proposed GA has been tested on benchmark instances,and the computational results show that it gives better convergence than existing heuristics.
Heuristics of the algorithm: Big Data, user interpretation and institutional translation
Directory of Open Access Journals (Sweden)
Göran Bolin
2015-10-01
Full Text Available Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification build on large aggregates of information (Big Data analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike, Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings, one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.
Heuristics of the algorithm: Big Data, user interpretation and institutional translation
Directory of Open Access Journals (Sweden)
Göran Bolin
2015-10-01
Full Text Available Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media (e.g. laptops, smartphones, credit cards/swipe cards and radio-frequency identification build on large aggregates of information (Big Data analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences (i.e. categories recognisable to media users and industry representatives alike, Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected (it is our technological gadgets that are being surveyed, rather than us as social beings, one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy.
New Hybrid Algorithm for Question Answering
Directory of Open Access Journals (Sweden)
Jaspreet Kaur
2013-08-01
Full Text Available With technical advancement, Question Answering has emerged as the main area for the researchers. User is provided with specific answers instead of large number of documents or passages in question answering. Question answering proposes the solution to acquire efficient and exact answers to user question asked in natural language rather than language query. The major goal of this paper is to develop a hybrid algorithm for question answering. For this task different question answering systems for different languages were studied. After deep study, we are able to develop an algorithm that comprises the best features from excellent systems. An algorithm developed by us performs well.
A Computational Investigation of Heuristic Algorithms for 2-Edge-Connectivity Augmentation
DEFF Research Database (Denmark)
Bang-Jensen, Jørgen; Chiarandini, Marco; Morling, Peter
2010-01-01
programming, simple construction heuristics and metaheuristics. As part of the design of heuristics, we consider different neighborhood structures for local search, among which is a very large scale neighborhood. In all cases, we exploit approaches through the graph formulation as well as through...
Development of hybrid genetic algorithms for product line designs.
Balakrishnan, P V Sundar; Gupta, Rakesh; Jacob, Varghese S
2004-02-01
In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operator's process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairy large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options.
Roozitalab, Ali; Asgharizadeh, Ezzatollah
2013-12-01
Warranty is now an integral part of each product. Since its length is directly related to the cost of production, it should be set in such a way that it would maximize revenue generation and customers' satisfaction. Furthermore, based on the behavior of customers, it is assumed that increasing the warranty period to earn the trust of more customers leads to more sales until the market is saturated. We should bear in mind that different groups of consumers have different consumption behaviors and that performance of the product has a direct impact on the failure rate over the life of the product. Therefore, the optimum duration for every group is different. In fact, we cannot present different warranty periods for various customer groups. In conclusion, using cuckoo meta-heuristic optimization algorithm, we try to find a common period for the entire population. Results with high convergence offer a term length that will maximize the aforementioned goals simultaneously. The study was tested using real data from Appliance Company. The results indicate a significant increase in sales when the optimization approach was applied; it provides a longer warranty through increased revenue from selling, not only reducing profit margins but also increasing it.
Sarwono, A. A.; Ai, T. J.; Wigati, S. S.
2017-01-01
Vehicle Routing Problem (VRP) is a method for determining the optimal route of vehicles in order to serve customers starting from depot. Combination of the two most important problems in distribution logistics, which is called the two dimensional loading vehicle routing problem, is considered in this paper. This problem combines the loading of the freight into the vehicles and the successive routing of the vehicles along the route. Moreover, an additional feature of last-in-first-out loading sequencesis also considered. In the sequential two dimensional loading capacitated vehicle routing problem (sequential 2L-CVRP), the loading must be compatible with the trip sequence: when the vehicle arrives at a customer i, there must be no obstacle (items for other customers) between the item of i and the loading door (rear part) of the vehicle. In other words, it is not necessary to move non-i’s items whenever the unloading process of the items of i. According with aforementioned conditions, a program to solve sequential 2L-CVRP is required. A nearest neighbor algorithm for solving the routing problem is presented, in which the loading component of the problem is solved through a collection of 5 packing heuristics.
Jeng-Fung Chen; Ho-Nien Hsieh; Quang Hung Do
2014-01-01
Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN) with the two meta-heuristic algorithm...
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.
Hybrid Genetic Algorithms for University Course Timetabling
Directory of Open Access Journals (Sweden)
Meysam Shahvali Kohshori
2012-03-01
Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.
Directory of Open Access Journals (Sweden)
Jianhua Wang
2014-10-01
Full Text Available Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers.Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model.Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time.Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints.Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great
Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem
Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf
2017-08-01
Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.
Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search
Directory of Open Access Journals (Sweden)
Z. Ismail
2008-01-01
Full Text Available This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities resulted.
Institute of Scientific and Technical Information of China (English)
Pei-Chann Chang; Wei-Hsiu Huang; Zhen-Zhen Zhang
2012-01-01
In this research,we introduce a new heuristic approach using the concept of ant colony optimization (ACO)to extract patterns from the chromosomes generated by previous generations for solving the generalized traveling salesman problem.The proposed heuristic is composed of two phases.In the first phase the ACO technique is adopted to establish an archive consisting of a set of non-overlapping blocks and of a set of remaining cities (nodes) to be visited.The second phase is a block recombination phase where the set of blocks and the rest of cities are combined to form an artificial chromosome.The generated artificial chromosomes (ACs) will then be injected into a standard genetic algorithm (SGA) to speed up the convergence.The proposed method is called "Puzzle-Based Genetic Algorithm" or "p-ACGA".We demonstrate that p-ACGA performs very well on all TSPLIB problems,which have been solved to optimality by other researchers.The proposed approach can prevent the early convergence of the genetic algorithm (GA) and lead the algorithm to explore and exploit the search space by taking advantage of the artificial chromosomes.
A Hybrid Intelligent Algorithm for Optimal Birandom Portfolio Selection Problems
Directory of Open Access Journals (Sweden)
Qi Li
2014-01-01
Full Text Available Birandom portfolio selection problems have been well developed and widely applied in recent years. To solve these problems better, this paper designs a new hybrid intelligent algorithm which combines the improved LGMS-FOA algorithm with birandom simulation. Since all the existing algorithms solving these problems are based on genetic algorithm and birandom simulation, some comparisons between the new hybrid intelligent algorithm and the existing algorithms are given in terms of numerical experiments, which demonstrate that the new hybrid intelligent algorithm is more effective and precise when the numbers of the objective function computations are the same.
Application of a hybrid generation/utility assessment heuristic to a class of scheduling problems
Heyward, Ann O.
1989-01-01
A two-stage heuristic solution approach for a class of multiobjective, n-job, 1-machine scheduling problems is described. Minimization of job-to-job interference for n jobs is sought. The first stage generates alternative schedule sequences by interchanging pairs of schedule elements. The set of alternative sequences can represent nodes of a decision tree; each node is reached via decision to interchange job elements. The second stage selects the parent node for the next generation of alternative sequences through automated paired comparison of objective performance for all current nodes. An application of the heuristic approach to communications satellite systems planning is presented.
Multimodal Logistics Network Design over Planning Horizon through a Hybrid Meta-Heuristic Approach
Shimizu, Yoshiaki; Yamazaki, Yoshihiro; Wada, Takeshi
Logistics has been acknowledged increasingly as a key issue of supply chain management to improve business efficiency under global competition and diversified customer demands. This study aims at improving a quality of strategic decision making associated with dynamic natures in logistics network optimization. Especially, noticing an importance to concern with a multimodal logistics under multiterms, we have extended a previous approach termed hybrid tabu search (HybTS). The attempt intends to deploy a strategic planning more concretely so that the strategic plan can link to an operational decision making. The idea refers to a smart extension of the HybTS to solve a dynamic mixed integer programming problem. It is a two-level iterative method composed of a sophisticated tabu search for the location problem at the upper level and a graph algorithm for the route selection at the lower level. To keep efficiency while coping with the resulting extremely large-scale problem, we invented a systematic procedure to transform the original linear program at the lower-level into a minimum cost flow problem solvable by the graph algorithm. Through numerical experiments, we verified the proposed method outperformed the commercial software. The results indicate the proposed approach can make the conventional strategic decision much more practical and is promising for real world applications.
Cluster hybrid Monte Carlo simulation algorithms
Plascak, J. A.; Ferrenberg, Alan M.; Landau, D. P.
2002-06-01
We show that addition of Metropolis single spin flips to the Wolff cluster-flipping Monte Carlo procedure leads to a dramatic increase in performance for the spin-1/2 Ising model. We also show that adding Wolff cluster flipping to the Metropolis or heat bath algorithms in systems where just cluster flipping is not immediately obvious (such as the spin-3/2 Ising model) can substantially reduce the statistical errors of the simulations. A further advantage of these methods is that systematic errors introduced by the use of imperfect random-number generation may be largely healed by hybridizing single spin flips with cluster flipping.
Directory of Open Access Journals (Sweden)
Yu Lin
2015-01-01
Full Text Available High frequency and small lot size are characteristics of milk runs and are often used to implement the just-in-time (JIT strategy in logistical systems. The common frequency problem, which simultaneously involves planning of the route and frequency, has been extensively researched in milk run systems. In addition, vehicle type choice in the milk run system also has a significant influence on the operating cost. Therefore, in this paper, we simultaneously consider vehicle routing planning, frequency planning, and vehicle type choice in order to optimize the sum of the cost of transportation, inventory, and dispatch. To this end, we develop a mathematical model to describe the common frequency problem with vehicle type choice. Since the problem is NP hard, we develop a two-phase heuristic algorithm to solve the model. More specifically, an initial satisfactory solution is first generated through a greedy heuristic algorithm to maximize the ratio of the superior arc frequency to the inferior arc frequency. Following this, a tabu search (TS with limited search scope is used to improve the initial satisfactory solution. Numerical examples with different sizes establish the efficacy of our model and our proposed algorithm.
A hybrid heuristic ordering and Variable Neighbourhood Search for the nurse rostering problem
Burke, Edmund; Curtois, Timothy; De Causmaecker, P.; Post, Gerhard; Berghe, van den G.; Trick, M.A.; Burke, E.K.
2004-01-01
This paper is concerned with the development of intelligent decision support methodologies for nurse rostering problems in large modern hospital environments. We present an approach which hybridises heuristic ordering with variable neighbourhood search. We show that the search can be extended and th
Directory of Open Access Journals (Sweden)
Chunfeng Liu
2013-01-01
Full Text Available The paper presents a novel hybrid genetic algorithm (HGA for a deterministic scheduling problem where multiple jobs with arbitrary precedence constraints are processed on multiple unrelated parallel machines. The objective is to minimize total tardiness, since delays of the jobs may lead to punishment cost or cancellation of orders by the clients in many situations. A priority rule-based heuristic algorithm, which schedules a prior job on a prior machine according to the priority rule at each iteration, is suggested and embedded to the HGA for initial feasible schedules that can be improved in further stages. Computational experiments are conducted to show that the proposed HGA performs well with respect to accuracy and efficiency of solution for small-sized problems and gets better results than the conventional genetic algorithm within the same runtime for large-sized problems.
Ouroboros: A Tool for Building Generic, Hybrid, Divide& Conquer Algorithms
Energy Technology Data Exchange (ETDEWEB)
Johnson, J R; Foster, I
2003-05-01
A hybrid divide and conquer algorithm is one that switches from a divide and conquer to an iterative strategy at a specified problem size. Such algorithms can provide significant performance improvements relative to alternatives that use a single strategy. However, the identification of the optimal problem size at which to switch for a particular algorithm and platform can be challenging. We describe an automated approach to this problem that first conducts experiments to explore the performance space on a particular platform and then uses the resulting performance data to construct an optimal hybrid algorithm on that platform. We implement this technique in a tool, ''Ouroboros'', that automatically constructs a high-performance hybrid algorithm from a set of registered algorithms. We present results obtained with this tool for several classical divide and conquer algorithms, including matrix multiply and sorting, and report speedups of up to six times achieved over non-hybrid algorithms.
A Heuristic and Hybrid Method for the Tank Allocation Problem in Maritime Bulk Shipping
DEFF Research Database (Denmark)
Vilhelmsen, Charlotte; Larsen, Jesper; Lusby, Richard Martin
Many bulk ships have multiple tanks and can thereby carry multiple inhomogeneous products at a time. A major challenge when operating such ships is how to best allocate cargoes to available tanks while taking tank capacity, safety restrictions, ship stability and strength as well as other...... above is an operational planning problem but it also arises as a subproblem in tactical planning when routing bulk ship eets. For each considered route, the TAP must be solved to assess route feasibility with respect to stowage. If the routing problem is solved in a way that requires assessment...... ship route. We have developed a randomised heuristic for eciently nding feasible allocations and computational results show that it can solve 99% of the considered instances within 0.5 seconds and all of them if allowed longer time. The heuristic is designed to work as an ecient subproblem solver...
Santos, Matilde; Lopez, Victoria
2011-01-01
In a Role-Playing Game, finding optimal trajectories is one of the most important tasks. In fact, the strategy decision system becomes a key component of a game engine. Determining the way in which decisions are taken (e.g. online, batch or simulated) and the consumed resources in decision making (e.g. execution time, memory) will influence, in mayor degree, the game performance. When classical search algorithms such as A* can be used, they are the very first option. Nevertheless, such methods rely on precise and complete models of the search space, and so there are many interesting scenarios where its application is not possible, hence model free methods for sequential decision making under uncertainty are the best choice. In this paper, we propose a heuristic planning strategy to incorporate, into a Dyna agent, the ability of heuristic-search in path-finding. The proposed Dyna-H algorithm, as A* does, selects branches more likely to produce outcomes than other branches. However, it has the advantages, A* ha...
Solving the Quadratic Assignment Problem by a Hybrid Algorithm
Directory of Open Access Journals (Sweden)
Aldy Gunawan
2011-01-01
Full Text Available This paper presents a hybrid algorithm to solve the Quadratic Assignment Problem (QAP. The proposed algorithm involves using the Greedy Randomized Adaptive Search Procedure (GRASP to obtain an initial solution, and then using a combined Simulated Annealing (SA and Tabu Search (TS algorithm to improve the solution. Experimental results indicate that the hybrid algorithm is able to obtain good quality solutions for QAPLIB test problems within reasonable computation time.
A Hybrid Search Algorithm for Midterm Optimal Scheduling of Thermal Power Plants
Directory of Open Access Journals (Sweden)
Shengli Liao
2015-01-01
Full Text Available A hybrid search algorithm consisting of three stages is presented to solve the midterm schedule for thermal power plants (MTSFTPP problem, where the primary objective is to achieve equal accumulated operating hours of installed capacity (EAOHIC for all thermal power plants during the selected period. First, feasible spaces are produced and narrowed based on constraints on the number of units and power load factors. Second, an initial feasible solution is obtained by a heuristic method that considers operating times and boundary conditions. Finally, the progressive optimality algorithm (POA, which we refer to as the vertical search algorithm (VSA, is used to solve the MTSFTPP problem. A method for avoiding convergence to a local minimum, called the lateral search algorithm (LSA, is presented. The LSA provides an updated solution that is used as a new feasible starting point for the next search in the VSA. The combination of the LSA and the VSA is referred to as the hybrid search algorithm (HSA, which is simple and converges quickly to the global minimum. The results of two case studies show that the algorithm is very effective in solving the MTSFTPP problem accurately and in real time.
BiCluE - Exact and heuristic algorithms for weighted bi-cluster editing of biomedical data
DEFF Research Database (Denmark)
Sun, Peng; Guo, Jiong; Baumbach, Jan
2013-01-01
different types. Bi-cluster editing, as a special case of clustering, which partitions two different types of data simultaneously, might be used for several biomedical scenarios. However, the underlying algorithmic problem is NP-hard.RESULTS:Here we contribute with BiCluE, a software package designed...... to solve the weighted bi-cluster editing problem. It implements (1) an exact algorithm based on fixed-parameter tractability and (2) a polynomial-time greedy heuristics based on solving the hardest part, edge deletions, first. We evaluated its performance on artificial graphs. Afterwards we exemplarily...... applied our implementation on real world biomedical data, GWAS data in this case. BiCluE generally works on any kind of data types that can be modeled as (weighted or unweighted) bipartite graphs.CONCLUSIONS:To our knowledge, this is the first software package solving the weighted bi-cluster editing...
A Hybrid Genetic Algorithm for Reduct of Attributes in Decision System Based on Rough Set Theory
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Knowledge reduction is an important issue when dealing with huge amounts of data. And it has been proved that computing the minimal reduct of decision system is NP-complete. By introducing heuristic information into genetic algorithm, we proposed a heuristic genetic algorithm. In the genetic algorithm, we constructed a new operator to maintaining the classification ability. The experiment shows that our algorithm is efficient and effective for minimal reduct, even for the special example that the simple heuristic algorithm can't get the right result.
Directory of Open Access Journals (Sweden)
R. Deepalakshmi
2014-01-01
Full Text Available Today, Internet of Things (IoT has introduced abundant bandwidth consumption and necessities in multimedia communications from online games to video-conferencing applications with the constraint of QoS requirements from time to time. The expected rapid proliferation of services would require performance unprecedented in the currently available best-effort routing algorithms. In, particular, the present routing mechanisms are based on the best-effort paradigm are unlikely to provide satisfactory end-to-end performance for services required in the real time applications. Thus, there is a definite need for architectures and algorithms that provide bandwidth guaranteed and QoS guarantees beyond those of the currently available ones. The proposed routing algorithm addressed the problem by computing low cost trees with delay bounded within the model wherein the bandwidth can be reserved and guaranteed once reserved on various links of the network there by providing QoS guarantees. This novel tree-pruning algorithm aids the bandwidth measurement tools by applying heuristic approach and the effectiveness of the algorithm is determined by two factors (i the end-to-end delay (ii the cost of routing. The new data structure significantly improves the running time complexity by O (log k for routing procedures under a variety of QoS constraints and checking tree routing runs in O(m+n^{2}.
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Dawid Połap
2017-09-01
Full Text Available In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO. The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space.
Heuristic space diversity control for improved meta-hyper-heuristic performance
CSIR Research Space (South Africa)
Grobler, J
2015-04-01
Full Text Available This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm in search of greater performance benefits...
Heuristic space diversity management in a meta-hyper-heuristic framework
CSIR Research Space (South Africa)
Grobler, J
2014-07-01
Full Text Available This paper introduces the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm. Evaluation on a diverse set of floating...
A Hybrid Aggressive Space Mapping Algorithm for EM Optimization
DEFF Research Database (Denmark)
Bakr, M.; Bandler, J. W.; Georgieva, N.;
1999-01-01
We present a novel, Hybrid Aggressive Space Mapping (HASM) optimization algorithm. HASM is a hybrid approach exploiting both the Trust Region Aggressive Space Mapping (TRASM) algorithm and direct optimization. It does not assume that the final space-mapped design is the true optimal design and is...
New MPPT algorithm based on hybrid dynamical theory
Elmetennani, Shahrazed
2014-11-01
This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.
A Fast Hybrid Algorithm for the Exact String Matching Problem
Directory of Open Access Journals (Sweden)
Abdulwahab A. Al-mazroi
2011-01-01
Full Text Available Problem statement: Due to huge amount and complicated nature of data being generated recently, the usage of one algorithm for string searching was not sufficient to ensure faster search and matching of patterns. So there is the urgent need to integrate two or more algorithms to form a hybrid algorithm (called BRSS to ensure speedy results. Approach: This study proposes the combination of two algorithms namely Berry-Ravindran and Skip Search Algorithms to form a hybrid algorithm in order to boost search performance. Results: The proposed hybrid algorithm contributes to better results by reducing the number of attempts, number of character comparisons and searching time. The performance of the hybrid was tested using different types of data-DNA, Protein and English text. The percentage of the improvements of the hybrid algorithm compared to Berry-Ravindran in DNA, Protein and English text are 50%, 43% and 44% respectively. The percentage of the improvements over Skip Search algorithm in DNA, Protein and English text are 20%, 30% and 18% respectively. The criteria applied for evaluation are number of attempts, number of character comparisons and searching time. Conclusion: The study shows how the integration of two algorithms gives better results than the original algorithms even the same data size and pattern lengths are applied as test evaluation on each of the algorithms.
A hybrid genetic algorithm for route optimization in the bale collecting problem
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C. Gracia
2013-06-01
Full Text Available The bale collecting problem (BCP appears after harvest operations in grain and other crops. Its solution defines the sequence of collecting bales which lie scattered over the field. Current technology on navigation-aid systems or auto-steering for agricultural vehicles and machines, is able to provide accurate data to make a reliable bale collecting planning. This paper presents a hybrid genetic algorithm (HGA approach to address the BCP pursuing resource optimization such as minimizing non-productive time, fuel consumption, or distance travelled. The algorithmic route generation provides the basis for a navigation tool dedicated to loaders and bale wagons. The approach is experimentally tested on a set of instances similar to those found in real situations. In particular, comparative results show an average improving of a 16% from those obtained by previous heuristics.
Intelligent System Design Using Hyper-Heuristics
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Nelishia Pillay
2015-07-01
Full Text Available Determining the most appropriate search method or artificial intelligence technique to solve a problem is not always evident and usually requires implementation of the different approaches to ascertain this. In some instances a single approach may not be sufficient and hybridization of methods may be needed to find a solution. This process can be time consuming. The paper proposes the use of hyper-heuristics as a means of identifying which method or combination of approaches is needed to solve a problem. The research presented forms part of a larger initiative aimed at using hyper-heuristics to develop intelligent hybrid systems. As an initial step in this direction, this paper investigates this for classical artificial intelligence uninformed and informed search methods, namely depth first search, breadth first search, best first search, hill-climbing and the A* algorithm. The hyper-heuristic determines the search or combination of searches to use to solve the problem. An evolutionary algorithm hyper-heuristic is implemented for this purpose and its performance is evaluated in solving the 8-Puzzle, Towers of Hanoi and Blocks World problems. The hyper-heuristic employs a generational evolutionary algorithm which iteratively refines an initial population using tournament selection to select parents, which the mutation and crossover operators are applied to for regeneration. The hyper-heuristic was able to identify a search or combination of searches to produce solutions for the twenty 8-Puzzle, five Towers of Hanoi and five Blocks World problems. Furthermore, admissible solutions were produced for all problem instances.
Directory of Open Access Journals (Sweden)
Jian-Lin Jiang
2013-01-01
Full Text Available This paper considers the locations of multiple facilities in the space , with the aim of minimizing the sum of weighted distances between facilities and regional customers, where the proximity between a facility and a regional customer is evaluated by the closest distance. Due to the fact that facilities are usually allowed to be sited in certain restricted areas, some locational constraints are imposed to the facilities of our problem. In addition, since the symmetry of distances is sometimes violated in practical situations, the gauge is employed in this paper instead of the frequently used norms for measuring both the symmetric and asymmetric distances. In the spirit of the Cooper algorithm (Cooper, 1964, a new location-allocation heuristic algorithm is proposed to solve this problem. In the location phase, the single-source subproblem with regional demands is reformulated into an equivalent linear variational inequality (LVI, and then, a projection-contraction (PC method is adopted to find the optimal locations of facilities, whereas in the allocation phase, the regional customers are allocated to facilities according to the nearest center reclassification (NCR. The convergence of the proposed algorithm is proved under mild assumptions. Some preliminary numerical results are reported to show the effectiveness of the new algorithm.
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Jeng-Fung Chen
2014-10-01
Full Text Available Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS and Cuckoo Optimization Algorithm (COA is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.
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K. Duraiswamy
2012-01-01
Full Text Available The moving object or vehicle location prediction based on their spatial and temporal information is an important task in many applications. Different methods were utilized for performing the vehicle movement detection and prediction process. In such works, there is a lack of analysis in predicting the vehicles location in current as well as in future. Moreover, such methods compute the vehicles movement by finding the topological relationships among trajectories and locations, whereas the representative GPS points are determined by the 30 m circular window. Due to this process, the performance of the method is degraded because such 30 m circular window is selected by calculating the error range in the given input image and such error range may vary from image to image. To reduce the drawback presented in the existing method, in this study a heuristic moving vehicle location prediction algorithm is proposed. The proposed heuristic algorithm mainly comprises two techniques namely, optimization GA algorithm and FFBNN. In this proposed technique, initially the vehicles frequent paths are collected by monitoring all the vehicles movement in a specific period. Among the frequent paths, the vehicles optimal paths are computed by the GA algorithm. The selected optimal paths for each vehicle are utilized to train the FFBNN. The well trained FFBNN is then utilized to find the vehicle movement from the current location. By combining the proposed heuristic algorithm with GA and FFBNN, the vehicles location is predicted efficiently. The implementation result shows the effectiveness of the proposed heuristic algorithm in predicting the vehicles future location from the current location. The performance of the heuristic algorithm is evaluated by comparing the result with the RBF classifier. The comparison result shows our proposed technique acquires an accurate vehicle location prediction ratio than the RBF prediction ratio, in terms of accuracy.
Heuristics for the economic dispatch problem
Energy Technology Data Exchange (ETDEWEB)
Flores, Benjamin Carpio [Centro Nacional de Controle de Energia (CENACE), Mexico, D.F. (Mexico). Dept. de Planificacion Economica de Largo Plazo], E-mail: benjamin.carpo@cfe.gob.mx; Laureano Cruces, A.L.; Lopez Bracho, R.; Ramirez Rodriguez, J. [Universidad Autonoma Metropolitana (UAM), Mexico, D.F. (Brazil). Dept. de Sistemas], Emails: clc@correo.azc.uam.mx, rlb@correo.azc.uam.mx, jararo@correo.azc.uam.mx
2009-07-01
This paper presents GRASP (Greedy Randomized Adaptive Search Procedure), Simulated Annealing (SAA), Genetic (GA), and Hybrid Genetic (HGA) Algorithms for the economic dispatch problem (EDP), considering non-convex cost functions and dead zones the only restrictions, showing the results obtained. We also present parameter settings that are specifically applicable to the EDP, and a comparative table of results for each heuristic. It is shown that these methods outperform the classical methods without the need to assume convexity of the target function. (author)
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Hui Zhou
2016-10-01
Full Text Available Real-time detection of gait events can be applied as a reliable input to control drop foot correction devices and lower-limb prostheses. Among the different sensors used to acquire the signals associated with walking for gait event detection, the accelerometer is considered as a preferable sensor due to its convenience of use, small size, low cost, reliability, and low power consumption. Based on the acceleration signals, different algorithms have been proposed to detect toe off (TO and heel strike (HS gait events in previous studies. While these algorithms could achieve a relatively reasonable performance in gait event detection, they suffer from limitations such as poor real-time performance and are less reliable in the cases of up stair and down stair terrains. In this study, a new algorithm is proposed to detect the gait events on three walking terrains in real-time based on the analysis of acceleration jerk signals with a time-frequency method to obtain gait parameters, and then the determination of the peaks of jerk signals using peak heuristics. The performance of the newly proposed algorithm was evaluated with eight healthy subjects when they were walking on level ground, up stairs, and down stairs. Our experimental results showed that the mean F1 scores of the proposed algorithm were above 0.98 for HS event detection and 0.95 for TO event detection on the three terrains. This indicates that the current algorithm would be robust and accurate for gait event detection on different terrains. Findings from the current study suggest that the proposed method may be a preferable option in some applications such as drop foot correction devices and leg prostheses.
A HYBRID THINNING ALGORITHM FOR BINARY TOPOGRAPHY MAP
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A hybrid thinning algorithm for binary topography maps is proposed on the basis of parallel thinning templates in this paper.The algorithm has a high processing speed and the strong ability of noise immunity and preservation of connectivity and skeleton symmetry. Experimental results show that the algorithm can solve t he thinning problem of binary maps effectively.
Constructive Heuristic Algorithms for the CARP Problem%CARP问题的构造型启发式算法研究
Institute of Scientific and Technical Information of China (English)
李庆华; 林丹
2011-01-01
全面综述了国内外用于求解容量约束弧路径问题(CARP问题)的构造型启发式算法的研究现状,指出了构造型启发式算法与元启发式算法相比而言的优点所在.将求解算法分为3类并且分别进行简要介绍,最后展望了构造型启发式算法的研究前景.%The constructive heuristic algorithms of the capacitated arc routing problems (CARP problem) comprehensively in research status at home and abroad . This paper points out the advantages of the constructive heuristic algorithms in comparison with the meta-heuristic. These algorithms are divided into three categories and are briefly introduced. Eventually,it is the looking forward to the prospects of constructive heuristic algorithms .
A Hybrid Differential Invasive Weed Algorithm for Congestion Management
Basak, Aniruddha; Pal, Siddharth; Pandi, V. Ravikumar; Panigrahi, B. K.; Das, Swagatam
This work is dedicated to solve the problem of congestion management in restructured power systems. Nowadays we have open access market which pushes the power system operation to their limits for maximum economic benefits but at the same time making the system more susceptible to congestion. In this regard congestion management is absolutely vital. In this paper we try to remove congestion by generation rescheduling where the cost involved in the rescheduling process is minimized. The proposed algorithm is a hybrid of Invasive Weed Optimization (IWO) and Differential Evolution (DE). The resultant hybrid algorithm was applied on standard IEEE 30 bus system and observed to beat existing algorithms like Simple Bacterial foraging (SBF), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE) and hybrid algorithms like Hybrid Bacterial Foraging and Differential Evolution (HBFDE) and Adaptive Bacterial Foraging with Nelder Mead (ABFNM).
Quality of Service Routing in Manet Using a Hybrid Intelligent Algorithm Inspired by Cuckoo Search
Directory of Open Access Journals (Sweden)
S. Rajalakshmi
2015-01-01
Full Text Available A hybrid computational intelligent algorithm is proposed by integrating the salient features of two different heuristic techniques to solve a multiconstrained Quality of Service Routing (QoSR problem in Mobile Ad Hoc Networks (MANETs is presented. The QoSR is always a tricky problem to determine an optimum route that satisfies variety of necessary constraints in a MANET. The problem is also declared as NP-hard due to the nature of constant topology variation of the MANETs. Thus a solution technique that embarks upon the challenges of the QoSR problem is needed to be underpinned. This paper proposes a hybrid algorithm by modifying the Cuckoo Search Algorithm (CSA with the new position updating mechanism. This updating mechanism is derived from the differential evolution (DE algorithm, where the candidates learn from diversified search regions. Thus the CSA will act as the main search procedure guided by the updating mechanism derived from DE, called tuned CSA (TCSA. Numerical simulations on MANETs are performed to demonstrate the effectiveness of the proposed TCSA method by determining an optimum route that satisfies various Quality of Service (QoS constraints. The results are compared with some of the existing techniques in the literature; therefore the superiority of the proposed method is established.
Quality of Service Routing in Manet Using a Hybrid Intelligent Algorithm Inspired by Cuckoo Search.
Rajalakshmi, S; Maguteeswaran, R
2015-01-01
A hybrid computational intelligent algorithm is proposed by integrating the salient features of two different heuristic techniques to solve a multiconstrained Quality of Service Routing (QoSR) problem in Mobile Ad Hoc Networks (MANETs) is presented. The QoSR is always a tricky problem to determine an optimum route that satisfies variety of necessary constraints in a MANET. The problem is also declared as NP-hard due to the nature of constant topology variation of the MANETs. Thus a solution technique that embarks upon the challenges of the QoSR problem is needed to be underpinned. This paper proposes a hybrid algorithm by modifying the Cuckoo Search Algorithm (CSA) with the new position updating mechanism. This updating mechanism is derived from the differential evolution (DE) algorithm, where the candidates learn from diversified search regions. Thus the CSA will act as the main search procedure guided by the updating mechanism derived from DE, called tuned CSA (TCSA). Numerical simulations on MANETs are performed to demonstrate the effectiveness of the proposed TCSA method by determining an optimum route that satisfies various Quality of Service (QoS) constraints. The results are compared with some of the existing techniques in the literature; therefore the superiority of the proposed method is established.
A PRODUCT HYBRID GMRES ALGORITHM FOR NONSYMMETRIC LINEAR SYSTEMS
Institute of Scientific and Technical Information of China (English)
Bao-jiang Zhong
2005-01-01
It has been observed that the residual polynomials resulted from successive restarting cycles of GMRES(m) may differ from one another meaningfully. In this paper, it is further shown that the polynomials can complement one another harmoniously in reducing the iterative residual. This characterization of GMRES(m) is exploited to formulate an efficient hybrid iterative scheme, which can be widely applied to existing hybrid algorithms for solving large nonsymmetric systems of linear equations. In particular, a variant of the hybrid GMRES algorithm of Nachtigal, Reichel and Trefethen (1992) is presented. It is described how the new algorithm may offer significant performance improvements over the original one.
Hybrid Algorithm for Optimal Load Sharing in Grid Computing
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A. Krishnan
2012-01-01
Full Text Available Problem statement: Grid Computing is the fast growing industry, which shares the resources in the organization in an effective manner. Resource sharing requires more optimized algorithmic structure, otherwise the waiting time and response time are increased and the resource utilization is reduced. Approach: In order to avoid such reduction in the performances of the grid system, an optimal resource sharing algorithm is required. In recent days, many load sharing technique are proposed, which provides feasibility but there are many critical issues are still present in these algorithms. Results: In this study a hybrid algorithm for optimization of load sharing is proposed. The hybrid algorithm contains two components which are Hash Table (HT and Distributed Hash Table (DHT. Conclusion: The results of the proposed study show that the hybrid algorithm will optimize the task than existing systems.
A hybrid genetic algorithm to optimize simple distillation column sequences
Institute of Scientific and Technical Information of China (English)
GAN YongSheng; Andreas Linninger
2004-01-01
Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
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Ahmed M.E. Khalil
2015-06-01
Full Text Available The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA and the Krill Herd Algorithm (KHA. The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.
Luis, Martino; Ramli, Mohammad Fadzli; Lin, Abdullah
2016-10-01
This study investigates the capacitated planar multi-facility location-allocation problem by considering various capacity constraints. The problem is also known as the capacitated multi-source Weber problem, where the number of facilities to be located is specified and each of which has a capacity constraint. An efficient greedy randomised adaptive search procedure (GRASP) is proposed to deal with the problem. A scheme that applies the furthest distance rule (FDR) and self-adjusted threshold parameters to generate initial facility locations that are situated sparsely within GRASP framework is also presented. The construction of the restricted candidate list (RCL) within GRASP is also guided by applying a concept of restricted regions that prevents new facility locations to be sited too close to the previous selected facility locations. The performance of the proposed GRASP heuristics is tested using benchmark data sets from literature. The computational experiments show that the proposed methods provide encouraging solutions when compared to recently published papers. Some future research avenues on the subject are also briefly highlighted.
An Optimal Algorithm for a Class of Parallel Machines Scheduling Problem
Institute of Scientific and Technical Information of China (English)
常俊林; 邵惠鹤
2004-01-01
This paper considers the parallel machines scheduling problem where jobs are subject to different release times. A constructive heuristic is first proposed to solve the problem in a modest amount of computer time. In general, the quality of the solutions provided by heuristics degrades with the increase of the probiem's scale. Combined the global search ability of genetic algorithm, this paper proposed a hybrid heuristic to improve the quality of solutions further. The computational results show that the hybrid heuristic combines the advantages of heuristic and genetic algorithm effectively and can provide very good solutions to some large problems in a reasonable amount of computer time.
Nafezi, Nima
2013-01-01
In this dissertation, we discussed a type of vehicle routing problem called vehicle routing problem with intermediate facilities with consideration of the impact of adding intermediate facilities to the problem. To study how IFs change the result of the problem, we firstly present a simple model based on clustering algorithm along with finding the shortest route between clusters, implementing Clarke and Wright’s algorithm within each cluster. Then we determine a set of design of experiments w...
Bio-Inspired Meta-Heuristics for Emergency Transportation Problems
Directory of Open Access Journals (Sweden)
Min-Xia Zhang
2014-02-01
Full Text Available Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired meta-heuristics, including genetic algorithms (GA, particle swarm optimization (PSO, ant colony optimization (ACO, etc., for solving emergency transportation problems. We then propose a new hybrid biogeography-based optimization (BBO algorithm, which outperforms some state-of-the-art heuristics on a typical transportation planning problem.
DEFF Research Database (Denmark)
Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan
2001-01-01
The paper suggests the combined use of different computational intelligence (CI) techniques in a hybrid scheme, as an effective approach to medical diagnosis. Getting to know the advantages and disadvantages of each computational intelligence technique in the recent years, the time has come for p...
An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem
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Majid Yousefikhoshbakht
2016-08-01
Full Text Available The traveling salesman problem (TSP is one of the most important NP-hard Problems and probably the most famous and extensively studied problem in the field of combinatorial optimization. In this problem, a salesman is required to visit each of n given nodes once and only once, starting from any node and returning to the original place of departure. This paper presents an efficient evolutionary optimization algorithm developed through combining imperialist competitive algorithm and lin-kernighan algorithm called (MICALK in order to solve the TSP. The MICALK is tested on 44 TSP instances involving from 24 to 1655 nodes from the literature so that 26 best known solutions of the benchmark problem are also found by our algorithm. Furthermore, the performance of MICALK is compared with several metaheuristic algorithms, including GA, BA, IBA, ICA, GSAP, ABO, PSO and BCO on 32 instances from TSPLIB. The results indicate that the MICALK performs well and is quite competitive with the above algorithms.
An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem
Directory of Open Access Journals (Sweden)
Majid Yousefikhoshbakht
2016-08-01
Full Text Available The traveling salesman problem (TSP is one of the most important NP-hard Problems and probably the most famous and extensively studied problem in the field of combinatorial optimization. In this problem, a salesman is required to visit each of n given nodes once and only once, starting from any node and returning to the original place of departure. This paper presents an efficient evolutionary optimization algorithm developed through combining imperialist competitive algorithm and lin-kernighan algorithm called (MICALK in order to solve the TSP. The MICALK is tested on 44 TSP instances involving from 24 to 1655 nodes from the literature so that 26 best known solutions of the benchmark problem are also found by our algorithm. Furthermore, the performance of MICALK is compared with several metaheuristic algorithms, including GA, BA, IBA, ICA, GSAP, ABO, PSO and BCO on 32 instances from TSPLIB. The results indicate that the MICALK performs well and is quite competitive with the above algorithms.
Directory of Open Access Journals (Sweden)
Markowski Marcin
2017-09-01
Full Text Available In recent years elastic optical networks have been perceived as a prospective choice for future optical networks due to better adjustment and utilization of optical resources than is the case with traditional wavelength division multiplexing networks. In the paper we investigate the elastic architecture as the communication network for distributed data centers. We address the problems of optimization of routing and spectrum assignment for large-scale computing systems based on an elastic optical architecture; particularly, we concentrate on anycast user to data center traffic optimization. We assume that computational resources of data centers are limited. For this offline problems we formulate the integer linear programming model and propose a few heuristics, including a meta-heuristic algorithm based on a tabu search method. We report computational results, presenting the quality of approximate solutions and efficiency of the proposed heuristics, and we also analyze and compare some data center allocation scenarios.
H-PoP and H-PoPG: heuristic partitioning algorithms for single individual haplotyping of polyploids.
Xie, Minzhu; Wu, Qiong; Wang, Jianxin; Jiang, Tao
2016-12-15
Some economically important plants including wheat and cotton have more than two copies of each chromosome. With the decreasing cost and increasing read length of next-generation sequencing technologies, reconstructing the multiple haplotypes of a polyploid genome from its sequence reads becomes practical. However, the computational challenge in polyploid haplotyping is much greater than that in diploid haplotyping, and there are few related methods. This article models the polyploid haplotyping problem as an optimal poly-partition problem of the reads, called the Polyploid Balanced Optimal Partition model. For the reads sequenced from a k-ploid genome, the model tries to divide the reads into k groups such that the difference between the reads of the same group is minimized while the difference between the reads of different groups is maximized. When the genotype information is available, the model is extended to the Polyploid Balanced Optimal Partition with Genotype constraint problem. These models are all NP-hard. We propose two heuristic algorithms, H-PoP and H-PoPG, based on dynamic programming and a strategy of limiting the number of intermediate solutions at each iteration, to solve the two models, respectively. Extensive experimental results on simulated and real data show that our algorithms can solve the models effectively, and are much faster and more accurate than the recent state-of-the-art polyploid haplotyping algorithms. The experiments also show that our algorithms can deal with long reads and deep read coverage effectively and accurately. Furthermore, H-PoP might be applied to help determine the ploidy of an organism. https://github.com/MinzhuXie/H-PoPG CONTACT: xieminzhu@hotmail.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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Hadi Mokhtari
2013-01-01
Full Text Available In this paper, the problem of received order scheduling by a manufacturer, with the measure of maximum completion times of orders, has been formulated and then an analytical approach has been devised for its solution. At the beginning of a planning period, the manufacturer receives a number of orders from customers, each of which requires two different stages for processing. In order to minimize the work in process inventories, the no-wait condition between two operations of each order is regarded. Then, the equality of obtained schedules is proved by machine idle time minimization, as objective, with the schedules obtained by maximum completion time minimization. A concept entitled “Order pairing” has been defined and an algorithm for achieving optimal order pairs which is based on symmetric assignment problem has been presented. Using the established order pairs, an upper bound has been developed based on contribution of every order pair out of total machines idle time. Out of different states of improving upper bound, 12 potential situations of order pairs sequencing have been also evaluated and then the upper bound improvement has been proved in each situation, separately. Finally, a heuristic algorithm has been developed based on attained results of pair improvement and a case study in printing industry has been investigated and analyzed to approve its applicability.
THE USE OF GENETIC ALGORITHM IN DIMENSIONING HYBRID AUTONOMOUS SYSTEMS
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RUS T.
2016-03-01
Full Text Available In this paper is presented the working principle of genetic algorithms used to dimension autonomous hybrid systems. It is presented a study case in which is dimensioned and optimized an autonomous hybrid system for a residential house located in Cluj-Napoca. After the autonomous hybrid system optimization is performed, it is achieved a reduction of the total cost of system investment, a reduction of energy produced in excess and a reduction of CO2 emissions.
A hybrid algorithm for unrelated parallel machines scheduling
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Mohsen Shafiei Nikabadi
2016-09-01
Full Text Available In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA using simulated annealing (SA is proposed for scheduling unrelated parallel machines with sequence-dependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines, number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.
A Heuristic Algorithm for 3D Bin-packing Problem%三维装箱问题的启发式算法
Institute of Scientific and Technical Information of China (English)
罗建军; 吴东辉; 罗细飞
2012-01-01
The 3D bin-packing problem is a classic NP-hard combinatorial optimization problem. On the basis of ID and 2D bin-packing problems, this paper develops a heuristic algorithm to overcome the over-reliance on "experience" of the general heuristic algorithm. This algorithm is structurally simple and has high convergence speed as is demonstrated in an experimental study.%三维装箱问题是一类典型的NP-hard组合优化问题.在一维、二维装箱问题基础上,设计了一种启发式算法,借以克服一般启发式算法依赖“经验”的不足,该算法结构简单,实验表明算法收敛速度快.
DOUBLE FOUR-BAR CRANK-SLIDER MECHANISM DYNAMIC BALANCING BY META-HEURISTIC ALGORITHMS
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Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by metaheuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and runtime values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental doublefour-bar crank-slider mechanism was provided for validating the proposed balancing method practically.
Indian Academy of Sciences (India)
OMPRAKASH TEMBHURNE; DEEPTI SHRIMANKAR
2017-07-01
A study of abundance estimation has vital importance in spectral unmixing of hyperspectral image. Recently, various methods have been proposed for spectral unmixing to achieve higher performance using an evolutionary approach. However, these methods are based on unconstrained optimisation problems. Theirperformance was also based on proper tuning parameters. We have proposed a new non-parametric algorithm using teaching-learning-based optimisation technique with an inbuilt constraints maintenance mechanism using the linear mixing model. In this approach, the unmixing problem is transformed into a combinatorial optimisation problem by introducing abundance sum to one constraint and abundance non-negative constraint. A comparative analysis of the proposed algorithm is conducted with other two state-of-the-art algorithms.Experimental results in known and unknown environments with varying signal-to-noise ratio on simulated and real hyper spectral data demonstrate that the proposed method outperforms the other methods.
Heuristic algorithms for solving of the tool routing problem for CNC cutting machines
Chentsov, P. A.; Petunin, A. A.; Sesekin, A. N.; Shipacheva, E. N.; Sholohov, A. E.
2015-11-01
The article is devoted to the problem of minimizing the path of the cutting tool to shape cutting machines began. This problem can be interpreted as a generalized traveling salesman problem. Earlier version of the dynamic programming method to solve this problem was developed. Unfortunately, this method allows to process an amount not exceeding thirty circuits. In this regard, the task of constructing quasi-optimal route becomes relevant. In this paper we propose options for quasi-optimal greedy algorithms. Comparison of the results of exact and approximate algorithms is given.
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Hosseinali Salemi
2016-04-01
Full Text Available Facility location models are observed in many diverse areas such as communication networks, transportation, and distribution systems planning. They play significant role in supply chain and operations management and are one of the main well-known topics in strategic agenda of contemporary manufacturing and service companies accompanied by long-lasting effects. We define a new approach for solving stochastic single source capacitated facility location problem (SSSCFLP. Customers with stochastic demand are assigned to set of capacitated facilities that are selected to serve them. It is demonstrated that problem can be transformed to deterministic Single Source Capacitated Facility Location Problem (SSCFLP for Poisson demand distribution. A hybrid algorithm which combines Lagrangian heuristic with adjusted mixture of Ant colony and Genetic optimization is proposed to find lower and upper bounds for this problem. Computational results of various instances with distinct properties indicate that proposed solving approach is efficient.
Project Scheduling Using Hybrid Genetic Algorithm with Fuzzy Logic Controller in SCM Environment
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
In supply chain management (SCM) environment, we consider a resource-constrained project scheduling problem (rcPSP) model as one of advanced scheduling problems considered by a constraint programming technique. We develop a hybrid genetic algorithm (hGA) with a fuzzy logic controller (FLC) to solve the rcPSP which is the well known NP-hard problem. This new approach is based on the design of genetic operators with FLC through initializing the serial method which is superior for a large rcPSP scale. For solving these rcPSP problems, we first demonstrate that our hGA with FLC (flc-hGA) yields better results than several heuristic procedures presented in the literature. We have revealed a fact that flc-hGA has the evolutionary behaviors of average fitness better than hGA without FLC.
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.
Improved hybrid optimization algorithm for 3D protein structure prediction.
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.
Energy Technology Data Exchange (ETDEWEB)
Perusquia del Cueto, R.; Montes T, J. L.; Ortiz S, J. J.; Castillo M, A., E-mail: raul.perusquia@inin.gob.mx [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)
2011-11-15
At present the techniques of evolution al computation receive an increasing attention in the scientific and technological areas. This situation is due to its enormous potential in the optimization applied to problems of discussed computational complexity. In the nuclear area these techniques are used in diverse problems of combinatory optimization related with designing cores of power reactors. A distinctive characteristic of the evolution al and/or meta-heuristic algorithms is that appeal in each one from their applications to an adjustment function, fitness or of quality. This function allows to discriminate or to evaluate potentials solutions of the problem to solve. The definition of this situation is very important since it allows following the search of the algorithm toward different regions of the search space. In this work the impact that has the election of this function in the quality of the found solution is shown. The optimization technique by ant colonies or Acs (ant colony system) was used applied to the radial design of fuel cells for a boiling water power reactor. The notable results of the Acs allowed to propose the adjustment method of the importance and with this to obtain adjustment functions that guide the search of solutions of collective algorithms efficiently, basic capacity to develop the proposal of emulation of the natural selection and to investigate the possibility that on order of specify goals, to obtain the corresponding decision variables. A variety of re tro-exit (re tro-out) complementary process of feedback (re tro-in) that opens extended application perspectives of be feasible. (Author)
Hybrid ant colony algorithm for traveling salesman problem
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A hybrid approach based on ant colony algorithm for the traveling salesman problem is proposed, which is an improved algorithm characterized by adding a local search mechanism, a cross-removing strategy and candidate lists. Experimental results show that it is competitive in terms of solution quality and computation time.
Directory of Open Access Journals (Sweden)
Narong Wichapa
2018-01-01
Full Text Available Infectious waste disposal remains one of the most serious problems in the medical, social and environmental domains of almost every country. Selection of new suitable locations and finding the optimal set of transport routes for a fleet of vehicles to transport infectious waste material, location routing problem for infectious waste disposal, is one of the major problems in hazardous waste management. Determining locations for infectious waste disposal is a difficult and complex process, because it requires combining both intangible and tangible factors. Additionally, it depends on several criteria and various regulations. This facility location problem for infectious waste disposal is complicated, and it cannot be addressed using any stand-alone technique. Based on a case study, 107 hospitals and 6 candidate municipalities in Upper-Northeastern Thailand, we considered criteria such as infrastructure, geology and social & environmental criteria, evaluating global priority weights using the fuzzy analytical hierarchy process (Fuzzy AHP. After that, a new multi-objective facility location problem model which hybridizes fuzzy AHP and goal programming (GP, namely the HGP model, was tested. Finally, the vehicle routing problem (VRP for a case study was formulated, and it was tested using a hybrid genetic algorithm (HGA which hybridizes the push forward insertion heuristic (PFIH, genetic algorithm (GA and three local searches including 2-opt, insertion-move and interexchange-move. The results show that both the HGP and HGA can lead to select new suitable locations and to find the optimal set of transport routes for vehicles delivering infectious waste material. The novelty of the proposed methodologies, HGP, is the simultaneous combination of relevant factors that are difficult to interpret and cost factors in order to determine new suitable locations, and HGA can be applied to determine the transport routes which provide a minimum number of vehicles
Heuristically Driven Search Methods for Topology Control in Directional Wireless Hybrid Networks
2007-03-01
sovereign options for the defense of the United States of America and its global interests- -to fly and fight in Air, Space, and Cyberspace.” One of...model has been formulated, it can be integrated into a linear solver. Erwin used Xpress -Optimizer, a component of the Xpress -MP suite and a well-known...no global information is used to make decisions. On the other hand, greedy techniques are often acceptable substitutes for approximation algorithms
Meta-heuristic algorithm to solve two-sided assembly line balancing problems
Wirawan, A. D.; Maruf, A.
2016-02-01
Two-sided assembly line is a set of sequential workstations where task operations can be performed at two sides of the line. This type of line is commonly used for the assembly of large-sized products: cars, buses, and trucks. This paper propose a Decoding Algorithm with Teaching-Learning Based Optimization (TLBO), a recently developed nature-inspired search method to solve the two-sided assembly line balancing problem (TALBP). The algorithm aims to minimize the number of mated-workstations for the given cycle time without violating the synchronization constraints. The correlation between the input parameters and the emergence point of objective function value is tested using scenarios generated by design of experiments. A two-sided assembly line operated in an Indonesia's multinational manufacturing company is considered as the object of this paper. The result of the proposed algorithm shows reduction of workstations and indicates that there is negative correlation between the emergence point of objective function value and the size of population used.
Wang, Wenrui; Wu, Yaohua; Wu, Yingying
2016-05-01
E-commerce, as an emerging marketing mode, has attracted more and more attention and gradually changed the way of our life. However, the existing layout of distribution centers can't fulfill the storage and picking demands of e-commerce sufficiently. In this paper, a modified miniload automated storage/retrieval system is designed to fit these new characteristics of e-commerce in logistics. Meanwhile, a matching problem, concerning with the improvement of picking efficiency in new system, is studied in this paper. The problem is how to reduce the travelling distance of totes between aisles and picking stations. A multi-stage heuristic algorithm is proposed based on statement and model of this problem. The main idea of this algorithm is, with some heuristic strategies based on similarity coefficients, minimizing the transportations of items which can not arrive in the destination picking stations just through direct conveyors. The experimental results based on the cases generated by computers show that the average reduced rate of indirect transport times can reach 14.36% with the application of multi-stage heuristic algorithm. For the cases from a real e-commerce distribution center, the order processing time can be reduced from 11.20 h to 10.06 h with the help of the modified system and the proposed algorithm. In summary, this research proposed a modified system and a multi-stage heuristic algorithm that can reduce the travelling distance of totes effectively and improve the whole performance of e-commerce distribution center.
Cost Optimization Using Hybrid Evolutionary Algorithm in Cloud Computing
Directory of Open Access Journals (Sweden)
B. Kavitha
2015-07-01
Full Text Available The main aim of this research is to design the hybrid evolutionary algorithm for minimizing multiple problems of dynamic resource allocation in cloud computing. The resource allocation is one of the big problems in the distributed systems when the client wants to decrease the cost for the resource allocation for their task. In order to assign the resource for the task, the client must consider the monetary cost and computational cost. Allocation of resources by considering those two costs is difficult. To solve this problem in this study, we make the main task of client into many subtasks and we allocate resources for each subtask instead of selecting the single resource for the main task. The allocation of resources for the each subtask is completed through our proposed hybrid optimization algorithm. Here, we hybrid the Binary Particle Swarm Optimization (BPSO and Binary Cuckoo Search algorithm (BCSO by considering monetary cost and computational cost which helps to minimize the cost of the client. Finally, the experimentation is carried out and our proposed hybrid algorithm is compared with BPSO and BCSO algorithms. Also we proved the efficiency of our proposed hybrid optimization algorithm.
Hybrid pre training algorithm of Deep Neural Networks
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Drokin I. S.
2016-01-01
Full Text Available This paper proposes a hybrid algorithm of pre training deep networks, using both marked and unmarked data. The algorithm combines and extends the ideas of Self-Taught learning and pre training of neural networks approaches on the one hand, as well as supervised learning and transfer learning on the other. Thus, the algorithm tries to integrate in itself the advantages of each approach. The article gives some examples of applying of the algorithm, as well as its comparison with the classical approach to pre training of neural networks. These examples show the effectiveness of the proposed algorithm.
A hybrid monkey search algorithm for clustering analysis.
Chen, Xin; Zhou, Yongquan; Luo, Qifang
2014-01-01
Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.
A Hybrid Monkey Search Algorithm for Clustering Analysis
Directory of Open Access Journals (Sweden)
Xin Chen
2014-01-01
Full Text Available Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.
Huseyin Turan, Hasan; Kasap, Nihat; Savran, Huseyin
2014-03-01
Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.
DEFF Research Database (Denmark)
Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan;
2001-01-01
The paper suggests the combined use of different computational intelligence (CI) techniques in a hybrid scheme, as an effective approach to medical diagnosis. Getting to know the advantages and disadvantages of each computational intelligence technique in the recent years, the time has come...... diagnoses. The final result is a short but robust rule based classification scheme, achieving high degree of classification accuracy (exceeding 90% of accuracy for most classes) in a meaningful and user-friendly representation form for the medical expert. The domain of application analyzed through the paper...... is the well-known Pap-Test problem, corresponding to a numerical database, which consists of 450 medical records, 25 diagnostic attributes and 5 different diagnostic classes. Experimental data are divided in two equal parts for the training and testing phase, and 8 mutually dependent rules for diagnosis...
Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms
Directory of Open Access Journals (Sweden)
Monica Alonso
2014-04-01
Full Text Available Transportation electrification has become an important issue in recent decades and the large scale deployment of electric vehicles (EVs has yet to be achieved. The smart coordination of EV demand addresses an improvement in the flexibility of power systems and reduces the costs of power system investment. The uncertainty in EV drivers’ behaviour is one of the main problems to solve to obtain an optimal integration of EVs into power systems. In this paper, an optimisation algorithm to coordinate the charging of EVs has been developed and implemented using a Genetic Algorithm (GA, where thermal line limits, the load on transformers, voltage limits and parking availability patterns are taken into account to establish an optimal load pattern for EV charging-based reliability. This methodology has been applied to an existing residential low-voltage system. The results indicate that a smart charging schedule for EVs leads to a flattening of the load profile, peak load shaving and the prevention of the aging of power system elements.
Directory of Open Access Journals (Sweden)
Hund-Der Yeh
2014-01-01
Full Text Available Simultaneous identification of the source location and release history in aquifers is complicated and time-consuming if the release of groundwater contaminant source varies in time. This paper presents an approach called SATSO-GWT to solve complicated source release problems which contain the unknowns of three location coordinates and several irregular release periods and concentrations. The SATSO-GWT combines with ordinal optimization algorithm (OOA, roulette wheel approach, and a source identification algorithm called SATS-GWT. The SATS-GWT was developed based on simulated annealing, tabu search, and three-dimensional groundwater flow and solute transport model MD2K-GWT. The OOA and roulette wheel method are utilized mainly to reduce the size of feasible solution domain and accelerate the identification of the source information. A hypothetic site with one contaminant source location and two release periods is designed to assess the applicability of the present approach. The results indicate that the performance of SATSO-GWT is superior to that of SATS-GWT. In addition, the present approach works very effectively in dealing with the cases which have different initial guesses of source location and measurement errors in the monitoring points as well as problems with large suspicious areas and several source release periods and concentrations.
Directory of Open Access Journals (Sweden)
Milinkovitch Michel C
2010-07-01
Full Text Available Abstract Background The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s but rather by practical issues such as ergonomics and/or the availability of specific functionalities. Results Here, we present MetaPIGA v2.0, a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood, including a Simulated Annealing algorithm, a classical Genetic Algorithm, and the Metapopulation Genetic Algorithm (metaGA together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data. MetaPIGA v2.0 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for automated selection of substitution models that best fit the data. Heuristics and substitution models are highly customizable through manual batch files and command line processing. However, MetaPIGA v2.0 also offers an extensive graphical user interface for parameters setting, generating and running batch files, following run progress, and manipulating result trees. MetaPIGA v2.0 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and multicore computers. Conclusions The metaGA resolves the major problem inherent to classical Genetic Algorithms by maintaining high inter-population variation even under strong intra-population selection. Implementation of the metaGA together with additional stochastic heuristics into a single software will allow rigorous optimization of each heuristic as well as a meaningful comparison of performances among these
Institute of Scientific and Technical Information of China (English)
Xianbin Wen; Hua Zhang; Jianguang Zhang; Xu Jiao; Lei Wang
2009-01-01
A novel method that hybridizes genetic algorithm (GA) and expectation maximization (EM) algorithm for the classification of syn-thetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive (MAR)model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy.
Davendra, Donald; Zelinka, Ivan; Senkerik, Roman; Jasek, Roman; Bialic-Davendra, Magdalena
2012-11-01
One of the new emerging application strategies for optimization is the hybridization of existing metaheuristics. The research combines the unique paradigms of solution space sampling of SOMA and memory retention capabilities of Scatter Search for the task of capacitated vehicle routing problem. The new hybrid heuristic is tested on the Taillard sets and obtains good results.
Guo, Peng; Cheng, Wenming; Wang, Yi
2015-11-01
This article considers the parallel machine scheduling problem with step-deteriorating jobs and sequence-dependent setup times. The objective is to minimize the total tardiness by determining the allocation and sequence of jobs on identical parallel machines. In this problem, the processing time of each job is a step function dependent upon its starting time. An individual extended time is penalized when the starting time of a job is later than a specific deterioration date. The possibility of deterioration of a job makes the parallel machine scheduling problem more challenging than ordinary ones. A mixed integer programming model for the optimal solution is derived. Due to its NP-hard nature, a hybrid discrete cuckoo search algorithm is proposed to solve this problem. In order to generate a good initial swarm, a modified Biskup-Hermann-Gupta (BHG) heuristic called MBHG is incorporated into the population initialization. Several discrete operators are proposed in the random walk of Lévy flights and the crossover search. Moreover, a local search procedure based on variable neighbourhood descent is integrated into the algorithm as a hybrid strategy in order to improve the quality of elite solutions. Computational experiments are executed on two sets of randomly generated test instances. The results show that the proposed hybrid algorithm can yield better solutions in comparison with the commercial solver CPLEX® with a one hour time limit, the discrete cuckoo search algorithm and the existing variable neighbourhood search algorithm.
一种动态个人最优交通分配的启发式方法%A Heuristic Algorithm for an Optimized Dynamic User Traffic Assignment
Institute of Scientific and Technical Information of China (English)
徐天泽; 黄德镛
2004-01-01
给出了一种基于动态最短路的动态个人最优交通分配的启发式方法.同时提出了与动态个人最优交通分配相关的一些定义.%A heuristic algorithm based on dynamic shortest paths for an optimized dynamic user traffic assignment is suggested. Several definitions related to the optimized dynamic user traffic assignment are also given.
A hybrid algorithm for speckle noise reduction of ultrasound images.
Singh, Karamjeet; Ranade, Sukhjeet Kaur; Singh, Chandan
2017-09-01
Medical images are contaminated by multiplicative speckle noise which significantly reduce the contrast of ultrasound images and creates a negative effect on various image interpretation tasks. In this paper, we proposed a hybrid denoising approach which collaborate the both local and nonlocal information in an efficient manner. The proposed hybrid algorithm consist of three stages in which at first stage the use of local statistics in the form of guided filter is used to reduce the effect of speckle noise initially. Then, an improved speckle reducing bilateral filter (SRBF) is developed to further reduce the speckle noise from the medical images. Finally, to reconstruct the diffused edges we have used the efficient post-processing technique which jointly considered the advantages of both bilateral and nonlocal mean (NLM) filter for the attenuation of speckle noise efficiently. The performance of proposed hybrid algorithm is evaluated on synthetic, simulated and real ultrasound images. The experiments conducted on various test images demonstrate that our proposed hybrid approach outperforms the various traditional speckle reduction approaches included recently proposed NLM and optimized Bayesian-based NLM. The results of various quantitative, qualitative measures and by visual inspection of denoise synthetic and real ultrasound images demonstrate that the proposed hybrid algorithm have strong denoising capability and able to preserve the fine image details such as edge of a lesion better than previously developed methods for speckle noise reduction. The denoising and edge preserving capability of hybrid algorithm is far better than existing traditional and recently proposed speckle reduction (SR) filters. The success of proposed algorithm would help in building the lay foundation for inventing the hybrid algorithms for denoising of ultrasound images. Copyright © 2017 Elsevier B.V. All rights reserved.
Hybrid SOA-SQP algorithm for dynamic economic dispatch with valve-point effects
Energy Technology Data Exchange (ETDEWEB)
Sivasubramani, S.; Swarup, K.S. [Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036 (India)
2010-12-15
This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with valve-point effects. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, SOA is used as a base level search, which can give a good direction to the optimal global region and SQP as a local search to fine tune the solution obtained from SOA. Thus SQP guides SOA to find optimal or near optimal solution in the complex search space. Two test systems i.e., 5 unit with losses and 10 unit without losses, have been taken to validate the efficiency of the proposed hybrid method. Simulation results clearly show that the proposed method outperforms the existing method in terms of solution quality. (author)
2012-09-13
Matrix produced by Wimer’s Algorithm # of Arcs j 1 2 3 . . . q 2 P1(2) P2(2) P3 (2) . . . Pq(2) 3 P1(3) P2(3) P3 (3) Pq(3) Node # u 4 P1(4) P2(4) P3 (4...Pq(4) ... ... . . . ... N P1(N) P2(N) P3 (N) . . . Pq(N) Assign another matrix Z, call each of its elements Zj(u), where each element is 25 Table 5...chooses ”contract” car- riers for long-term partnerships ; thus the need to model schedules is negated. Look at [10] for one detailed model of
Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis
Directory of Open Access Journals (Sweden)
S. Muthurajkumar
2014-05-01
Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.
DEFF Research Database (Denmark)
Ju, Suquan; Clausen, Jens
2004-01-01
The ELDSP problem is a combined lot sizing and sequencing problem. A supplier produces and delivers components of different component types to a consumer in batches. The task is to determine the cycle time, i.e. that time between deliveries, which minimizes the total cost per time unit. This incl......The ELDSP problem is a combined lot sizing and sequencing problem. A supplier produces and delivers components of different component types to a consumer in batches. The task is to determine the cycle time, i.e. that time between deliveries, which minimizes the total cost per time unit....... This includes the determination of the production sequence of the component types within each cycle. We investigate the computational behavior of two published algorithms, a heuristic and an optimal algorithm. With large number of component types, the optimal algorithm has long running times. We devise a hybrid...
A Novel Hybrid Algorithm for Task Graph Scheduling
Directory of Open Access Journals (Sweden)
Vahid Majid Nezhad
2011-03-01
Full Text Available One of the important problems in multiprocessor systems is Task Graph Scheduling. Task Graph Scheduling is an NP-Hard problem. Both learning automata and genetic algorithms are search tools which are used for solving many NP-Hard problems. In this paper a new hybrid method based on Genetic Algorithm and Learning Automata is proposed. The proposed algorithm begins with an initial population of randomly generated chromosomes and after some stages, each chromosome maps to an automaton. Experimental results show that superiority of the proposed algorithm over the current approaches.
An Effective Hybrid Optimization Algorithm for Capacitated Vehicle Routing Problem
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high computational complexity. A hybrid algorithm was developed to solve the problem, in which an artificial immune clonal algorithm (AICA) makes use of the global search ability to search the optimal results and simulated annealing (SA) algorithm employs certain probability to avoid becoming trapped in a local optimum. The results obtained from the computational study show that the proposed algorithm is a feasible and effective method for capacitated vehicle routing problem.
A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling
Institute of Scientific and Technical Information of China (English)
SHU Wanneng; ZHENG Shijue
2006-01-01
In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGSAHA) and applied to solve task scheduling problem in grid computing .It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively.When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole.From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.
An Efficient Hybrid Algorithm for Mining Web Frequent Access Patterns
Institute of Scientific and Technical Information of China (English)
ZHAN Li-qiang; LIU Da-xin
2004-01-01
We propose an efficient hybrid algorithm WDHP in this paper for mining frequent access patterns.WDHP adopts the techniques of DHP to optimize its performance, which is using hash table to filter candidate set and trimming database.Whenever the database is trimmed to a size less than a specified threshold, the algorithm puts the database into main memory by constructing a tree, and finds frequent patterns on the tree.The experiment shows that WDHP outperform algorithm DHP and main memory based algorithm WAP in execution efficiency.
A Novel Hybrid Algorithm for Task Graph Scheduling
Nezhad, Vahid Majid; Efimov, Evgueni
2011-01-01
One of the important problems in multiprocessor systems is Task Graph Scheduling. Task Graph Scheduling is an NP-Hard problem. Both learning automata and genetic algorithms are search tools which are used for solving many NP-Hard problems. In this paper a new hybrid method based on Genetic Algorithm and Learning Automata is proposed. The proposed algorithm begins with an initial population of randomly generated chromosomes and after some stages, each chromosome maps to an automaton. Experimental results show that superiority of the proposed algorithm over the current approaches.
A New Class of Hybrid Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Da-Qing Guo; Yong-Jin Zhao; Hui Xiong; Xiao Li
2007-01-01
A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique (LDIW) and the mutative scale chaos optimization algorithm (MSCOA) are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.
Srinivas, B; Kulick, S N; Doran, Christine; Kulick, Seth
1995-01-01
There are currently two philosophies for building grammars and parsers -- Statistically induced grammars and Wide-coverage grammars. One way to combine the strengths of both approaches is to have a wide-coverage grammar with a heuristic component which is domain independent but whose contribution is tuned to particular domains. In this paper, we discuss a three-stage approach to disambiguation in the context of a lexicalized grammar, using a variety of domain independent heuristic techniques. We present a training algorithm which uses hand-bracketed treebank parses to set the weights of these heuristics. We compare the performance of our grammar against the performance of the IBM statistical grammar, using both untrained and trained weights for the heuristics.
Rogers, David
1991-01-01
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T
2015-02-01
Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.
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.
New Hoopoe Heuristic Optimization
El-Dosuky, Mohammed; EL-Bassiouny, Ahmed; Hamza, Taher; Rashad, Magdy
2012-01-01
Most optimization problems in real life applications are often highly nonlinear. Local optimization algorithms do not give the desired performance. So, only global optimization algorithms should be used to obtain optimal solutions. This paper introduces a new nature-inspired metaheuristic optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will study HH and validate it against some test functions. Investigations show that it is very promising and could be seen as an optimi...
SOLUTION OF THE SATELLITE TRANSFER PROBLEM WITH HYBRID MEMETIC ALGORITHM
Directory of Open Access Journals (Sweden)
A. V. Panteleyev
2014-01-01
Full Text Available This paper presents a hybrid memetic algorithm (MA to solve the problem of finding the optimal program control of nonlinear continuous deterministic systems based on the concept of the meme, which is one of the promising solutions obtained in the course of implementing the procedure for searching the extremes. On the basis of the proposed algorithm the software complex is formed in C#. The solution of satellite transfer problem is presented.
A New Hybrid Watermarking Algorithm for Images in Frequency Domain
Directory of Open Access Journals (Sweden)
AhmadReza Naghsh-Nilchi
2008-03-01
Full Text Available In recent years, digital watermarking has become a popular technique for digital images by hiding secret information which can protect the copyright. The goal of this paper is to develop a hybrid watermarking algorithm. This algorithm used DCT coefficient and DWT coefficient to embedding watermark, and the extracting procedure is blind. The proposed approach is robust to a variety of signal distortions, such as JPEG, image cropping and scaling.
Babaveisi, Vahid; Paydar, Mohammad Mahdi; Safaei, Abdul Sattar
2017-07-01
This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.
Mignon, David; Simonson, Thomas
2016-07-15
Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, "Cost Function Network" method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc.
A new hybrid imperialist competitive algorithm on data clustering
Indian Academy of Sciences (India)
Taher Niknam; Elahe Taherian Fard; Shervin Ehrampoosh; Alireza Rousta
2011-06-01
Clustering is a process for partitioning datasets. This technique is very useful for optimum solution. -means is one of the simplest and the most famous methods that is based on square error criterion. This algorithm depends on initial states and converges to local optima. Some recent researches show that -means algorithm has been successfully applied to combinatorial optimization problems for clustering. In this paper, we purpose a novel algorithm that is based on combining two algorithms of clustering; -means and Modify Imperialist Competitive Algorithm. It is named hybrid K-MICA. In addition, we use a method called modiﬁed expectation maximization (EM) to determine number of clusters. The experimented results show that the new method carries out better results than the ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and -means.
Hybridizing Evolutionary Algorithms with Opportunistic Local Search
DEFF Research Database (Denmark)
Gießen, Christian
2013-01-01
There is empirical evidence that memetic algorithms (MAs) can outperform plain evolutionary algorithms (EAs). Recently the first runtime analyses have been presented proving the aforementioned conjecture rigorously by investigating Variable-Depth Search, VDS for short (Sudholt, 2008). Sudholt...... raised the question if there are problems where VDS performs badly. We answer this question in the affirmative in the following way. We analyze MAs with VDS, which is also known as Kernighan-Lin for the TSP, on an artificial problem and show that MAs with a simple first-improvement local search...... outperform VDS. Moreover, we show that the performance gap is exponential. We analyze the features leading to a failure of VDS and derive a new local search operator, coined Opportunistic Local Search, that can easily overcome regions of the search space where local optima are clustered. The power...
Hybrid Architectures for Evolutionary Computing Algorithms
2008-01-01
Clarkson Univ., at AFRL, summer 2005 (yellow) Genetic Algorithm FPGA Core Burns P1026/MAPLD 200524 GA Core Datapath – Top-level Module • EA parameters and...Statistics are read from I/O ports Burns P1026/MAPLD 200525 GA Core Datapath – Population Module • Array of individuals • Population size register...Permutation generator • Current permutation element register • Current index register Burns P1026/MAPLD 200526 GA Core Datapath – PRNG Module • When
A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments
Institute of Scientific and Technical Information of China (English)
Shengxiang Yang; Renato Tinós
2007-01-01
Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.
Performance Assessment of Hybrid Data Fusion and Tracking Algorithms
DEFF Research Database (Denmark)
Sand, Stephan; Mensing, Christian; Laaraiedh, Mohamed
2009-01-01
This paper presents an overview on the performance of hybrid data fusion and tracking algorithms evaluated in the WHERE consortium. The focus is on three scenarios. For the small scale indoor scenario with ultra wideband (UWB) complementing cellular communication systems, the accuracy can vary in...
Hybrid Probabilistic Logics: Theoretical Aspects, Algorithms and Experiments
Michels, S.
2016-01-01
Steffen Michels Hybrid Probabilistic Logics: Theoretical Aspects, Algorithms and Experiments Probabilistic logics aim at combining the properties of logic, that is they provide a structured way of expressing knowledge and a mechanical way of reasoning about such knowledge, with the ability of prob
A Hybrid Aggressive Space Mapping Algorithm for EM Optimization
DEFF Research Database (Denmark)
Bakr, Mohamed H.; Bandler, John W.; Georgieva, N.;
1999-01-01
We propose a novel hybrid aggressive space-mapping (HASM) optimization algorithm. HASM exploits both the trust-region aggressive space-mapping (TRASM) strategy and direct optimization. Severe differences between the coarse and fine models and nonuniqueness of the parameter extraction procedure ma...
Hybrid Bee Ant Colony Algorithm for Effective Load Balancing And ...
African Journals Online (AJOL)
PROF. OLIVER OSUAGWA
Genetic Algorithm (MO-GA) for dynamic job scheduling ... selection of a data centre. 2.2 Load ... An artificial ant colony, that was capable of .... Scheduling in Hybrid Cloud,” International Journal of Engineering and Technology Volume 2. No.
Hybrid Active Noise Control using Adjoint LMS Algorithms
Energy Technology Data Exchange (ETDEWEB)
Nam, Hyun Do; Hong, Sik Ki [Dankook University (Korea, Republic of)
1998-07-01
A multi-channel hybrid active noise control(MCHANC) is derived by combining hybrid active noise control techniques and adjoint LMS algorithms, and this algorithm is applied to an active noise control system in a three dimensional enclosure. A MCHANC system uses feed forward and feedback filters simultaneously to cancel noises in an enclosure. The adjoint LMs algorithm, in which the error is filtered through an adjoint filter of the secondary channel, is also used to reduce the computational burden of adaptive filters. The overall attenuation performance and convergence characteristics of MCHANC algorithm is better than both multiple-channel feed forward algorithms and multiple-channel feedback algorithms. In a large enclosure, the acoustic reverberation can be very long, which means a very high order feed forward filter must be used to cancel the reverberation noises. Strong reverberation noises are generally narrow band and low frequency, which can be effectively predicted and canceled by a feedback adaptive filters. So lower order feed forward filter taps can be used in MCHANC algorithm which combines advantages of fast convergence and small excess mean square error. In this paper, computer simulations and real time implementations is carried out on a TMS320C31 processor to evaluate the performance of the MCHANC systems. (author). 11 refs., 11 figs., 1 tab.
A hybrid evolutionary algorithm for distribution feeder reconﬁguration
Indian Academy of Sciences (India)
Taher Niknam; Reza Khorshidi; Bahman Bahmani Firouzi
2010-04-01
Distribution feeder reconﬁguration (DFR) is formulated as a multiobjective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance ($\\lambda_2$ norm) between the vectorvalued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems.
A New Hybrid Algorithm for Association Rule Mining
Institute of Scientific and Technical Information of China (English)
ZHANG Min-cong; YAN Cun-liang; ZHU Kai-yu
2007-01-01
HA (hashing array), a new algorithm, for mining frequent itemsets of large database is proposed. It employs a structure hash array, ItemArray ( ) to store the information of database and then uses it instead of database in later iteration. By this improvement, only twice scanning of the whole database is necessary, thereby the computational cost can be reduced significantly. To overcome the performance bottleneck of frequent 2-itemsets mining, a modified algorithm of HA, DHA (direct-addressing hashing and array) is proposed, which combines HA with direct-addressing hashing technique. The new hybrid algorithm, DHA, not only overcomes the performance bottleneck but also inherits the advantages of HA. Extensive simulations are conducted in this paper to evaluate the performance of the proposed new algorithm, and the results prove the new algorithm is more efficient and reasonable.
ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM
Directory of Open Access Journals (Sweden)
D. Amutha Guka
2012-01-01
Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.
Combining ptychographical algorithms with the Hybrid Input-Output (HIO) algorithm.
Konijnenberg, A P; Coene, W M J; Pereira, S F; Urbach, H P
2016-12-01
In this article we combine the well-known Ptychographical Iterative Engine (PIE) with the Hybrid Input-Output (HIO) algorithm. The important insight is that the HIO feedback function should be kept strictly separate from the reconstructed object, which is done by introducing a separate feedback function per probe position. We have also combined HIO with floating PIE (fPIE) and extended PIE (ePIE). Simulations indicate that the combined algorithm performs significantly better in many situations. Although we have limited our research to a combination with HIO, the same insight can be used to combine ptychographical algorithms with any phase retrieval algorithm that uses a feedback function.
Xu, Ye; Wang, Ling; Wang, Shengyao; Liu, Min
2014-09-01
In this article, an effective hybrid immune algorithm (HIA) is presented to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, a decoding method is proposed to transfer a job permutation sequence to a feasible schedule considering both factory dispatching and job sequencing. Secondly, a local search with four search operators is presented based on the characteristics of the problem. Thirdly, a special crossover operator is designed for the DPFSP, and mutation and vaccination operators are also applied within the framework of the HIA to perform an immune search. The influence of parameter setting on the HIA is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on 420 small-sized instances and 720 large-sized instances are provided. The effectiveness of the HIA is demonstrated by comparison with some existing heuristic algorithms and the variable neighbourhood descent methods. New best known solutions are obtained by the HIA for 17 out of 420 small-sized instances and 585 out of 720 large-sized instances.
Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Hayder M. Albeahdili
2015-10-01
Full Text Available The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN. Although stochastic gradient descend (SGD is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA and particle swarm optimization (PSO is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.
The theory of variational hybrid quantum-classical algorithms
McClean, Jarrod R; Babbush, Ryan; Aspuru-Guzik, Alán
2015-01-01
Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as "the quantum variational eigensolver" was developed with the philosophy that even minimal quantum resources could be made useful when used in conjunction with classical routines. In this work we extend the general theory of this algorithm and suggest algorithmic improvements for practical implementations. Specifically, we develop a variational adiabatic ansatz and explore unitary coupled cluster where we establish a connection from second order unitary coupled cluster to universal gate sets through relaxation of exponential splitting. We introduce the concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device. Additionally, we analyze truncation and correlated sampling in Hamiltonian averaging as ways to reduce the cost of this proced...
Hybrid Collision Detection Algorithm based on Image Space
Directory of Open Access Journals (Sweden)
XueLi Shen
2013-07-01
Full Text Available Collision detection is an important application in the field of virtual reality, and efficiently completing collision detection has become the research focus. For the poorly real-time defect of collision detection, this paper has presented an algorithm based on the hybrid collision detection, detecting the potential collision object sets quickly with the mixed bounding volume hierarchy tree, and then using the streaming pattern collision detection algorithm to make an accurate detection. With the above methods, it can achieve the purpose of balancing load of the CPU and GPU and speeding up the detection rate. The experimental results show that compared with the classic Rapid algorithm, this algorithm can effectively improve the efficiency of collision detection.
Institute of Scientific and Technical Information of China (English)
WANG Chong; LI Jun; JING Ning; WANG Jun; CHEN Hao
2011-01-01
Traditionally,heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites.However,the traditional heuristic strategies depend on the concrete tasks,which often affect the result's optimality.Noticing that the historical information of cooperative task planning will impact the latter planning results,we propose a hybrid learning algorithrn for dynamic multi-satellite task planning,which is based on the multi-agent reinforcement learning of policy iteration and the transfer learning.The reinforcement learning strategy of each satellite is described with neural networks.The policy neural network individuals with the best topological structure and weights are found by applying co-evolutionary search iteratively.To avoid the failure of the historical learning caused by the randomly occurring observation requests,a novel approach is proposed to balance the quality and efficiency of the task planning,which converts the historical leaming strategy to the current initial learning strategy by applying the transfer learning algorithm.The simulations and analysis show the feasibility and adaptability of the proposed approach especially for the situation with randomly occurring observation requests.
A hybrid adaptive large neighborhood search algorithm applied to a lot-sizing problem
DEFF Research Database (Denmark)
Muller, Laurent Flindt; Spoorendonk, Simon
This paper presents a hybrid of a general heuristic framework that has been successfully applied to vehicle routing problems and a general purpose MIP solver. The framework uses local search and an adaptive procedure which choses between a set of large neighborhoods to be searched. A mixed integer...
Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm
Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian
2017-03-01
DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Furthermore, the hybrid clustering is applied by using implementation of Parallel K-Means algorithm and DIANA algorithm. The aim of using Hybrid Clustering is to obtain better clusters result. For validating the resulted clusters, to get optimum number of clusters, we use Davies-Bouldin Index (DBI). In this study, the result of implementation of Parallel K-Means clustering is data clustered become 5 clusters with minimal IDB value is 0.8741, and Hybrid Clustering clustered data become 13 sub-clusters with minimal IDB values = 0.8216, 0.6845, 0.3331, 0.1994 and 0.3952. The IDB value of hybrid clustering less than IBD value of Parallel K-Means clustering only that perform at 1ts stage. Its means clustering using Hybrid Clustering have the better result to clustered DNA sequence of HPV than perform parallel K-Means Clustering only.
Multiphase Return Trajectory Optimization Based on Hybrid Algorithm
Directory of Open Access Journals (Sweden)
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.
Institute of Scientific and Technical Information of China (English)
Bo HUANG; Yamin SUN
2005-01-01
This paper proposes and evaluates two improved Petri net (PN)-based hybrid search strategies and their applications to flexible manufacturing system (FMS) scheduling.The algorithms proposed in some previous papers,which combine PN simulation capabilities with A* heuristic search within the PN reachability graph,may not find an optimum solution even with an admissible heuristic function.To remedy the defects an improved heuristic search strategy is proposed,which adopts a different method for selecting the promising markings and reserves the admissibility of the algorithm.To speed up the search process,another algorithm is also proposed which invokes faster termination conditions and still guarantees that the solution found is optimum.The scheduling results are compared through a simple FMS between our algorithms and the previous methods.They are also applied and evaluated in a set of randomly-generated FMSs with such characteristics as multiple resources and alternative routes.
RH+: A Hybrid Localization Algorithm for Wireless Sensor Networks
Basaran, Can; Baydere, Sebnem; Kucuk, Gurhan
Today, localization of nodes in Wireless Sensor Networks (WSNs) is a challenging problem. Especially, it is almost impossible to guarantee that one algorithm giving optimal results for one topology will give optimal results for any other random topology. In this study, we propose a centralized, range- and anchor-based, hybrid algorithm called RH+ that aims to combine the powerful features of two orthogonal techniques: Classical Multi-Dimensional Scaling (CMDS) and Particle Spring Optimization (PSO). As a result, we find that our hybrid approach gives a fast-converging solution which is resilient to range-errors and very robust to topology changes. Across all topologies we studied, the average estimation error is less than 0.5m. when the average node density is 10 and only 2.5% of the nodes are beacons.
A hybrid variational-perturbational nuclear motion algorithm
Fábri, Csaba; Furtenbacher, Tibor; Császár, Attila G.
2014-09-01
A hybrid variational-perturbational nuclear motion algorithm based on the perturbative treatment of the Coriolis coupling terms of the Eckart-Watson kinetic energy operator following a variational treatment of the rest of the operator is described. The algorithm has been implemented in the quantum chemical code DEWE. Performance of the hybrid treatment is assessed by comparing selected numerically exact variational vibration-only and rovibrational energy levels of the C2H4, C2D4, and CH4 molecules with their perturbatively corrected counterparts. For many of the rotational-vibrational states examined, numerical tests reveal excellent agreement between the variational and even the first-order perturbative energy levels, whilst the perturbative approach is able to reduce the computational cost of the matrix-vector product evaluations, needed by the iterative Lanczos eigensolver, by almost an order of magnitude.
Solving Timetabling Problems by Hybridizing Genetic Algorithms and Taboo Search
Rahoual, Malek; Saad, Rachid
2006-01-01
International audience; As demand for Education increases and diversifies, so does the difficulty of designing workable timetables for schools and academic institutions. Besides the intractability of the basic problem, there is an increasing variety of constraints that come into play. In this paper we present a hybrid of two metaheuristics (genetic algorithm and tabu search) to tackle the problem in its most general setting. Promising experimental results are shown.
A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION
Directory of Open Access Journals (Sweden)
Mutasem K. Alsmadi
2014-01-01
Full Text Available Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI. A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA with Fuzzy C-Means algorithm (FCM. In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.
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...
Albuquerque, Fabio; Beier, Paul
2015-01-01
Here we report that prioritizing sites in order of rarity-weighted richness (RWR) is a simple, reliable way to identify sites that represent all species in the fewest number of sites (minimum set problem) or to identify sites that represent the largest number of species within a given number of sites (maximum coverage problem). We compared the number of species represented in sites prioritized by RWR to numbers of species represented in sites prioritized by the Zonation software package for 11 datasets in which the size of individual planning units (sites) ranged from algorithms remain superior for conservation prioritizations that consider compactness and multiple near-optimal solutions in addition to species representation. But because RWR can be implemented easily and quickly in R or a spreadsheet, it is an attractive alternative to integer programming or heuristic algorithms in some conservation prioritization contexts.
A new heuristic for the quadratic assignment problem
Zvi Drezner
2002-01-01
We propose a new heuristic for the solution of the quadratic assignment problem. The heuristic combines ideas from tabu search and genetic algorithms. Run times are very short compared with other heuristic procedures. The heuristic performed very well on a set of test problems.
Efficient Heuristic Variable Ordering of OBDDs
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
An efficient heuristic algorithm for variable ordering of OBDDs, the WDHA (Weight-and-Distance based Heuristic Algorithm), is presented. The algorithm is based on the heuristics implied in the circuit structure graph. To scale the heuristics, pi- weight, node- weight, average- weight and pi- distance in the circuit structure graph are defined. As any of the heuristics is not a panacea for all circuits, several sub-algorithms are proposed to cope with various cases. One is a direct method that uses pi- weight and pi- distance. The others are based on the depth-first-search (DFS) traversal of the circuit structure graph, with each focusing on one of the heuristics. An adaptive order selection strategy is adopted in WDHA. Experimental results show that WDHA is efficient in terms of BDD size and run time, and the dynamic OBDD variable ordering is more attractive if combined with WDHA.
A Hybrid Graph Representation for Recursive Backtracking Algorithms
Abu-Khzam, Faisal N.; Langston, Michael A.; Mouawad, Amer E.; Nolan, Clinton P.
Many exact algorithms for NP-hard graph problems adopt the old Davis-Putman branch-and-reduce paradigm. The performance of these algorithms often suffers from the increasing number of graph modifications, such as deletions, that reduce the problem instance and have to be "taken back" frequently during the search process. The use of efficient data structures is necessary for fast graph modification modules as well as fast take-back procedures. In this paper, we investigate practical implementation-based aspects of exact algorithms by providing a hybrid graph representation that addresses the take-back challenge and combines the advantage of {O}(1) adjacency-queries in adjacency-matrices with the advantage of efficient neighborhood traversal in adjacency-lists.
Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem
Directory of Open Access Journals (Sweden)
Leila Jadidi
2012-06-01
Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.
Multi Population Hybrid Genetic Algorithms for University Course Timetabling
Directory of Open Access Journals (Sweden)
Mehrnaz Shirani LIRI
2012-08-01
Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with at the beginning of each university year. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or almost optimal feasible solutions instead of exact solutions. Genetic algorithms, because of their multidirectional search property, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In the proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainty and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP
Multiple-Goal Heuristic Search
Davidov, D; 10.1613/jair.1940
2011-01-01
This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which estimates the cost and the benefit of exploring a subtree below a search node. We developed two methods for online learning of the marginal-utility heuristic. One is based on local similarity of the partial marginal utility of sibling nodes, and the other generalizes marginal-utility over the state feature space. We apply our adaptive and non-adaptive multiple-goal search algorithms to several problems, including focused crawling, and show their superiority over existing methods.
Development of hybrid artificial intelligent based handover decision algorithm
Directory of Open Access Journals (Sweden)
A.M. Aibinu
2017-04-01
Full Text Available The possibility of seamless handover remains a mirage despite the plethora of existing handover algorithms. The underlying factor responsible for this has been traced to the Handover decision module in the Handover process. Hence, in this paper, the development of novel hybrid artificial intelligent handover decision algorithm has been developed. The developed model is made up of hybrid of Artificial Neural Network (ANN based prediction model and Fuzzy Logic. On accessing the network, the Received Signal Strength (RSS was acquired over a period of time to form a time series data. The data was then fed to the newly proposed k-step ahead ANN-based RSS prediction system for estimation of prediction model coefficients. The synaptic weights and adaptive coefficients of the trained ANN was then used to compute the k-step ahead ANN based RSS prediction model coefficients. The predicted RSS value was later codified as Fuzzy sets and in conjunction with other measured network parameters were fed into the Fuzzy logic controller in order to finalize handover decision process. The performance of the newly developed k-step ahead ANN based RSS prediction algorithm was evaluated using simulated and real data acquired from available mobile communication networks. Results obtained in both cases shows that the proposed algorithm is capable of predicting ahead the RSS value to about ±0.0002 dB. Also, the cascaded effect of the complete handover decision module was also evaluated. Results obtained show that the newly proposed hybrid approach was able to reduce ping-pong effect associated with other handover techniques.
Institute of Scientific and Technical Information of China (English)
张国梁; 侯晓鹏; 苗虎; 安源; 周玉成; 姚永和
2014-01-01
Algorithms which were available in most literatures for whole layout of large scale rectangular parts gave solutions that resulted in frequent change of saw line and therefore dropped sawing velocity down. To solve this problem, a grouping and dimension-reducing heuristic rule which took areas of rectangular parts as priority was put forward in this paper. According to this rule,no more than three kinds of rectangular parts were considered in each layout calculation. Corresponding mathematical model was set up. Hybrid punishment function that was the combination of interior point method and exterior point one was applied to deal with constrains. Genetic algorithm ( GA) was adopted to search global optimal solution for layout. It was proved by example that the algorithm used in this paper could provide layout solution which exactly fulfilled guillotine cutting requirement and had saw line in order and therefore was useful to increase of sawing efficiency.
A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means
Institute of Scientific and Technical Information of China (English)
TRAN Dang Cong; WU Zhijian; WANG Zelin; DENG Changshou
2015-01-01
To improve the performance of K-means clustering algorithm, this paper presents a new hybrid ap-proach of Enhanced artificial bee colony algorithm and K-means (EABCK). In EABCK, the original artificial bee colony algorithm (called ABC) is enhanced by a new mu-tation operation and guided by the global best solution (called EABC). Then, the best solution is updated by K-means in each iteration for data clustering. In the experi-ments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCK on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCK out-perform other comparative ABC variants and data clus-tering algorithms, respectively.
Directory of Open Access Journals (Sweden)
Johan Soewanda
2007-01-01
Full Text Available This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than the company's, but the same as Ant Colony, Genetic-Tabu, and Hybrid Genetic. In addition, Robust Hybrid Genetic Algorithm required less computational time than Hybrid Genetic Algorithm
Effective pathfinding for four-wheeled robot based on combining Theta* and hybrid A* algorithms
Directory of Open Access Journals (Sweden)
Віталій Геннадійович Михалько
2016-07-01
Full Text Available Effective pathfinding algorithm based on Theta* and Hybrid A* algorithms was developed for four-wheeled robot. Pseudocode for algorithm was showed and explained. Algorithm and simulator for four-wheeled robot were implemented using Java programming language. Algorithm was tested on U-obstacles, complex maps and for parking problem
Algorithm of Topic-oriented Crawling Based on Heuristic Search%一种启发式主题爬行算法
Institute of Scientific and Technical Information of China (English)
刘欣宇; 唐学文; 邓一贵
2012-01-01
To solve the traditional topic crawler's drawback in terms of precision and efficiency as well as improve the precise ratio and recall ratio of general search engine results, a new kind of topic-oriented crawling algorithm is put forward, according to the current features of the topic - oriented crawling methods. The methods based on the link analysis and the content analysis of the topic methods is combined through page radiation space to combine, with heuristic algorithm embed. The experiment result shows that this algorithm is more efficient than the u-, sual algorithms.%为克服传统主题爬行器在爬行速度和主题预测精度上的不足,提高爬行器的查准率和查全率,根据当前常用主题爬行策略的特点,通过页面辐射空间的引入将主题策略中基于链接分析和基于内容分析的方法相结合,并嵌入启发式算法,提出一种基于启发式的主题爬行算法.实验结果表明,该算法较常用爬行算法有较好的爬行效率.
柔性作业车间调度分析及其启发式算法%Flexible job-shop scheduling analysis and its heuristic algorithm
Institute of Scientific and Technical Information of China (English)
苏子林; 苑金梁; 陈炜; 邱景炜
2012-01-01
针对多目标柔性作业车间调度问题,基于甘特图和搭积木经验进行了分析,提出了一种组合优先规则和基于此优先规则的启发式算法.组合优先规则面向完工时间、关键机床负荷和总负荷三个指标,改变规则中各数据项的比例可调整三个指标所占的比例.算法采用随机方式调整三个指标的比例,并微调最优解对应的比例,能随机产生多个高质量调度解.对比测试表明,算法求解质量更高,运行速度快,稳定,可直接用于在其他调度算法中产生初始解,或者用于动态调度.%The multi-objective flexible job-shop scheduling problem is analyzed based on Gantt graph and experience from building block, a composite priority rule and heuristic algorithm based on this priority rule are presented. This composite priority rule is for three scheduling targets including makespan, critical machine workload and total workload, changing the ratio of data items in the rule can adjust the ratio of the three scheduling targets. This heuristic algorithm randomly adjusts the ratio of this three scheduling targets, and slightly adjusts the ratio corresponding to the best solution, can randomly generate many excellent scheduling solutions. The algorithm' s comparison and test show that the result of this algorithm is more excellent. The algorithm runs rapidly and steadily, and can directly be used in generating initial solution in other scheduling algorithms or used in dynamic scheduling.
Heuristic Chinese sentence compression algorithm based on hot word%基于词语热度的启发式中文句子压缩算法
Institute of Scientific and Technical Information of China (English)
韩静; 张东站
2014-01-01
Since the parallel sentence/compression corpora which most of the traditional methods based on are not easy to obtain, a linguistically-motivated heuristics Chinese sentence compression algorithm is proposed after studying traditional methods. By analyzing the human-produced compression and linguistic knowledge, two sets of rules are proposed, one is in word layer and the other is in clause layer. Two sets of rules based on the parse tree and the words dependence are used to compress sentence, and enhance the algorithm by hot word in order to keep the algorithm flexibility and accuracy. In the last step the compression result is cleaned and repaired. Human-produced compression, rule-only algorithm and hot word enhanced algorithm are compared then the results are evaluated in compression rate, grammaticality, informative-ness and heat. The experimental results show that heuristic Chinese sentence compression algorithm based on hot word can improve the heat of compression results without much loss in compression rate, grammaticality and informativeness.%传统的句子压缩方法多基于难以获得的“原句-压缩句”对齐语料库，因此提出了不依赖于对齐语料库的中文句子压缩算法。通过研究人工压缩结果并结合语言学知识，提出了词语层面和分句层面的两组压缩规则。算法在原句句法分析树和词语间依赖关系的基础上，使用两组规则进行压缩，同时为了保证压缩算法具有更强的适应性和准确性，引入词语的热度加强了压缩算法，最后通过句子整理和语法修复得到最终的压缩句。对比了人工压缩、只使用规则压缩和引入词语热度压缩三种压缩方法。实验结果表明，基于热度的启发式中文句子压缩算法可以在压缩比、语法性、信息量都损失较少的情况下，提高压缩句的热度。
Evolving Quantum Oracles with Hybrid Quantum-inspired Evolutionary Algorithm
Ding, S; Yang, Q; Ding, Shengchao; Jin, Zhi; Yang, Qing
2006-01-01
Quantum oracles play key roles in the studies of quantum computation and quantum information. But implementing quantum oracles efficiently with universal quantum gates is a hard work. Motivated by genetic programming, this paper proposes a novel approach to evolve quantum oracles with a hybrid quantum-inspired evolutionary algorithm. The approach codes quantum circuits with numerical values and combines the cost and correctness of quantum circuits into the fitness function. To speed up the calculation of matrix multiplication in the evaluation of individuals, a fast algorithm of matrix multiplication with Kronecker product is also presented. The experiments show the validity and the effects of some parameters of the presented approach. And some characteristics of the novel approach are discussed too.
Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness
Directory of Open Access Journals (Sweden)
Animesh Biswas
2014-01-01
Full Text Available This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people.
Healing Temperature of Hybrid Structures Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
赵中伟; 陈志华; 刘红波
2016-01-01
The healing temperature of suspen-dome with stacked arches(SDSA)and arch-supported single-layer lattice shell structures was investigated based on the genetic algorithm. The temperature field of arch under solar radiation was derived by FLUENT to investigate the influence of solar radiation on the determination of the healing temperature. Moreover, a multi-scale model was established to apply the complex temperature field under solar radiation. The change in the mechanical response of these two kinds of structures with the healing temperature was discussed. It can be concluded that solar radiation has great influence on the healing temperature, and the genetic algorithm can be effectively used in the optimization of the healing temperature for hybrid structures.
Institute of Scientific and Technical Information of China (English)
周宁; 谢博鋆; 王涛
2011-01-01
The capability of noise cancelling in decision tree is the critical factor in heuristic algorithms design. The comparison between ID3 and DoI, the two heuristic algorithms for the capacity of resisting noise was investigated. The investigation was aiming at giving some experimentally comparative advantages on the robustness for the two heuristics.%决策树抵抗噪声的能力是启发式算法设计中的关键因素.对ID3和DoI 2种启发式算法在抵抗噪声的能力上做了对比研究.通过实验比较得出由DoI算法构建出的决策树在抵抗噪声的干扰方面与根据ID3算法构建出的决策树相比具有一定优势.
Lightfoot, J.; Wyrowski, F.; Muders, D.; Boone, F.; Davis, L.; Shepherd, D.; Wilson, C.
2006-07-01
The ALMA (Atacama Large Millimeter Array) Pipeline Heuristics system is being developed to automatically reduce data taken with the standard observing modes. The goal is to make ALMA user-friendly to astronomers who are not experts in radio interferometry. The Pipeline Heuristics system must capture the expert knowledge required to provide data products that can be used without further processing. Observing modes to be processed by the system include single field interferometry, mosaics and single dish `on-the-fly' maps, and combinations of these modes. The data will be produced by the main ALMA array, the ALMA Compact Array (ACA) and single dish antennas. The Pipeline Heuristics system is being developed as a set of Python scripts. For interferometry these use as data processing engines the CASA/AIPS++ libraries and their bindings as CORBA objects within the ALMA Common Software (ACS). Initial development has used VLA and Plateau de Bure data sets to build and test a heuristic script capable of reducing single field data. In this paper we describe the reduction datapath and the algorithms used at each stage. Test results are presented. The path for future development is outlined.
VLSI Implementation of Hybrid Algorithm Architecture for Speech Enhancement
Directory of Open Access Journals (Sweden)
Jigar Shah
2012-07-01
Full Text Available The speech enhancement techniques are required to improve the speech signal quality without causing any offshoot in many applications. Recently the growing use of cellular and mobile phones, hands free systems, VoIP phones, voice messaging service, call service centers etc. require efficient real time speech enhancement and detection strategies to make them superior over conventional speech communication systems. The speech enhancement algorithms are required to deal with additive noise and convolutive distortion that occur in any wireless communication system. Also the single channel (one microphone signal is available in real environments. Hence a single channel hybrid algorithm is used which combines minimum mean square error-log spectral amplitude (MMSE-LSA algorithm for additive noise removal and the relative spectral amplitude (RASTA algorithm for reverberation cancellation. The real time and embedded implementation on directly available DSP platforms like TMS320C6713 shows some defects. Hence the VLSI implementation using semi-custom (e.g. FPGA or full-custom approach is required. One such architecture is proposed in this paper.
Ma, Li; Fan, Suohai
2017-03-14
The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.
Hybrid genetic algorithm for minimizing non productive machining ...
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A Bi-criteria M-Machine SDST Flow Shop Scheduling using Modified Heuristic Genetic ... He has more than 35 research papers in international/national journals and ... supply chain management, inventory management, machine learning, etc.
高效的移动sink路由问题的启发式算法%Efficient heuristic algorithm for the mobile sink routing problem
Institute of Scientific and Technical Information of China (English)
袁远; 彭宇行; 李姗姗; 唐文胜
2011-01-01
In large-scale monitoring region, randomly deployed wireless sensor networks may not be fully connected with high probability. Using mobile sink for data collection is one of the feasible solutions. Mobile sink shortest routing problem can be regarded as a special case of TSP with neighborhoods (TSPN) problem, since the neighborhoods are the radio ranges of the sensor nodes, which can be modeled as possibly overlapped disks with diverse sizes. This kind of TSPN problem has no polynomial algorithms so far. To handle it, a novel approximation algorithm was proposed, which first forms a "racetrack" by utilizing the non-intersecting loop property of TSP routes, and then through the inner lane heuristic, the bend heuristic and the shortcut searching, the algorithm can find an approximation solution within O(n2) computation time. The formal proofs and the large-scale simulations all verify that our algorithm can achieve a good approximation ratio and can be more efficient than the related algorithms.%移动sink最短路由问题可以看作是带邻近区域的旅行商问题(TSPN)的一个特例,其邻近区域为随机部署的传感器节点的无线通信范围,可建模成大小各异并且存在重叠的圆盘.由于目前还不存在多项式时间算法来解决该种TSPN问题,提出了一种新颖的启发式算法.它利用TSP路径为不自交环路的特性构造一条赛道,通过内圈启发式、弯道启发式以及捷径搜索在O(n2)时间复杂度内找出赛道内的近似最短路径.形式化证明和大规模模拟实验都验证了该算法较同类算法能够更高效地找出较优的近似解.
A study of image reconstruction algorithms for hybrid intensity interferometers
Crabtree, Peter N.; Murray-Krezan, Jeremy; Picard, Richard H.
2011-09-01
Phase retrieval is explored for image reconstruction using outputs from both a simulated intensity interferometer (II) and a hybrid system that combines the II outputs with partially resolved imagery from a traditional imaging telescope. Partially resolved imagery provides an additional constraint for the iterative phase retrieval process, as well as an improved starting point. The benefits of this additional a priori information are explored and include lower residual phase error for SNR values above 0.01, increased sensitivity, and improved image quality. Results are also presented for image reconstruction from II measurements alone, via current state-of-the-art phase retrieval techniques. These results are based on the standard hybrid input-output (HIO) algorithm, as well as a recent enhancement to HIO that optimizes step lengths in addition to step directions. The additional step length optimization yields a reduction in residual phase error, but only for SNR values greater than about 10. Image quality for all algorithms studied is quite good for SNR>=10, but it should be noted that the studied phase-recovery techniques yield useful information even for SNRs that are much lower.
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Haisheng Song
2013-01-01
Full Text Available The back propagation neural network (BPNN algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.
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Lu-Chuan Ceng
2014-01-01
Full Text Available We present a hybrid iterative algorithm for finding a common element of the set of solutions of a finite family of generalized mixed equilibrium problems, the set of solutions of a finite family of variational inequalities for inverse strong monotone mappings, the set of fixed points of an infinite family of nonexpansive mappings, and the set of solutions of a variational inclusion in a real Hilbert space. Furthermore, we prove that the proposed hybrid iterative algorithm has strong convergence under some mild conditions imposed on algorithm parameters. Here, our hybrid algorithm is based on Korpelevič’s extragradient method, hybrid steepest-descent method, and viscosity approximation method.
Institute of Scientific and Technical Information of China (English)
李颖浩; 郭瑞鹏
2012-01-01
电力系统机组组合问题是一个高维、离散、非线性的工程优化问题。提出了一种基于Benders分解的启发式算法。该算法一方面充分利用研究时段负荷曲线的特征，将问题进行解耦，减小被研究问题的规模。另一方面，利用Benders分解算法在混合整数规划中的有效性，提高了解决问题的效率。算例表明该方法效率高、结果稳定，有较好的实用价值。%Unit commitment （UC） of power system is a high dimensional, nonlinear and mixed-integer engineering optimization problem. To solve this problem a generalized Benders decomposition based heuristic algorithm is proposed. On the one hand the characteristics of load curve in the time-interval being researched are fully utilized to decouple the problem and to decrease the scale of the problem, on the other hand using the effectiveness of Benders algorithm is solving mixed-integer programming problem the efficiency of solving the problem is improved. Results of calculation example show that the proposed algorithm is efficient and practicable.
Wang, Yan; Huang, Song; Ji, Zhicheng
2017-07-01
This paper presents a hybrid particle swarm optimization and gravitational search algorithm based on hybrid mutation strategy (HGSAPSO-M) to optimize economic dispatch (ED) including distributed generations (DGs) considering market-based energy pricing. A daily ED model was formulated and a hybrid mutation strategy was adopted in HGSAPSO-M. The hybrid mutation strategy includes two mutation operators, chaotic mutation, Gaussian mutation. The proposed algorithm was tested on IEEE-33 bus and results show that the approach is effective for this problem.
Institute of Scientific and Technical Information of China (English)
潘立军; 符卓
2012-01-01
针对已有求解带硬时间窗车辆路径问题时插入启发式算法结构复杂、参数多、求解效率不高的缺点,提出了求解该问题的时差插入启发式算法.该算法引入时差的概念,将时差作为启发规则的评价指标.相比已有求解该问题的经典启发式算法,该算法有参数个数少、算法结构简单等特点.应用标准测试算例测试表明,所提算法的求解质量优于Solomon的插入启发式算法和Potvin的平行插入启发式算法.%The Vehicle Routing Problem with Hard Time Window ( VRPHTW) is a kind of Vehicle Routing Problem (VRP) which has a lot of applications. The existing heuristics of this problem hold shortcomings such as complex structure, lots of parameters and low efficiency. Therefore, Time Difference Insertion Heuristics (TDIH) for VRPHTW was proposed. The algorithm introduced the concept of Time Difference ( TD), and took TD as a heuristic rule evaluation indicator. Compared to other classic heuristics, the algorithm was characterized with fewer parameters and simpler structure. The computational results on the benchmark problems show that the algorithm is better than the Solomon's insertion heuristics and Potvin's parallel insertion heuristics.
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A. A. Almazroi
2011-01-01
Full Text Available Problem statement: String matching algorithm had been an essential means for searching biological sequence database. With the constant expansion in scientific data such as DNA and Protein; the development of enhanced algorithms have even become more critical as the major concern had always been how to raise the performances of these search algorithms to meet challenges of scientific information. Approach: Therefore a new hybrid algorithm comprising Berry Ravindran (BR and Alpha Skip Search (ASS is presented. The concept is based on BR shift function and combines with ASS to ensure improved performance. Results: The results obtained in percentages from the proposed hybrid algorithm displayed superior results in terms of number of attempts and number of character comparisons than the original algorithms when various types of data namely DNA, Protein and English text are applied to appraise the hybrid performances. The enhancement of the proposed hybrid algorithm performs better at 71%, 60% and 63% when compared to Berry-Ravindran in DNA, Protein and English text correspondingly. Moreover the rate of enhancement over Alpha Skip Search algorithm in DNA, Protein and English text are 48%, 28% and 36% respectively. Conclusion: The new proposed hybrid algorithm is relevant for searching biological science sequence database and also other string search systems.
基于优势关系的启发式属性约简算法%Heuristic Algorithm for Attribute Reduction Based on Dominance Relation
Institute of Scientific and Technical Information of China (English)
廖帆; 膝书华; 邵世雷
2011-01-01
根据优势原理,提出一种具有明确粗糙集理论含义的指标——优势度,用于度量序目标信息系统的协调程度.在证明优势度粒化单调性的基础上,给出属性集重要性度量函数,提出一种基于优势度的序目标信息系统启发式约简算法.该算法与经典粗糙集理论约简有相同的理论基础,易于理解.应用结果表明,该算法适用于优势关系下目标信息系统的知识发现.%A new uncertainty measure, such as dominance degree is proposed in ordered objective information systems based on dominance principle, and an explicit theoretical meaning of rough set is given to the dominance degree which can be used to measure the inconsistence of objective information system. The granulation monotonicity of dominance degree is proved, based on which a new measure of attribution importance is designed. An heuristic reduct algorithm in objective information system is provided based on dominance relation. An example illustrates the validity of this algorithm, and results show that the algorithm has the same theoretical foundation with classical reduct algorithm in rough set theory, and it is easily understood. The algorithm provides an important theoretical basis for knowledge discovery in ordered objective information systems.
A fast hybrid algorithm for exoplanetary transit searches
Cameron, A C; Street, R A; Lister, T A; West, R G; Wilson, D M; Pont, F; Christian, D J; Clarkson, W I; Enoch, B; Evans, A; Fitzsimmons, A; Haswell, C A; Hellier, C; Hodgkin, S T; Horne, K; Irwin, J; Kane, S R; Keenan, F P; Norton, A J; Parley, N R; Osborne, J; Ryans, R; Skillen, I; Wheatley, P J
2006-01-01
We present a fast and efficient hybrid algorithm for selecting exoplanetary candidates from wide-field transit surveys. Our method is based on the widely-used SysRem and Box Least-Squares (BLS) algorithms. Patterns of systematic error that are common to all stars on the frame are mapped and eliminated using the SysRem algorithm. The remaining systematic errors caused by spatially localised flat-fielding and other errors are quantified using a boxcar-smoothing method. We show that the dimensions of the search-parameter space can be reduced greatly by carrying out an initial BLS search on a coarse grid of reduced dimensions, followed by Newton-Raphson refinement of the transit parameters in the vicinity of the most significant solutions. We illustrate the method's operation by applying it to data from one field of the SuperWASP survey, comprising 2300 observations of 7840 stars brighter than V=13.0. We identify 11 likely transit candidates. We reject stars that exhibit significant ellipsoidal variations indicat...
Evaluation of hybrids algorithms for mass detection in digitalized mammograms
Energy Technology Data Exchange (ETDEWEB)
Cordero, Jose; Garzon Reyes, Johnson, E-mail: josecorderog@hotmail.com [Grupo de Optica y Espectroscopia GOE, Centro de Ciencia Basica, Universidad Pontifica Bolivariana de Medellin (Colombia)
2011-01-01
The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.
Validation and incremental value of the hybrid algorithm for CTO PCI.
Pershad, Ashish; Eddin, Moneer; Girotra, Sudhakar; Cotugno, Richard; Daniels, David; Lombardi, William
2014-10-01
To evaluate the outcomes and benefits of using the hybrid algorithm for chronic total occlusion (CTO) percutaneous coronary intervention (PCI). The hybrid algorithm harmonizes antegrade and retrograde techniques for performing CTO PCI. It has the potential to increase success rates and improve efficiency for CTO PCI. No previous data have analyzed the impact of this algorithm on CTO PCI success rates and procedural efficiency. Retrospective analysis of contemporary CTO PCI performed at two high-volume centers with adoption of the hybrid technique was compared to previously published CTO outcomes in a well matched group of patients and lesion subsets. After adoption of the hybrid algorithm, technical success was significantly higher in the post hybrid algorithm group 189/198 (95.4%) vs the pre-algorithm group 367/462 (79.4%) (P CTO PCI. © 2014 Wiley Periodicals, Inc.
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Utkarsh Gautam
2015-05-01
Full Text Available Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.220-225, DOI: http://dx.doi.org/10.14429/dsj.65.7855
Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm
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Jeng-Fung Chen
2015-06-01
Full Text Available Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN has been a challenging task in the supervised learning area. Particle swarm optimization (PSO is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. On the other hand, Cuckoo Search (CS algorithm has been proven to have a good ability for finding the global optimum; however, it has a slow convergence rate. In this study, a hybrid algorithm based on PSO and CS is proposed to make use of the advantages of both PSO and CS algorithms. The proposed hybrid algorithm is employed as a new training method for feedforward neural networks (FNNs. To investigate the performance of the proposed algorithm, two benchmark problems are used and the results are compared with those obtained from FNNs trained by original PSO and CS algorithms. The experimental results show that the proposed hybrid algorithm outperforms both PSO and CS in training FNNs.
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Hyo Seon Park
2014-01-01
Full Text Available Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.
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Dania Tamayo-Vera
2016-04-01
Full Text Available Spanish abstract Los problemas lineales con restricciones de equilibrio son un caso particular de los modelos de optimización con restricciones de equilibrio. Debido a la complejidad que presentan, la condición de equilibrio se sustituye por condiciones necesarias obteniéndose un problema con restricciones de complementariedad (MPCC. La estructura del conjunto de soluciones factibles del MPCC obtenido es compleja ya que es la unión de poliedros. Resolver todos los problemas correspondientes a minimizar la función objetivo sobre cada uno de estos poliedros es computacionalmente costoso. El presente trabajo utiliza un enfoque heurístico para dar solución al MPCC, adaptando los algoritmos de Búsqueda Local y Recocido Simulado. Este trabajo presenta un conjunto de funciones de prueba y los resultados computacionales más significativos obtenidos. English abstract Linear equilibrium constrained programming is a special class of optimization models with equilibrium constraints. Because of the complexity of the equilibrium condition it is replaced by necessary conditions, which leads to a complementarity constrained problem (MPCC. The set of feasible solutions in a MPCC is structured as a union of polyhedrons. Solving the MPCC problem would require the minimization of the objective function on each of these polyhedrons. The computation cost of this approach is unfeasible, thus, this work presents a new approach where heuristic algorithms such as Hill Climbing and Simulated Annealing are used to search for good solutions on the polyhedrons space. A new benchmark for linear equilibrium constrained optimization is introduced. The computational results achieved by the proposed heuristics on the new benchmark are presented.
Hybrid Monte Carlo algorithm with fat link fermion actions
Kamleh, Waseem; Williams, Anthony G; 10.1103/PhysRevD.70.014502
2004-01-01
The use of APE smearing or other blocking techniques in lattice fermion actions can provide many advantages. There are many variants of these fat link actions in lattice QCD currently, such as flat link irrelevant clover (FLIC) fermions. The FLIC fermion formalism makes use of the APE blocking technique in combination with a projection of the blocked links back into the special unitary group. This reunitarization is often performed using an iterative maximization of a gauge invariant measure. This technique is not differentiable with respect to the gauge field and thus prevents the use of standard Hybrid Monte Carlo simulation algorithms. The use of an alternative projection technique circumvents this difficulty and allows the simulation of dynamical fat link fermions with standard HMC and its variants. The necessary equations of motion for FLIC fermions are derived, and some initial simulation results are presented. The technique is more general however, and is straightforwardly applicable to other smearing ...
A hybrid algorithm for parallel molecular dynamics simulations
Mangiardi, Chris M
2016-01-01
This article describes an algorithm for hybrid parallelization and SIMD vectorization of molecular dynamics simulations with short-ranged forces. The parallelization method combines domain decomposition with a thread-based parallelization approach. The goal of the work is to enable efficient simulations of very large (tens of millions of atoms) and inhomogeneous systems on many-core processors with hundreds or thousands of cores and SIMD units with large vector sizes. In order to test the efficiency of the method, simulations of a variety of configurations with up to 74 million atoms have been performed. Results are shown that were obtained on multi-core systems with AVX and AVX-2 processors as well as Xeon-Phi co-processors.
A NEW HYBRID ALGORITHM FOR BUSINESS INTELLIGENCE RECOMMENDER SYSTEM
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P.Prabhu
2014-03-01
Full Text Available Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful and useful information. Recommender system is one of business intelligence system that is used to obtain knowledge to the active user for better decision making. Recommender systems apply data mining techniques to the problem of making personalized recommendations for information. Due to the growth in the number of information and the users in recent years offers challenges in recommender systems. Collaborative, content, demographic and knowledge-based are four different types of recommendations systems. In this paper, a new hybrid algorithm is proposed for recommender system which combines knowledge based, profile of the users and most frequent item mining technique to obtain intelligence.
A study of speech emotion recognition based on hybrid algorithm
Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei
2011-10-01
To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.
A hybrid algorithm for parallel molecular dynamics simulations
Mangiardi, Chris M.; Meyer, R.
2017-10-01
This article describes algorithms for the hybrid parallelization and SIMD vectorization of molecular dynamics simulations with short-range forces. The parallelization method combines domain decomposition with a thread-based parallelization approach. The goal of the work is to enable efficient simulations of very large (tens of millions of atoms) and inhomogeneous systems on many-core processors with hundreds or thousands of cores and SIMD units with large vector sizes. In order to test the efficiency of the method, simulations of a variety of configurations with up to 74 million atoms have been performed. Results are shown that were obtained on multi-core systems with Sandy Bridge and Haswell processors as well as systems with Xeon Phi many-core processors.
Memetic firefly algorithm for combinatorial optimization
Fister, Iztok; Fister, Iztok; Brest, Janez
2012-01-01
Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.
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M. Omidvari
2015-09-01
Full Text Available Introduction: Occupational accidents are of the main issues in industries. It is necessary to identify the main root causes of accidents for their control. Several models have been proposed for determining the accidents root causes. FTA is one of the most widely used models which could graphically establish the root causes of accidents. The non-linear function is one of the main challenges in FTA compliance and in order to obtain the exact number, the meta-heuristic algorithms can be used. Material and Method: The present research was done in power plant industries in construction phase. In this study, a pattern for the analysis of human error in work-related accidents was provided by combination of neural network algorithms and FTA analytical model. Finally, using this pattern, the potential rate of all causes was determined. Result: The results showed that training, age, and non-compliance with safety principals in the workplace were the most important factors influencing human error in the occupational accident. Conclusion: According to the obtained results, it can be concluded that human errors can be greatly reduced by training, right choice of workers with regard to the type of occupations, and provision of appropriate safety conditions in the work place.
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ASHWIN MISHRA,
2011-01-01
Full Text Available In this study singularity analysis of the six degree of freedom (DOF Stewart Platform using the various heuristic methods in a specified design configuration has been carried out .The Jacobian matrix of the Stewart platform is obtained and the absolute value of the determinant of the Jacobian is taken as the objective function, and the least value of this objective function is fished in the reachable workspace of the Stewart platform so as to find the singular configurations. The singular configurations of the platform depend on the value of this objective function under consideration, if it is zero the configuration is singular. The results thus obtained by different methods namely the genetic algorithm, Particle Swarm optimization and variants and simulated annealing are compared with each other. The variable sets considered are the respective desirable platform motions in the form of translation and rotation in six degrees of freedom. This paper hence presents a proper comparative study of these algorithms based on the results that are obtained and highlights the advantage of each in terms of computational cost and accuracy.
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K. Kumaravel
2015-05-01
Full Text Available Wireless Mesh Network (WMN uses the latest technology which helps in providing end users a high quality service referred to as the Internet’s “last mile”. Also considering WMN one of the most important technologies that are employed is multicast communication. Among the several issues routing which is significantly an important issue is addressed by every WMN technologies and this is done during the process of data transmission. The IEEE 802.11s Standard entails and sets procedures which need to be followed to facilitate interconnection and thus be able to devise an appropriate WMN. There has been introduction of several protocols by many authors which are mainly devised on the basis of machine learning and artificial intelligence. Multi-path routing may be considered as one such routing method which facilitates transmission of data over several paths, proving its capabilities as a useful strategy for achieving reliability in WMN. Though, multi-path routing in any manner cannot really guarantee deterministic transmission. As here there are multiple paths available for enabling data transmission from source to destination node. The algorithm that had been employed before in the studies conducted did not take in to consideration routing metrics which include energy aware metrics that are used for path selection during transferring of data. The following study proposes use of the hybrid multipath routing algorithm while taking in to consideration routing metrics which include energy, minimal loss for efficient path selection and transferring of data. Proposed algorithm here has two phases. In the first phase prim’s algorithm has been proposed so that in networks route discovery may be possible. For the second one the Hybrid firefly algorithm which is based on harmony search has been employed for selection of the most suitable and best through proper analysis of metrics which include energy awareness and minimal loss for every path that has
A hybrid multiview stereo algorithm for modeling urban scenes.
Lafarge, Florent; Keriven, Renaud; Brédif, Mathieu; Vu, Hoang-Hiep
2013-01-01
We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial meshbased surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to state-of-the-art multiview stereo meshing algorithms.
Beam Pattern Synthesis Based on Hybrid Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
YU Yan-li; WANG Ying-min; LI Lei
2010-01-01
As conventional methods for beam pattern synthesis can not always obtain the desired optimum pattern for the arbitrary underwater acoustic sensor arrays, a hybrid numerical synthesis method based on adaptive principle and genetic algorithm was presented in this paper. First, based on the adaptive theory, a given array was supposed as an adaptive array and its sidelobes were reduced by assigning a number of interference signals in the sidelobe region. An initial beam pattern was obtained after several iterations and adjustments of the interference intensity, and based on its parameters, a desired pattern was created. Then, an objective function based on the difference between the designed and desired patterns can be constructed. The pattern can be optimized by using the genetic algorithm to minimize the objective function. A design example for a double-circular array demonstrates the effectiveness of this method. Compared with the approaches existing before, the proposed method can reduce the sidelobe effectively and achieve less synthesis magnitude error in the mainlobe.The method can search for optimum attainable pattern for the specific elements if the desired pattern can not be found.
Johan Soewanda; Tanti Octavia; Iwan Halim Sahputra
2007-01-01
This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than...
Double Motor Coordinated Control Based on Hybrid Genetic Algorithm and CMAC
Cao, Shaozhong; Tu, Ji
A novel hybrid cerebellar model articulation controller (CMAC) and online adaptive genetic algorithm (GA) controller is introduced to control two Brushless DC motor (BLDCM) which applied in a biped robot. Genetic Algorithm simulates the random learning among the individuals of a group, and CMAC simulates the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments have been done in MATLAB/SIMULINK. Analysis among GA, hybrid GA-CMAC and CMAC feed-forward control is also given. The results prove that the torque ripple of the coordinated control system is eliminated by using the hybrid GA-CMAC algorithm.
Optimum Performance-Based Seismic Design Using a Hybrid Optimization Algorithm
Directory of Open Access Journals (Sweden)
S. Talatahari
2014-01-01
Full Text Available A hybrid optimization method is presented to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized. The algorithm is based on the concepts of the charged system search in which each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Comparison of the results of the hybrid algorithm with those of other metaheuristic algorithms shows the efficiency of the hybrid algorithm.
BiCluE - Exact and heuristic algorithms for weighted bi-cluster editing of biomedical data
DEFF Research Database (Denmark)
Sun, Peng; Guo, Jiong; Baumbach, Jan
2013-01-01
different types. Bi-cluster editing, as a special case of clustering, which partitions two different types of data simultaneously, might be used for several biomedical scenarios. However, the underlying algorithmic problem is NP-hard.RESULTS:Here we contribute with BiCluE, a software package designed...
2015-01-01
How can we advance knowledge? Which methods do we need in order to make new discoveries? How can we rationally evaluate, reconstruct and offer discoveries as a means of improving the ‘method’ of discovery itself? And how can we use findings about scientific discovery to boost funding policies, thus fostering a deeper impact of scientific discovery itself? The respective chapters in this book provide readers with answers to these questions. They focus on a set of issues that are essential to the development of types of reasoning for advancing knowledge, such as models for both revolutionary findings and paradigm shifts; ways of rationally addressing scientific disagreement, e.g. when a revolutionary discovery sparks considerable disagreement inside the scientific community; frameworks for both discovery and inference methods; and heuristics for economics and the social sciences.
柔性作业车间调度问题的一种启发式算法%Heuristic algorithm for flexible Job-Shop scheduling
Institute of Scientific and Technical Information of China (English)
苏子林; 车忠志; 苑金梁
2011-01-01
为了研究多目标柔性作业车间调度问题,基于甘特图和搭积木经验进行了分析,提出了一种组合优先规则和基于此优先规则的启发式算法.组合优先规则面向完工时间、关键机床负荷和总负荷三个指标,改变规则中各数据项的比例可调整三个指标所占的比例;算法采用随机方式调整三个指标的比例,并微调最优解对应的比例.能随机产生多个高质量调度解.算法对比测试表明,该算法求解质量高、运行速度快且稳定,可直接用于在其他调度算法中产生初始解或者用于动态调度.%To study multi-objective flexible Job-Shop scheduling problem, this paper analyzed it based on Gantt graph and experience from building block, presented a composite priority rule and heuristic algorithm based on this priority rule.This composite priority rule was for three scheduling targets including makespan, critical machine workload and total workload, changing the ratio of data items in the rule could adjust the ratio of the three scheduling targets.The algorithm randomly adjusted the ratio of this three scheduling targets, and slightly adjusted the ratio corresponding to the best solution, could randomly generate many excellent scheduling solutions.The algorithm' s comparison and test show that the result of this algorithm is more excellent, the algorithm runs rapidly and steadily, and can directly be used in generating initial solution in other scheduling algorithms or dynamic scheduling.
双边多工位装配线平衡问题%Heuristic algorithm of two-sided with multi-parallel stations assembly line balancing
Institute of Scientific and Technical Information of China (English)
张宏林; 殷复鹏; 吴爱华
2013-01-01
针对装配线上一个位置左右两边各有多个工位的装配线平衡问题建立数学模型,提出一种启发式平衡算法.该算法分为两个阶段,阶段1从未分配的作业元素中找出能够分配给某一位置的作业,构成集合W；阶段2按照不违背作业优先关系、方位约束和工位时间不超过节拍的原则,把W中的部分或全部作业元素分配到该位置内的各工位上；依此循环,直至所有作业元素分配完毕.以某重型汽车装配翻车前的装配线平衡为例,说明了所提算法的有效性.%To study the complex assembly line balancing problem, such as two-sided with multi-stations assembly line balancing problem, a mathematical model based on some assumptions to this problem was set up and a heuristic balancing algorithm was proposed. The algorithm had two phases. Phase one was to find out the operations which could be assigned to one position, corresponding to the constraint conditions in the mathematical model, and set up the set W with them. And phase two was to select and assign operations from W to the right station of the position without violating rules as precedence, position constraint and station time. All operations' assignments were completed based on this cycle. Finally, a truck assembly line balancing problem was provided as example to verify the effectiveness of the algorithm.
A Hybrid Quantum Search Engine: A Fast Quantum Algorithm for Multiple Matches
Younes, A; Miller, J; Younes, Ahmed; Rowe, Jon; Miller, Julian
2003-01-01
In this paper we will present a quantum algorithm which works very efficiently in case of multiple matches within the search space and in the case of few matches, the algorithm performs classically. This allows us to propose a hybrid quantum search engine that integrates Grover's algorithm and the proposed algorithm here to have general performance better that any pure classical or quantum search algorithm.
Selçuk K. İşleyen; Ö. Faruk Baykoç
2008-01-01
We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD) where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP) and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS) algorithm in order to reach an effective solution.
Directory of Open Access Journals (Sweden)
2015-03-01
Full Text Available The dynamic economic load dispatch is one of the main problems of power systems generation and operation. The objective is to schedule power generation for units over a certain period of time, while satisfying operating constraints and load demand in each interval. Wind farms, as renewable energy resources are playing an increasing role in electricity generation. In this paper, a computational framework is presented to solve the dynamic economic emission dispatch problem with inclusion of wind farms considering their associated constraints. An optimization algorithm called modified co-evolutionary particle swarm optimization (MCPSO is proposed to solve the problem. In the proposed algorithm, two kinds of swarms evolve interactively where one of them is used to calculate the penalty factors (constraints handling and the other is used for searching good solutions (optimization process. In addition, some modifications such as using an inertia weight that decreases linearly during the simulation are made to improve the performance of the algorithm. Finally, the validity and superiority of the proposed method are demonstrated by simulation results on a modiﬁed IEEE benchmark system including six thermal units and two wind farms.
Multiuser Detection in MIMO-OFDM Wireless Communication System Using Hybrid Firefly Algorithm
Directory of Open Access Journals (Sweden)
B. Sathish Kumar
2014-05-01
Full Text Available In recent years, future generation wireless communication technologies are most the prominent fields in which many innovative techniques are used for effective communication. Orthogonal frequency-division multiplexing is one of the important technologies used for communication in future generation technologies. Although it gives efficient results, it has some problems during the implementation in real-time. MIMO and OFDM are integrated to have both their benefits. But, noise and interference are the major issues in the MIMO OFDM systems. To overcome these issues multiuser detection method is used in MIMO OFDM. Several algorithms and mathematical formulations have been presented for solving multiuser detection problem in MIMO OFDM systems. The algorithms such as genetic simulated annealing algorithm, hybrid ant colony optimization algorithm are used for multiuser detection problem in previous studies. But, due to the limitations of those optimization algorithms, the results obtained are not significant. In this research, to overcome the noise and interference problems, hybrid firefly optimization algorithm based on the evolutionary algorithm is proposed. The proposed algorithm is compared with the existing multiuser detection algorithm such as particle swarm optimization, CEFM-GADA [complementary error function mutation (CEFM and a differential algorithm (DA genetic algorithm (GA] and Hybrid firefly optimization algorithm based on evolutionary algorithm. The simulation results shows that performance of the proposed algorithm is better than the existing algorithm and it provides a satisfactory trade-off between computational complexity and detection performance
HYBRID APPROACH FOR OPTIMAL CLUSTER HEAD SELECTION IN WSN USING LEACH AND MONKEY SEARCH ALGORITHMS
Directory of Open Access Journals (Sweden)
T. SHANKAR
2017-02-01
Full Text Available Wireless Sensor Networks (WSNs are being widely used with low-cost, lowpower, multifunction sensors based on the development of wireless communication, which has enabled a wide variety of new applications. In WSN, the main concern is that it contains a limited power battery and is constrained in energy consumption hence energy and lifetime are of paramount importance. To achieve high energy efficiency and prolong network lifetime in WSNs, clustering techniques have been widely adopted. The proposed algorithm is hybridization of well-known Low-Energy Adaptive Clustering Hierarchy (LEACH algorithm with a distinctive Monkey Search (MS algorithm, which is an optimization algorithm used for optimal cluster head selection. The proposed hybrid algorithm exhibit high throughput, residual energy and improved lifetime. Comparison of the proposed hybrid algorithm is made with the well-known cluster-based protocols for WSNs, namely, LEACH and monkey search algorithm, individually.
A HYBRID GRANULARITY PARALLEL ALGORITHM FOR PRECISE INTEGRATION OF STRUCTURAL DYNAMIC RESPONSES
Institute of Scientific and Technical Information of China (English)
Yuanyin Li; Xianlong Jin; Genguo Li
2008-01-01
Precise integration methods to solve structural dynamic responses and the corre-sponding time integration formula are composed of two parts: the multiplication of an exponential matrix with a vector and the integration term. The second term can be solved by the series solu-tion. Two hybrid granularity parallel algorithms are designed, that is, the exponential matrix and the first term are computed by the fine-grained parallel algorithm and the second term is com-puted by the coarse-grained parallel algorithm. Numerical examples show that these two hybrid granularity parallel algorithms obtain higher speedup and parallel efficiency than two existing parallel algorithms.
Institute of Scientific and Technical Information of China (English)
Shao Wei; Qian Zuping; Yuan Feng
2007-01-01
A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on generalized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA composed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables.Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is simpler than conventional algorithms when it comes to hardware implementation. Moreover, it processes only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.
Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.
Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng
2013-09-01
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.
Directory of Open Access Journals (Sweden)
Eric Z. Chen
2015-01-01
Full Text Available Error control codes have been widely used in data communications and storage systems. One central problem in coding theory is to optimize the parameters of a linear code and construct codes with best possible parameters. There are tables of best-known linear codes over finite fields of sizes up to 9. Recently, there has been a growing interest in codes over $\\mathbb{F}_{13}$ and other fields of size greater than 9. The main purpose of this work is to present a database of best-known linear codes over the field $\\mathbb{F}_{13}$ together with upper bounds on the minimum distances. To find good linear codes to establish lower bounds on minimum distances, an iterative heuristic computer search algorithm is employed to construct quasi-twisted (QT codes over the field $\\mathbb{F}_{13}$ with high minimum distances. A large number of new linear codes have been found, improving previously best-known results. Tables of $[pm, m]$ QT codes over $\\mathbb{F}_{13}$ with best-known minimum distances as well as a table of lower and upper bounds on the minimum distances for linear codes of length up to 150 and dimension up to 6 are presented.
Directory of Open Access Journals (Sweden)
Dadiek Pranindito
2014-11-01
Full Text Available Saat ini, dalam dunia telekomunikasi, (Worldwide Interoperability for Microwave Access WiMaX merupakan teknologi nirkabel yang menyediakan hubungan jalur lebar dalam jarak jauh, memiliki kecepatan akses yang tinggi dan jangkauan yang luas serta menyediakan berbagai macam jenis layanan. Masalah yang menarik dan menantang pada WiMaX adalah dalam hal menyediakan jaminan kualitas pelayanan (QoS untuk jenis layanan yang berbeda dengan bermacam-macam kebutuhan QoS-nya. Untuk memenuhi kebutuhan QoS tersebut, maka diperlukan suatu algoritma penjadwalan. Dalam penelitian ini dilakukan simulasi jaringan WiMaX menerapkan algoritma penjadwalan dengan metode homogeneous algorithm dan hybrid algorithm. Perwakilan pada metode homogeneous algorithm akan menggunakan algoritma penjadwalan Weighted Fair Queuing (WFQ dan Deficit Round Robin (DRR, sedangkan pada metode hybrid algorithm menggunakan penggabungan antara algoritma penjadwalan DRR dan WFQ. Pengujian kinerja algoritma penjadwalan tersebut dilakukan dengan membandingkan kedalam 5 jenis kelas QoS pada WiMAX yaitu UGS, rtPS, nrtPS, ertPS, dan Best Effort. Dari hasil pengujian, hybrid algorithm memberikan nilai QoS yang lebih baik jika dibandingkan dengan homogeneous algorithm. hybrid algorithm sangat cocok jika diterapkan pada kondisi jaringan yang memiliki trafik dengan paket data yang bervariasi, karena dapat menghasilkan throughput yang tinggi, serta dapat menghasilkan nilai delay dan jitter yang rendah
Ryzhikov, I. S.; Semenkin, E. S.
2017-02-01
This study is focused on solving an inverse mathematical modelling problem for dynamical systems based on observation data and control inputs. The mathematical model is being searched in the form of a linear differential equation, which determines the system with multiple inputs and a single output, and a vector of the initial point coordinates. The described problem is complex and multimodal and for this reason the proposed evolutionary-based optimization technique, which is oriented on a dynamical system identification problem, was applied. To improve its performance an algorithm restart operator was implemented.
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS algorithm in order to reach an effective solution.
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization
Directory of Open Access Journals (Sweden)
Yanhua Zhong
2012-11-01
Full Text Available Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision; analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
A Comparative Study of Several Hybrid Particle Swarm Algorithms for Function Optimization
Directory of Open Access Journals (Sweden)
Yanhua Zhong
2013-01-01
Full Text Available Currently, the researchers have made a lot of hybrid particle swarm algorithm in order to solve the shortcomings that the Particle Swarm Algorithms is easy to converge to local extremum, these algorithms declare that there has been better than the standard particle swarm. This study selects three kinds of representative hybrid particle swarm optimizations (differential evolution particle swarm optimization, GA particle swarm optimization, quantum particle swarm optimization and the standard particle swarm optimization to test with three objective functions. We compare evolutionary algorithm performance by a fixed number of iterations of the convergence speed and accuracy and the number of iterations under the fixed convergence precision, analyzing these types of hybrid particle swarm optimization results and practical performance. Test results show hybrid particle algorithm performance has improved significantly.
A Heuristic Approach for Aeromedical Evacuation System scheduling and Routing
1988-12-16
can be used to assign AES aircraft to the six medical regions. Additionally, a heuristic algorithm is developed and applied to the AES in order to...These works were used to form the foundation for developing a heuristic algorithm that can be applied to the AES, or to other systems in which vehicles...his destination. Another possible research effort involving the daily routing problem involves formulating a heuristic algorithm that quickly and
Model of Wagons’ Placing-In and Taking-Out Problem in a Railway Station and Its Heuristic Algorithm
Directory of Open Access Journals (Sweden)
Chuijiang Guo
2014-01-01
Full Text Available Placing-in and taking-out wagons timely can decrease wagons’ dwell time in railway stations, improve the efficiency of railway transportation, and reduce the cost of goods transportation. We took the locomotive running times between goods operation sites as weights, so the wagons’ placing-in and taking-out problem could be regarded as a single machine scheduling problem, 1pijCmax, which could be transformed into the shortest circle problem in a Hamilton graph whose relaxation problem was an assignment problem. We used a Hungarian algorithm to calculate the optimal solution of the assignment problem. Then we applied a broken circle and connection method, whose computational complexity was O(n2, to find the available satisfactory order of wagons’ placing-in and taking-out. Complex problems, such as placing-in and transferring combined, taking-out and transferring combined, placing-in and taking-out combined, or placing-in, transferring, and taking-out combined, could also be resolved with the extended algorithm. A representative instance was given to illustrate the reliability and efficiency of our results.
Jough, Fooad Karimi Ghaleh; Şensoy, Serhan
2016-12-01
Different performance levels may be obtained for sideway collapse evaluation of steel moment frames depending on the evaluation procedure used to handle uncertainties. In this article, the process of representing modelling uncertainties, record to record (RTR) variations and cognitive uncertainties for moment resisting steel frames of various heights is discussed in detail. RTR uncertainty is used by incremental dynamic analysis (IDA), modelling uncertainties are considered through backbone curves and hysteresis loops of component, and cognitive uncertainty is presented in three levels of material quality. IDA is used to evaluate RTR uncertainty based on strong ground motion records selected by the k-means algorithm, which is favoured over Monte Carlo selection due to its time saving appeal. Analytical equations of the Response Surface Method are obtained through IDA results by the Cuckoo algorithm, which predicts the mean and standard deviation of the collapse fragility curve. The Takagi-Sugeno-Kang model is used to represent material quality based on the response surface coefficients. Finally, collapse fragility curves with the various sources of uncertainties mentioned are derived through a large number of material quality values and meta variables inferred by the Takagi-Sugeno-Kang fuzzy model based on response surface method coefficients. It is concluded that a better risk management strategy in countries where material quality control is weak, is to account for cognitive uncertainties in fragility curves and the mean annual frequency.
Hybrid genetic algorithm approach for selective harmonic control
Energy Technology Data Exchange (ETDEWEB)
Dahidah, Mohamed S.A. [Faculty of Engineering, Multimedia University, 63100, Jalan Multimedia-Cyberjaya, Selangor (Malaysia); Agelidis, Vassilios G. [School of Electrical and Information Engineering, The University of Sydney, NSW (Australia); Rao, Machavaram V. [Faculty of Engineering and Technology, Multimedia University, 75450, Jalan Ayer Keroh Lama-Melaka (Malaysia)
2008-02-15
The paper presents an optimal solution for a selective harmonic elimination pulse width modulated (SHE-PWM) technique suitable for a high power inverter used in constant frequency utility applications. The main challenge of solving the associated non-linear equations, which are transcendental in nature and, therefore, have multiple solutions, is the convergence, and therefore, an initial point selected considerably close to the exact solution is required. The paper discusses an efficient hybrid real coded genetic algorithm (HRCGA) that reduces significantly the computational burden, resulting in fast convergence. An objective function describing a measure of the effectiveness of eliminating selected orders of harmonics while controlling the fundamental, namely a weighted total harmonic distortion (WTHD) is derived, and a comparison of different operating points is reported. It is observed that the method was able to find the optimal solution for a modulation index that is higher than unity. The theoretical considerations reported in this paper are verified through simulation and experimentally on a low power laboratory prototype. (author)
An Effective Hybrid Artificial Bee Colony Algorithm for Nonnegative Linear Least Squares Problems
Directory of Open Access Journals (Sweden)
Xiangyu Kong
2014-07-01
Full Text Available An effective hybrid artificial bee colony algorithm is proposed in this paper for nonnegative linear least squares problems. To further improve the performance of algorithm, orthogonal initialization method is employed to generate the initial swarm. Furthermore, to balance the exploration and exploitation abilities, a new search mechanism is designed. The performance of this algorithm is verified by using 27 benchmark functions and 5 nonnegative linear least squares test problems. And the comparison analyses are given between the proposed algorithm and other swarm intelligence algorithms. Numerical results demonstrate that the proposed algorithm displays a high performance compared with other algorithms for global optimization problems and nonnegative linear least squares problems.
The novel generating algorithm and properties of hybrid-P-ary generalized bridge functions
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In this paper, we develop novel non-sine functions, named hybrid-P-ary generalized bridge functions, based on the copy and shift methods. The generating algorithm of hybrid-P-ary generalized bridge functions is introduced based on the hybrid-P-ary generalized Walsh function's copy algorithm. The main property, product property, is also discussed. This function may be viewed as the generalization of the theory of bridge functions. And a lot of non-sine orthogonal functions are the special subset of these novel functions. The hybrid-P-ary generalized bridge functions can be used to search many unknown non-sine functions by defining different parameters.
Heuristic algorithm to incorporating robustness into airline fleet planning%航空公司机队的鲁棒性规划启发式算法
Institute of Scientific and Technical Information of China (English)
汪瑜; 孙宏
2013-01-01
为了解决传统机队规划方法无法反映机队运营鲁棒性的缺陷,针对单基地线性航线结构运营模式特点,以基地机场配置机型数最小为目标函数,考虑“航班节”机型分配成本限制,“航班节”机型分配唯一性限制,所选机型最少飞机数限制等条件构建机队的鲁棒性规划模型,并结合唯一竞争机型限制为模型设计启发式算法.“39个航班节,6种候选机型”的案例分析表明:传统机队规划法所得出的机队构成中有3种机型,而由机队的鲁棒性规划法所得出的机队构成中机型数仅为2种,且机队构成能够很好的适应市场需求的波动,因此算法可行.%Traditional airline fleet planning methods could not reflect the robustness of fleet composition. In order to solve this shortcoming for airlines which operated in single-base linear route structure operating mode, this paper regarded minimum aircraft types deployed on single-base airport as objective, with flight pairing fleet assignment cost constraint, flight pairing fleet assignment uniqueness constraint, and least numbers of selected aircraft types constraint, to incorporate robustness into airline fleet planning model. Combining with only one competitive aircraft type in a desired fleet composition, the simulated annealing algorithm was employed to design heuristic algorithm for this proposed model. An empirical example containing 39 flight parings and 6 candidate aircraft types indicates that the fleet composition derived from traditional fleet planning method has three aircraft types while the proposed algorithm has only two. Furthermore, the fleet composition can well adapt to the market fluctuations, so the algorithm is feasible.
Cryptanalysis of optical encryption: a heuristic approach
Gopinathan, Unnikrishnan; Monaghan, David S.; Naughton, Thomas J.; Sheridan, John T.
2006-10-01
The Fourier plane encryption algorithm is subjected to a heuristic known-plaintext attack. The simulated annealing algorithm is used to estimate the key using a known plaintext-ciphertext pair which decrypts the ciphertext with arbitrarily low error. The strength of the algorithm is tested by using the key to decrypt a different ciphertext encrypted using the same original key. The Fourier plane encryption algorithm is found to be susceptible to a known-plaintext heuristic attack. It is found that phase only encryption, a variation of Fourier plane encoding algorithm, successfully defends against this attack.
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.
A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns
Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng
2009-11-01
Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.
Meta-heuristics in cellular manufacturing: A state-of-the-art review
Directory of Open Access Journals (Sweden)
Tamal Ghosh
2011-01-01
Full Text Available Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area.
A FLEXIBLE HYBRID GMRES ALGORITHM%一种灵活的混合GMRES算法
Institute of Scientific and Technical Information of China (English)
钟宝江
2001-01-01
A variant of the hybrid GMRES algorithm of N.M. Nachtigal, L. Reichel, and L. N. Trefethen for solving large nonsymmetric systems of linear equations is presented. This algorithm allows the GMRES polynomial re-applied later being constructed in the course of a restarted GMRES iteration. It is described how the new hybrid scheme may offer significant performance improvements over the old one.
A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids
Jing Li; Wei Wei; Ji Xiang
2012-01-01
In this paper, we develop a simple algorithm to determine the required number of generating units of wind-turbine generator and photovoltaic array, and the associated storage capacity for stand-alone hybrid microgrid. The algorithm is based on the observation that the state of charge of battery should be periodically invariant. The optimal sizing of hybrid microgrid is given in the sense that the life cycle cost of system is minimized while the given load power demand can be satisfied without...
Hybrid algorithm for accelerating the double series of Floquet vector modes
Institute of Scientific and Technical Information of China (English)
LI Weidong; HONG Wei; HAO Zhangcheng; ZHOU Houxing
2006-01-01
In this paper, a hybrid algorithm for accelerating the double series of Floquet vector modes arising in the analysis of frequency selective surfaces (FSS) is presented. The asymptotic terms with slow convergence in the double series are first accelerated by Poisson transformation and Ewald method, and then the remained series is accelerated by Shank transformation. It results in significant savings in memory and computing time. Numerical examples verify the validity of the hybrid acceleration algorithm.
An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy.
Zhang, Yu-Xian; Qian, Xiao-Yi; Peng, Hui-Deng; Wang, Jian-Hui
2016-01-01
For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. And H ε gate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved by Markov chain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.
An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy
Directory of Open Access Journals (Sweden)
Yu-Xian Zhang
2016-01-01
Full Text Available For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. And Hε gate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved by Markov chain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.
Energy Technology Data Exchange (ETDEWEB)
Sheng, Zheng, E-mail: 19994035@sina.com [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Wang, Jun; Zhou, Bihua [National Defense Key Laboratory on Lightning Protection and Electromagnetic Camouflage, PLA University of Science and Technology, Nanjing 210007 (China); Zhou, Shudao [College of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101 (China); Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044 (China)
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems
Narendhar, S.; T Amudha
2012-01-01
Bio-Inspired computing is the subset of Nature-Inspired computing. Job Shop Scheduling Problem is categorized under popular scheduling problems. In this research work, Bacterial Foraging Optimization was hybridized with Ant Colony Optimization and a new technique Hybrid Bacterial Foraging Optimization for solving Job Shop Scheduling Problem was proposed. The optimal solutions obtained by proposed Hybrid Bacterial Foraging Optimization algorithms are much better when compared with the solution...
Heuristic Algorithm for Approximate Pattern Matching Problem%求解近似模式匹配的启发式算法
Institute of Scientific and Technical Information of China (English)
黄国林; 郭丹; 胡学钢
2013-01-01
研究了带有灵活通配符和长度约束的近似模式匹配问题(approximate pattern matching with wildcards and length constraint,APMWL)；为避免文本字符重复使用造成解的指数级增长,引入了一次性使用原则one_off条件,提出了一种后向构造编辑距离矩阵的BAPM(backward approximate pattern matching)算法.该算法在one_off条件、灵活通配符和长度约束条件的基础上,可同时处理插入、替换和删除三种编辑操作.与同类算法Sail_Approx进行实验对比,结果表明BAPM算法获取解的平均增长率可达18.99%,具备良好的解优势.%This paper studies APMWL (approximate pattern matching with wildcards and length constraint) problem. In order to avoid the exponential growth of matching patterns, the paper introduces the one_off condition, and proposes a heuristic algorithm BAPM (backward approximate pattern matching) which constructing the edit distance matrix backwardly. Based on one_off condition, flexible wildcards and length constraint, BAPM can simultaneously process three edit operations, namely insertion, replacement and deletion. The experimental results show that BAPM has a significant advantage on matching solutions compared with Sail-Approx, and the average improvement rate of matching is up to 18.99%.
CHAOS-REGULARIZATION HYBRID ALGORITHM FOR NONLINEAR TWO-DIMENSIONAL INVERSE HEAT CONDUCTION PROBLEM
Institute of Scientific and Technical Information of China (English)
王登刚; 刘迎曦; 李守巨
2002-01-01
A numerical model of nonlinear two-dimensional steady inverse heat conduction problem was established considering the thermal conductivity changing with temperature.Combining the chaos optimization algorithm with the gradient regularization method, a chaos-regularization hybrid algorithm was proposed to solve the established numerical model.The hybrid algorithm can give attention to both the advantages of chaotic optimization algorithm and those of gradient regularization method. The chaos optimization algorithm was used to help the gradient regalarization method to escape from local optima in the hybrid algorithm. Under the assumption of temperature-dependent thermal conductivity changing with temperature in linear rule, the thermal conductivity and the linear rule were estimated by using the present method with the aid of boundary temperature measurements. Numerical simulation results show that good estimation on the thermal conductivity and the linear function can be obtained with arbitrary initial guess values, and that the present hybrid algorithm is much more efficient than conventional genetic algorithm and chaos optimization algorithm.
Zhang, Jiapu
2010-01-01
Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...
2014-01-01
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the ...
Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes.
Tumuluru, Jaya Shankar; McCulloch, Richard
2016-11-09
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.
Directory of Open Access Journals (Sweden)
Navjot Kaur Dalip
2011-11-01
Full Text Available This paper, proposes a solution for Travelling Salesman Problem (TSP [1], using Genetic Algorithm (GA. The proposed algorithm works on data sets of latitude and longitude coordinates of cities and provides optimal tours in shorter time; giving convergence that is fast and better. To improve the solution few heuristic improvements are applied to prevent converging to local optima. The principleof natural selection here is based on both survival and reproduction capacities; that accelerate the convergence speed. Various factors affect the performance of GA(s, such as genetic operators,population etc. As the performance of GA is greatly affected by the initial population, the initial population for the algorithm is sorted first, using Quick Sort, this preserves the better fit population. Also, GA parameters such as selection and mutation probabilities are varied, to obtain enhanced and better performance. The computational results are compared with symmetric problems for some benchmark TSP LIB instances.
A two-stage heuristic method for vehicle routing problem with split deliveries and pickups
Institute of Scientific and Technical Information of China (English)
Yong WANG; Xiao-lei MA; Yun-teng LAO; Hai-yan YU; Yong LIU
2014-01-01
The vehicle routing problem (VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups (VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.
A Heuristic Approach for International Crude Oil Transportation Scheduling Problems
Yin, Sisi; Nishi, Tatsushi; Izuno, Tsukasa
In this paper, we propose a heuristic algorithm to solve a practical ship scheduling problem for international crude oil transportation. The problem is considered as a vehicle routing problem with split deliveries. The objective of this paper is to find an optimal assignment of tankers, a sequence of visiting and loading volume simultaneously in order to minimize the total distance satisfying the capacity of tankers. A savings-based meta-heuristic algorithm with lot sizing parameters and volume assignment heuristic is developed. The proposed method is applied to solve a case study with real data. Computational results demonstrate the effectiveness of the heuristic algorithm compared with that of human operators.
Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms
Abraham, Ajith
2004-01-01
Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex s...
2012-03-01
Rao discusses the most common gradient search algorithms in [71] along with some heuristic ones that use some added randomness to direct the search... aircrews and mission planners who have interest in this type of problem. 98 Target T=Threat T x1 x2 B ou nd ar y B ou nd ar y T θ V (tf , x1(tf ), x2(tf...processing. The most valuable product to mission planners and aircrews is not the set of statistics describing expected values, variances, and covariances and
Three hybridization models based on local search scheme for job shop scheduling problem
Balbi Fraga, Tatiana
2015-05-01
This work presents three different hybridization models based on the general schema of Local Search Heuristics, named Hybrid Successive Application, Hybrid Neighborhood, and Hybrid Improved Neighborhood. Despite similar approaches might have already been presented in the literature in other contexts, in this work these models are applied to analyzes the solution of the job shop scheduling problem, with the heuristics Taboo Search and Particle Swarm Optimization. Besides, we investigate some aspects that must be considered in order to achieve better solutions than those obtained by the original heuristics. The results demonstrate that the algorithms derived from these three hybrid models are more robust than the original algorithms and able to get better results than those found by the single Taboo Search.
Scheduling constrained tools using heuristic techniques
Maram, Venkataramana; Rahman, Syariza Abdul; Maram, Sandhya Rani
2015-12-01
One of the main challenge to the current manufacturing production planning is to provide schedules of operations to maximize resource utilization to yield highest overall productivity. This is achieved by scheduling available resources to activities. There can be many different real time scenarios with different combination of input resources to produce parts. In this paper, the problem is simplified to single machine with individual process times and due dates to represent the real world scheduling problem. The main objective function is to minimize the total tardiness or late jobs. Nearest greedy method of assignment problem algorithm is used to find the initial solution followed by Simulated Annealing (SA) algorithm for the improvement part. Simulated Annealing is one of the meta-heuristic techniques in solving combinatorial optimization problem. The general purpose Microsoft Visual C++ is used to developed algorithm for finding the best solution. The proposed hybrid approach able to generate best schedule in 7th and optimal in 170th iteration with tardiness 8 and 7 hours respectively.
Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation
Directory of Open Access Journals (Sweden)
R. V. V. Krishna
2016-10-01
Full Text Available This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD which is a modification of Weber Local Descriptor (WLD is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.
Hybrid partheno-genetic algorithm and its application in flow-shop problem
Institute of Scientific and Technical Information of China (English)
李树刚; 吴智铭; 庞小红
2004-01-01
In order to solve the constraint satisfied problem in the genetic algorithm, the partheno-genetic algorithm is designed. And then the schema theorem of the partheno-genetic algorithm is proposed to show that the high rank schemas at the subsequent generation decrease exponentially even though its fitness is more optimal than the average one in the population and the low rank schemas at the subsequent generation increase exponentially when its fitness is more optimal than the average one in the population. In order to overcome the shortcoming that the optimal high rank schema can be deserted arbitrarily, the HGA (hybrid partheno-genetic algorithm) is proposed, that is, the hill-climbing algorithm is integrated to search for a better individual. Finally, the results of the simulation for facility layout problem and no-wait schedule problem are given. It is shown that the hybrid partheno- genetic algorithm is of high efficiency.
Energy Technology Data Exchange (ETDEWEB)
Elsheikh, Ahmed H., E-mail: aelsheikh@ices.utexas.edu [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom); Wheeler, Mary F. [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Hoteit, Ibrahim [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
A Hybrid Distributed Mutual Exclusion Algorithm for Cluster-Based Systems
Directory of Open Access Journals (Sweden)
Moharram Challenger
2013-01-01
Full Text Available Distributed mutual exclusion is a fundamental problem which arises in various systems such as grid computing, mobile ad hoc networks (MANETs, and distributed databases. Reducing key metrics like message count per any critical section (CS and delay between two CS entrances, which is known as synchronization delay, is a great challenge for this problem. Various algorithms use either permission-based or token-based protocols. Token-based algorithms offer better communication costs and synchronization delay. Raymond's and Suzuki-Kasami's algorithms are well-known token-based ones. Raymond's algorithm needs only O(log2(N messages per CS and Suzuki-Kasami's algorithm needs just one message delivery time between two CS entrances. Nevertheless, both algorithms are weak in the other metric, synchronization delay and message complexity correspondingly. In this work, a new hybrid algorithm is proposed which gains from powerful aspects of both algorithms. Raysuz's algorithm (the proposed algorithm uses a clustered graph and executes Suzuki-Kasami's algorithm intraclusters and Raymond's algorithm interclusters. This leads to have better message complexity than that of pure Suzuki-Kasami's algorithm and better synchronization delay than that of pure Raymond's algorithm, resulting in an overall efficient DMX algorithm pure algorithm.
Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm
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.
Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid approximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimization (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.
A Simple Sizing Algorithm for Stand-Alone PV/Wind/Battery Hybrid Microgrids
Directory of Open Access Journals (Sweden)
Jing Li
2012-12-01
Full Text Available In this paper, we develop a simple algorithm to determine the required number of generating units of wind-turbine generator and photovoltaic array, and the associated storage capacity for stand-alone hybrid microgrid. The algorithm is based on the observation that the state of charge of battery should be periodically invariant. The optimal sizing of hybrid microgrid is given in the sense that the life cycle cost of system is minimized while the given load power demand can be satisfied without load rejection. We also report a case study to show the efficacy of the developed algorithm.
A hybrid quantum encoding algorithm of vector quantization for image compression
Institute of Scientific and Technical Information of China (English)
Pang Chao-Yang; Zhou Zheng-Wei; Guo Guang-Can
2006-01-01
Many classical encoding algorithms of vector quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.
A hybrid genetic algorithm based on mutative scale chaos optimization strategy
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In order to avoid such problems as low convergent speed and local optimal solution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In this algorithm, a mutative scale chaos optimization strategy is operated on the population after a genetic operation. And according to the searching process, the searching space of the optimal variables is gradually diminished and the regulating coefficient of the secondary searching process is gradually changed which will lead to the quick evolution of the population. The algorithm has such advantages as fast search, precise results and convenient using etc. The simulation results show that the performance of the method is better than that of simple genetic algorithms.
Comparison Of Hybrid Sorting Algorithms Implemented On Different Parallel Hardware Platforms
Directory of Open Access Journals (Sweden)
Dominik Zurek
2013-01-01
Full Text Available Sorting is a common problem in computer science. There are lot of well-known sorting algorithms created for sequential execution on a single processor. Recently, hardware platforms enable to create wide parallel algorithms. We have standard processors consist of multiple cores and hardware accelerators like GPU. The graphic cards with their parallel architecture give new possibility to speed up many algorithms. In this paper we describe results of implementation of a few different sorting algorithms on GPU cards and multicore processors. Then hybrid algorithm will be presented which consists of parts executed on both platforms, standard CPU and GPU.
de Jong, Menno D.T.; van der Geest, Thea
2000-01-01
This article is intended to make Web designers more aware of the qualities of heuristics by presenting a framework for analyzing the characteristics of heuristics. The framework is meant to support Web designers in choosing among alternative heuristics. We hope that better knowledge of the
Kanagaraj, G.; Ponnambalam, S. G.; Jawahar, N.; Mukund Nilakantan, J.
2014-10-01
This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.
A Hybrid Artificial Neural Network-based Scheduling Knowledge Acquisition Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Weida; WANG Wei; LIU Wenjian
2006-01-01
It is a key issue that constructing successful knowledge base to satisfy an efficient adaptive scheduling for the complex manufacturing system. Therefore, a hybrid artificial neural network (ANN)-based scheduling knowledge acquisition algorithm is presented in this paper. We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid optimization method, in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search. The hybrid method was utilized to resolve the optimal attributes subset of manufacturing system and determine the optimal topology and parameters of ANN under different scheduling objectives; ANN was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal attributes subset, optimal ANN's topology and parameters. The experimental results demonstrate that the proposed algorithm produces significant performance improvements over other machine learning-based algorithms.
Comparison of Heuristics for Inhibitory Rule Optimization
Alsolami, Fawaz
2014-09-13
Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.
Itoh, Jyunpei; Yamamoto, Masayoshi; Funabiki, Shigeyuki
Electric power demand has an increasing tendency year by year. The fluctuation of the electric power causes further increase in the cost of the electric power facility and electricity charges. The development of the electric power-leveling systems (EPLS) using energy storage technology is desired to improve the electric power quality. The EPLS with a SMES is proposed as one of the countermeasures for the electric power quality improvement. However, the SMES is very expensive and it is difficult to decide the gains of the controller. It is essential in the practical use that the reduction of SMES capacity is realized. This paper proposes a new optimization method of the EPLS. The proposed algorithm is hybrid architecture with a combination of SimE (Simulated Evolution) and GA (Genetic Algorithms). The optimization of the EPLS can be achieved by the proposed hybrid algorithm compared to the SimE and the GA.
Heuristics in Global Combat Logistics Force Operational Planning
2010-03-01
maximizes deployed battle group on-station-time and endurance. The author presents a heuristic algorithm extension to the legacy CLF planning tool to plan...for completion, and can require 5 minutes to an hour just to find an initial feasible solution. On the contrary, a heuristic algorithm can provide initial feasible solutions in a matter of seconds.
Heuristic methods for shared backup path protection planning
DEFF Research Database (Denmark)
Haahr, Jørgen Thorlund; Stidsen, Thomas Riis; Zachariasen, Martin
2012-01-01
present heuristic algorithms and lower bound methods for the SBPP planning problem. Experimental results show that the heuristic algorithms are able to find good quality solutions in minutes. A solution gap of less than 3.5% was achieved for more than half of the benchmark instances (and a gap of less...
Heuristic methods for single link shared backup path protection
DEFF Research Database (Denmark)
Haahr, Jørgen Thorlund; Stidsen, Thomas Riis; Zachariasen, Martin
2014-01-01
heuristic algorithms and lower bound methods for the SBPP planning problem. Experimental results show that the heuristic algorithms are able to find good quality solutions in minutes. A solution gap of less than 3.5 % was achieved for 5 of 7 benchmark instances (and a gap of less than 11 % for the remaining...
Heuristic Procedures for 0-1 Integer Programming.
1987-03-01
A 30 60 0.021 0 ST B 30 60 0.010 0 ie 40 Chapter 4 Conclusions A heuristic algorithm aims at obtaining a very good feasible solution relatively...Department of Operations Research, Stanford University, February, 1977. 19. Ibaraki, T., Ohashi, T., and Mine, H. " A Heuristic Algorithm for Mixed
Detection of Defective Sensors in Phased Array Using Compressed Sensing and Hybrid Genetic Algorithm
Directory of Open Access Journals (Sweden)
Shafqat Ullah Khan
2016-01-01
Full Text Available A compressed sensing based array diagnosis technique has been presented. This technique starts from collecting the measurements of the far-field pattern. The system linking the difference between the field measured using the healthy reference array and the field radiated by the array under test is solved using a genetic algorithm (GA, parallel coordinate descent (PCD algorithm, and then a hybridized GA with PCD algorithm. These algorithms are applied for fully and partially defective antenna arrays. The simulation results indicate that the proposed hybrid algorithm outperforms in terms of localization of element failure with a small number of measurements. In the proposed algorithm, the slow and early convergence of GA has been avoided by combining it with PCD algorithm. It has been shown that the hybrid GA-PCD algorithm provides an accurate diagnosis of fully and partially defective sensors as compared to GA or PCD alone. Different simulations have been provided to validate the performance of the designed algorithms in diversified scenarios.
Systematic Design of High-performance Hybrid Feedback Algorithms
2015-06-24
C8. A. Subbaraman and A.R. Teel, “A Krasovskii- LaSalle function based recurrence principle for a class of stochastic hybrid systems”, Proceedings...variety of results on this topic. Our results on stochastic discrete-time (difference) inclusions include an invariance principle [J1], converse...public release. 12 and an ensuing invariance /recurrence principle [C10], [C8]. We considered hybrid systems with both stochastic flows and stochastic
Indian Academy of Sciences (India)
V K MANUPATI; G RAJYALAKSHMI; FELIX T S CHAN; J J THAKKAR
2017-03-01
This paper addresses a fuzzy mixed-integer non-linear programming (FMINLP) model by considering machine-dependent and job-sequence-dependent set-up times that minimize the total completion time,the number of tardy jobs, the total flow time and the machine load variation in the context of unrelated parallel machine scheduling (UPMS) problem. The above-mentioned multi-objectives were considered based on nonzero ready times, machine- and sequence-dependent set-up times and secondary resource constraints for jobs.The proposed approach considers unrelated parallel machines with inherent uncertainty in processing times and due dates. Since the problem is shown to be NP-hard in nature, it is a challenging task to find the optimal/nearoptimal solutions for conflicting objectives simultaneously in a reasonable time. Therefore, we introduced a new multi-objective-based evolutionary artificial immune non-dominated sorting genetic algorithm (AI-NSGA-II) to resolve the above-mentioned complex problem. The performance of the proposed multi-objective AI-NSGA-II algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventionalnon-dominated sorting genetic algorithm (CNSGA-II), and it is found that the proposed multi-objective-based hybrid meta-heuristic produces high-quality solutions. Finally, the results obtained from benchmark instances and randomly generated instances as test problems evince the robust performance of the proposed multiobjective algorithm.
DEFF Research Database (Denmark)
Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar
2017-01-01
, 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...... and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm...... optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality....
A New Hybrid Algorithm to Solve Winner Determination Problem in Multiunit Double Internet Auction
Directory of Open Access Journals (Sweden)
Mourad Ykhlef
2015-01-01
Full Text Available Solving winner determination problem in multiunit double auction has become an important E-business task. The main issue in double auction is to improve the reward in order to match the ideal prices and quantity and make the best profit for sellers and buyers according to their bids and predefined quantities. There are many algorithms introduced for solving winner in multiunit double auction. Conventional algorithms can find the optimal solution but they take a long time, particularly when they are applied to large dataset. Nowadays, some evolutionary algorithms, such as particle swarm optimization and genetic algorithm, were proposed and have been applied. In order to improve the speed of evolutionary algorithms convergence, we will propose a new kind of hybrid evolutionary algorithm that combines genetic algorithm (GA with particle swarm optimization (PSO to solve winner determination problem in multiunit double auction; we will refer to this algorithm as AUC-GAPSO.
一种最小创建者集合的构建算法%Heuristic Algorithm for the Minimum Founder Set Reconstructive Problem
Institute of Scientific and Technical Information of China (English)
吴璟莉; 刘广海
2012-01-01
最小创建者集合问题(the Minimum Founder Set problem,MFS)是求解重组体的嵌合体结构或创建者集合的有效模型,提出一种求解访问题的构造性启发式算法H-MFS.在嵌合体中的最小片断长度须不小于L的模型约束下,各重组体的前L列之间及最后L列之间均不能出现断点.基于这个思想,该算法将创建者序列的基因位点分成三个部分分别构建.采用两组真实的生物数据对算法进行测试分析:1.Kreitman的果蝇乙醇脱氢酶数据；2.国际人类基因组单体型图计划发布的CEU种群样本.实验结果显示,该算法能快速有效地求解MFS问题,并且当重组体的SNP位点个数取值较大时,H-MFS仍具有较高的执行效率,有很好的实用价值.%The minimum founder set problem (MFS) is an effective computation model for constructing the mosaic patter or founder set from a set of recombinant sequences. In this paper, a practical constructive heuristic algorithm H-MFS is presented for solving the problem. Based on the intuition that there can not be any breakpoints among the fust (resp. the last) L columns under the constraints that the minimum fragment length of the mosaic must not less than L. H-MFS partitions the sites of founders into three parts and reconstructs them respectively.Experiments were conducted by using two sets of real biological data; L Kreitman' ADH data..2. the CEU population sample released by the International HapMap Project. The results indicate that H-MFS can solve the minimum founder set problem fast and effectively. Furthermore, when the number of recombinants and SNP sites grows large, H-MFS is still able to find satisfied solution to this problem very quickly. Hence it is practical in realistic applications.
A hybrid algorithm for Caputo fractional differential equations
Salgado, G. H. O.; Aguirre, L. A.
2016-04-01
This paper is concerned with the numerical solution of fractional initial value problems (FIVP) in sense of Caputo's definition for dynamical systems. Unlike for integer-order derivatives that have a single definition, there is more than one definition of non integer-order derivatives and the solution of an FIVP is definition-dependent. In this paper, the chief differences of the main definitions of fractional derivatives are revisited and a numerical algorithm to solve an FIVP for Caputo derivative is proposed. The main advantages of the algorithm are twofold: it can be initialized with integer-order derivatives, and it is faster than the corresponding standard algorithm. The performance of the proposed algorithm is illustrated with examples which suggest that it requires about half the computation time to achieve the same accuracy than the standard algorithm.
SAR Image Segmentation Based On Hybrid PSOGSA Optimization Algorithm
Directory of Open Access Journals (Sweden)
Amandeep Kaur
2014-09-01
Full Text Available Image segmentation is useful in many applications. It can identify the regions of interest in a scene or annotate the data. It categorizes the existing segmentation algorithm into region-based segmentation, data clustering, and edge-base segmentation. Region-based segmentation includes the seeded and unseeded region growing algorithms, the JSEG, and the fast scanning algorithm. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR images is still a challenging problem. We proposed a fast SAR image segmentation method based on Particle Swarm Optimization-Gravitational Search Algorithm (PSO-GSA. In this method, threshold estimation is regarded as a search procedure that examinations for an appropriate value in a continuous grayscale interval. Hence, PSO-GSA algorithm is familiarized to search for the optimal threshold. Experimental results indicate that our method is superior to GA based, AFS based and ABC based methods in terms of segmentation accuracy, segmentation time, and Thresholding.
The Verification of Hybrid Image Deformation algorithm for PIV
Directory of Open Access Journals (Sweden)
Novotný Jan
2016-06-01
Full Text Available The aim of this paper was to test a newly designed algorithm for more accurate calculation of the image displacement of seeding particles when taking measurement using the Particle Image Velocimetry method. The proposed algorithm is based on modification of a classical iterative approach using a three-point subpixel interpolation and method using relative deformation of individual areas for accurate detection of signal peak position. The first part briefly describes the tested algorithm together with the results of the performed synthetic tests. The other part describes the measurement setup and the overall layout of the experiment. Subsequently, a comparison of results of the classical iterative scheme and our designed algorithm is carried out. The conclusion discusses the benefits of the tested algorithm, its advantages and disadvantages.
A hybrid evolutionary algorithm for distribution feeder reconfiguration
Institute of Scientific and Technical Information of China (English)
Taher; NIKNAM; Ehsan; AZAD; FARSANI
2010-01-01
This paper presents a new method to reduce the distribution system loss by feeder reconfiguration.This new method combines self-adaptive particle swarm optimization(SAPSO) with shuffled frog-leaping algorithm(SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration(DFR).In PSO algorithm,appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort.Thus,a self-adaptive framework is proposed to improve the robustness of PSO.In SAPSO the learning factors of PSO coevolve with the particles.SFLA is combined with the SAPSO algorithm to improve its performance.The proposed algorithm is tested on two distribution test networks.The results of simulation show that the proposed algorithm is very powerful and guarantees to obtain the global optimization in minimum time.
Directory of Open Access Journals (Sweden)
Daniel Morillo-Torres
2015-01-01
Full Text Available En este artículo se aborda el problema de Programación de Tareas con Recursos Restringidos (RCPSP. Para su solución, se desarrolla y se implementa una metodología híbrida que usa como base un algoritmo de Ramificación y Acotamiento con potentes reglas de dominancia, y se combina con cuatro heurísticas determinísticas cuyo objetivo es truncar ramas del árbol de búsqueda, pero, a su vez, minimizar la probabilidad de descartar ramales que contengan soluciones óptimas. En esencia, estas estrategias permiten la repartición de iteraciones en forma mayoritaria y organizada en las regiones más promisorias usando, únicamente, subconjuntos que tengan características similares o iguales a las de las soluciones óptimas en cada nivel del árbol, garantizando así una amplia exploración dentro de la región factible y al mismo tiempo una buena explotación. Finalmente se analiza el desempeño del algoritmo desarrollado mediante la solución de algunos problemas de la librería de prueba PSPLIB.
Design of Passive Analog Electronic Circuits Using Hybrid Modified UMDA algorithm
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J. Slezak
2015-04-01
Full Text Available Hybrid evolutionary passive analog circuits synthesis method based on modified Univariate Marginal Distribution Algorithm (UMDA and a local search algorithm is proposed in the paper. The modification of the UMDA algorithm which allows to specify the maximum number of the nodes and the maximum number of the components of the synthesized circuit is proposed. The proposed hybrid approach efficiently reduces the number of the objective function evaluations. The modified UMDA algorithm is used for synthesis of the topology and the local search algorithm is used for determination of the parameters of the components of the designed circuit. As an example the proposed method is applied to a problem of synthesis of the fractional capacitor circuit.
Adaptive Identification of Logging Lithology Based on VPSO-ENN Hybrid Algorithm
Institute of Scientific and Technical Information of China (English)
GUO Jian; WANG Yuan-han; LI Yin-ping
2008-01-01
Particle swarm optimization (PSO) was modified by variation method of particle velocity, and a variation PSO (VPSO) algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network (ENN) to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm (GA) and BP neural network (GA-BP), VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification.
Identification of vibration loads on hydro generator by using hybrid genetic algorithm
Institute of Scientific and Technical Information of China (English)
Shouju Li; Yingxi Liu
2006-01-01
Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators.An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm.From the measured dynamic responses of a hydro generator,an appropriate estimation algorithm is needed to identify the loading parameters,including the main frequencies and amplitudes of vibrating forces.In order to identify parameters in an efficient and robust manner,an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy.The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification.In which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.
A Fast Hybrid Algorithm of Global Optimization for Feedforward Neural Networks
Institute of Scientific and Technical Information of China (English)
JIANG Minghu; ZHANG Bo; ZHU Xiaoyan; JINAG Mingyan
2001-01-01
This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN).The effect of inexact line search on conjugacy was studied, based on which a generalized conjugate gradient method was proposed, showing global convergence for error backpagation of MLFNN. It overcomes the drawback of conventional BP and Polak-Ribieve conjugate gradient algorithms that maybe plunge into local minima. The hybrid algorithm's recognition rate is higher than that of Polak-Ribieve algorithm and convergence BP for test data, its training time is less than that of Fletcher-Reeves algorithm and far less than that of convergence BP, and it has a less complicated and stronger robustness to real speech data.
Heuristic algorithm for planning problem of fishing port sheltered from typhoon%避风型渔港规划问题的启发式算法研究
Institute of Scientific and Technical Information of China (English)
于红; 冯艳红; 李放; 孙庚; 栾曙光
2012-01-01
The rational planning of fishing port sheltered from typhoon plays an important role in reducing fishery loss from typhoon. The mathematie model of fishing port planning in the southeastern coastal region is built. The complexity of the model is analyzed. Following the principle of " Fishing vessles should shelter from typhoon in the nearest fishing port" , the paper puts forward a heuristic algorithm, which firstly considers the shortest time to return fishing port. The actual data of fishing ports and vessels in the southeast region are utilized in calculation and the survey data are used to envaluate computing result of heuristic algorithm. The result shows that the heuristic algorithm carries accepable accuracy.%避风型渔港的合理规划对减少台风造成渔业的损失具有重要意义.建立了中国东南沿海避风型渔港规划问题的数学模型,并对该模型的复杂度进行了分析,利用渔船就近避风的原则,提出了一种优先考虑最短回港时间的启发式算法,并用中国东南沿海实际的渔港及渔船数据进行了计算,用调研数据对算法的运行效果进行了评估.试验结果表明,本算法具有较好的准确度.
Directory of Open Access Journals (Sweden)
Santosh Kumar Singh
2017-06-01
Full Text Available This paper presents a new hybrid method based on Gravity Search Algorithm (GSA and Recursive Least Square (RLS, known as GSA-RLS, to solve the harmonic estimation problems in the case of time varying power signals in presence of different noises. GSA is based on the Newton’s law of gravity and mass interactions. In the proposed method, the searcher agents are a collection of masses that interact with each other using Newton’s laws of gravity and motion. The basic GSA algorithm strategy is combined with RLS algorithm sequentially in an adaptive way to update the unknown parameters (weights of the harmonic signal. Simulation and practical validation are made with the experimentation of the proposed algorithm with real time data obtained from a heavy paper industry. A comparative performance of the proposed algorithm is evaluated with other recently reported algorithms like, Differential Evolution (DE, Particle Swarm Optimization (PSO, Bacteria Foraging Optimization (BFO, Fuzzy-BFO (F-BFO hybridized with Least Square (LS and BFO hybridized with RLS algorithm, which reveals that the proposed GSA-RLS algorithm is the best in terms of accuracy, convergence and computational time.
HYBRID CHRIPTOGRAPHY STREAM CIPHER AND RSA ALGORITHM WITH DIGITAL SIGNATURE AS A KEY
Directory of Open Access Journals (Sweden)
Grace Lamudur Arta Sihombing
2017-03-01
Full Text Available Confidentiality of data is very important in communication. Many cyber crimes that exploit security holes for entry and manipulation. To ensure the security and confidentiality of the data, required a certain technique to encrypt data or information called cryptography. It is one of the components that can not be ignored in building security. And this research aimed to analyze the hybrid cryptography with symmetric key by using a stream cipher algorithm and asymmetric key by using RSA (Rivest Shamir Adleman algorithm. The advantages of hybrid cryptography is the speed in processing data using a symmetric algorithm and easy transfer of key using asymmetric algorithm. This can increase the speed of transaction processing data. Stream Cipher Algorithm using the image digital signature as a keys, that will be secured by the RSA algorithm. So, the key for encryption and decryption are different. Blum Blum Shub methods used to generate keys for the value p, q on the RSA algorithm. It will be very difficult for a cryptanalyst to break the key. Analysis of hybrid cryptography stream cipher and RSA algorithms with digital signatures as a key, indicates that the size of the encrypted file is equal to the size of the plaintext, not to be larger or smaller so that the time required for encryption and decryption process is relatively fast.
Energy Technology Data Exchange (ETDEWEB)
Delbem, Alexandre C.B.; Bretas, Newton G. [Sao Paulo Univ., Sao Carlos, SP (Brazil). Dept. de Engenharia Eletrica; Carvalho, Andre C.P.L.F. [Sao Paulo Univ., Sao Carlos, SP (Brazil). Dept. de Ciencias de Computacao e Estatistica
1996-11-01
A search approach using fuzzy heuristics and a neural network parameter was developed for service restoration of a distribution system. The goal was to restore energy for an un-faulted zone after a fault had been identified and isolated. The restoration plan must be carried out in a very short period. However, the combinatorial feature of the problem constrained the application of automatic energy restoration planners. To overcome this problem, an heuristic search approach using fuzzy heuristics was proposed. As a result, a genetic algorithm approach was developed to achieve the optimal energy restoration plan. The effectiveness of these approaches were tested in a simplified distribution system based on the complex distribution system of Sao Carlos city, Sao Paulo State - southeast Brazil. It was noticed that the genetic algorithm provided better performance than the fuzzy heuristic search in this problem. 11 refs., 10 figs.
A Location-Aware Vertical Handoff Algorithm for Hybrid Networks
Mehbodniya, Abolfazl
2010-07-01
One of the main objectives of wireless networking is to provide mobile users with a robust connection to different networks so that they can move freely between heterogeneous networks while running their computing applications with no interruption. Horizontal handoff, or generally speaking handoff, is a process which maintains a mobile user\\'s active connection as it moves within a wireless network, whereas vertical handoff (VHO) refers to handover between different types of networks or different network layers. Optimizing VHO process is an important issue, required to reduce network signalling and mobile device power consumption as well as to improve network quality of service (QoS) and grade of service (GoS). In this paper, a VHO algorithm in multitier (overlay) networks is proposed. This algorithm uses pattern recognition to estimate user\\'s position, and decides on the handoff based on this information. For the pattern recognition algorithm structure, the probabilistic neural network (PNN) which has considerable simplicity and efficiency over existing pattern classifiers is used. Further optimization is proposed to improve the performance of the PNN algorithm. Performance analysis and comparisons with the existing VHO algorithm are provided and demonstrate a significant improvement with the proposed algorithm. Furthermore, incorporating the proposed algorithm, a structure is proposed for VHO from the medium access control (MAC) layer point of view. © 2010 ACADEMY PUBLISHER.
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.
Novel hybrid genetic algorithm for progressive multiple sequence alignment.
Afridi, Muhammad Ishaq
2013-01-01
The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution.
DEFF Research Database (Denmark)
Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup
2011-01-01
The paper presents a hybrid Genetic and Simulated Annealing algorithm for implementing Chordal Ring structure in optical backbone network. In recent years, topologies based on regular graph structures gained a lot of interest due to their good communication properties for physical topology...... of the networks. There have been many use of evolutionary algorithms to solve the problems which are in combinatory complexity nature, and extremely hard to solve by exact approaches. Both Genetic and Simulated annealing algorithms are similar in using controlled stochastic method to search the solution....... The paper combines the algorithms in order to analyze the impact of implementation performance....
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
Directory of Open Access Journals (Sweden)
Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
A Hybrid Continuous Max-Sum Algorithm for Decentralised Coordination
Voice, Thomas; Stranders, Ruben; Rogers, Alex; Jennings, Nick
2010-01-01
Recent advances in decentralised coordination of multiple agents have led to the proposal of the max-sum algorithm for solving distributed constraint optimisation problems (DCOPs). The max-sum algorithm is fully decentralised, converges to optimality for problems with acyclic constraint graphs and otherwise performs well in empirical studies. However, it requires agents to have discrete state spaces, which are of practical size to conduct repeated searches over. In contrast, there are decentr...
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
Kobayashi, Takahisa; Simon, Donald L.
2001-01-01
In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
DEFF Research Database (Denmark)
Wang, Yong; Cai, Zixing; Zhou, Yuren
2009-01-01
A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...
Hybrid and dependent task scheduling algorithm for on-board system software
Institute of Scientific and Technical Information of China (English)
魏振华; 洪炳熔; 乔永强; 蔡则苏; 彭俊杰
2003-01-01
In order to solve the hybrid and dependent task scheduling and critical source allocation problems, atask scheduling algorithm has been developed by first presenting the tasks, and then describing the hybrid anddependent scheduling algorithm and deriving the predictable schedulability condition. The performance of thisagorithm was evaluated through simulation, and it is concluded from the evaluation results that the hybrid taskscheduling subalgorithm based on the comparison factor can be used to solve the problem of aperiodic task beingblocked by periodic task in the traditional operating system for a very long time, which results in poor schedu-ling predictability; and the resource allocation subalgorithm based on schedulability analysis can be used tosolve the problems of critical section conflict, ceiling blocking and priority inversion; and the scheduling algo-rithm is nearest optimal when the abortable critical section is 0.6.
DESIGN OF A NEW SECURITY PROTOCOL USING HYBRID CRYPTOGRAPHY ALGORITHMS
Directory of Open Access Journals (Sweden)
Dr.S.Subasree and Dr.N.K.Sakthivel
2010-02-01
Full Text Available A Computer Network is an interconnected group of autonomous computing nodes, which use a well defined, mutually agreed set of rules and conventions known as protocols, interact with one-another meaningfully and allow resource sharing preferably in a predictable and controllable manner. Communication has a major impact on today’s business. It is desired to communicate data with high security. Security Attacks compromises the security and hence various Symmetric and Asymmetric cryptographic algorithms have been proposed to achieve the security services such as Authentication, Confidentiality, Integrity, Non-Repudiation and Availability. At present, various types of cryptographic algorithms provide high security to information on controlled networks. These algorithms are required to provide data security and users authenticity. To improve the strength of these security algorithms, a new security protocol for on line transaction can be designed using combination of both symmetric and asymmetric cryptographic techniques. This protocol provides three cryptographic primitives such as integrity, confidentiality and authentication. These three primitives can be achieved with the help of Elliptic Curve Cryptography, Dual-RSA algorithm and Message Digest MD5. That is it uses Elliptic Curve Cryptography for encryption, Dual-RSA algorithm for authentication and MD-5 for integrity. This new security protocol has been designed for better security with integrity using a combination of both symmetric and asymmetric cryptographic techniques.
Gigerenzer, Gerd; Gaissmaier, Wolfgang
2011-01-01
As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.
A Heuristic Hierarchical Scheme for Academic Search and Retrieval
DEFF Research Database (Denmark)
Amolochitis, Emmanouil; Christou, Ioannis T.; Tan, Zheng-Hua
2013-01-01
We present PubSearch, a hybrid heuristic scheme for re-ranking academic papers retrieved from standard digital libraries such as the ACM Portal. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score...
Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline
2013-04-01
Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.
Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk
2014-01-01
We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.
An immune-tabu hybrid algorithm for thermal unit commitment of electric power systems
Institute of Scientific and Technical Information of China (English)
Wei LI; Hao-yu PENG; Wei-hang ZHU; De-ren SHENG; Jian-hong CHEN
2009-01-01
This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment (TUC) problem in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in modern power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the objective function of the TUC problem is considered as an antigen and the solutions are considered as antibodies,which are determined by the affinity computation. The code length of an antibody is shortened by encoding the continuous operating time, and the optimum searching speed is improved. Each feasible individual in the immune algorithm (IA) is used as the initial solution of the tabu search (TS) algorithm after certain generations of IA iteration. As examples, the proposed method has been applied to several thermal unit systems for a period of 24 h. The computation results demonstrate the good global optimum searching performance of the proposed immune-tabu hybrid algorithm. The presented algorithm can also be used to solve other optimization problems in fields such as the chemical industry and the power industry.
A Hybrid Constructive Algorithm for Single-Layer Feedforward Networks Learning.
Wu, Xing; Rózycki, Paweł; Wilamowski, Bogdan M
2015-08-01
Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determining network size and training the parameters. Most current algorithms could not be satisfactory to both sides. Some algorithms focused on construction and only tuned part of the parameters, which may not be able to achieve a compact network. Other gradient-based optimization algorithms focused on parameters tuning while the network size has to be preset by the user. Therefore, trial-and-error approach has to be used to search the optimal network size. Because results of each trial cannot be reused in another trial, it costs much computation. In this paper, a hybrid constructive (HC)algorithm is proposed for SLFN learning, which can train all the parameters and determine the network size simultaneously. At first, by combining Levenberg-Marquardt algorithm and least-square method, a hybrid algorithm is presented for training SLFN with fixed network size. Then,with the hybrid algorithm, an incremental constructive scheme is proposed. A new randomly initialized neuron is added each time when the training entrapped into local minima. Because the training continued on previous results after adding new neurons, the proposed HC algorithm works efficiently. Several practical problems were given for comparison with other popular algorithms. The experimental results demonstrated that the HC algorithm worked more efficiently than those optimization methods with trial and error, and could achieve much more compact SLFN than those construction algorithms.