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An Effective DE-EDA for Permutation Flow-shop Scheduling Problem

An Effective DE-EDA for Permutation Flow-shop Scheduling Pro...

IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Li, Zuo-cheng Guo, Qingxin Tang, Lixin Northeastern Univ Inst Ind Engn & Logist Optimizat Shenyang Peoples R China

ISBN: (纸本)9781509006229

Aiming at the permutation flow-shop scheduling problem (PFSSP) with makespan criterion, a combination algorithm based on differential evolution (DE) and estimation of distribution algorithm (EDA), namely DE-EDA, is proposed. Firstly, DE-EDA combines the probability-dependent macro information extracted by EDA and the individual-dependent micro information obtained by DE to execute the exploration, which is helpful in guiding the global search to explore promising solutions. Secondly, in order to make DE well suited to solve PFSSP, a convert rule named smallest-ranked-value (SRV) is designed to generate the discrete job permutations from the continuous values. Thirdly, a sequence-learning-based Bayes posterior probability is presented to estimate EDA's probability model and sample new solutions, so that the global information of promising search regions can be learned precisely. In addition, a simple but effective two-stage local search is embedded into DE-EDA to perform the exploitation, and thereafter numerous potential solution(s) with relative better fitness can be exploited in some narrow search regions. Finally, simulation experiments and comparisons based on 29 well-known benchmark instances demonstrate the effectiveness of the proposed DE-EDA.

关键词： differential evolution estimation of distribution algorithm probability model permutation flow-shop scheduling problem makespan

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EDA-based Decomposition Approach for Binary LSGO Problems 8

EDA-based Decomposition Approach for Binary LSGO Problems

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8th International Joint Conference on Computational Intelligence

作者： Sopov, Evgenii Siberian State Aerosp Univ Dept Syst Anal & Operat Res Krasnoyarsk Russia

ISBN: (纸本)9789897582011

In recent years many real-world optimization problems have had to deal with growing dimensionality. Optimization problems with many hundreds or thousands of variables are called large-scale global optimization (LSGO) problems. Many well-known real-world LSGO problems are not separable and are complex for detailed analysis, thus they are viewed as the black-box optimization problems. The most advanced algorithms for LSGO are based on cooperative coevolution schemes using the problem decomposition. These algorithms are mainly proposed for the real-valued search space and cannot be applied for problems with discrete or mixed variables. In this paper a novel technique is proposed, that uses a binary genetic algorithm as the core technique. The estimation of distribution algorithm (EDA) is used for collecting statistical data based on the past search experience to provide the problem decomposition by fixing genes in chromosomes. Such an EDA-based decomposition technique has the benefits of the random grouping methods and the dynamic learning methods. The EDA-based decomposition GA using the island model is also discussed. The results of numerical experiments for benchmark problems from the CEC competition are presented and discussed. The experiments show that the approach demonstrates efficiency comparable to other advanced techniques.

关键词： Large-Scale Global Optimization Problem Decomposition estimation of distribution algorithm Binary Genetic algorithm

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Distributed estimation of distribution algorithms for continuous optimization: How does the exchanged information influence their behavior?

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INFORMATION SCIENCES 2014年 268卷 231-254页

作者： Muelas, Santiago Mendiburu, Alexander LaTorre, Antonio Pena, Jose-Maria Univ Politecn Madrid Dept Comp Syst Architecture & Technol E-28040 Madrid Spain Univ Basque Country Dept Comp Architecture & Technol Intelligent Syst Grp San Sebastian Spain CSIC Inst Cajal Madrid Spain

One of the most promising areas in which probabilistic graphical models have shown an incipient activity is the field of heuristic optimization and, in particular, in estimation of distribution algorithms. Due to their inherent parallelism, different research lines have been studied trying to improve estimation of distribution algorithms from the point of view of execution time and/or accuracy. Among these proposals, we focus on the so-called distributed or island-based models. This approach defines several islands (algorithms instances) running independently and exchanging information with a given frequency. The information sent by the islands can be either a set of individuals or a probabilistic model. This paper presents a comparative study for a distributed univariate estimation of distribution algorithm and a multivariate version, paying special attention to the comparison of two alternative methods for exchanging information, over a wide set of parameters and problems - the standard benchmark developed for the IEEE Workshop on Evolutionary algorithms and other Metaheuristics for Continuous Optimization Problems of the ISDA 2009 Conference. Several analyses from different points of view have been conducted to analyze both the influence of the parameters and the relationships between them including a characterization of the configurations according to their behavior on the proposed benchmark. (c) 2014 Published by Elsevier Inc.

关键词： Evolutionary computation Graphical model estimation of distribution algorithm Island model Continuous optimization

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Quality Analysis of Discretization Methods for estimation of distribution algorithms

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2014年第5期E97D卷 1312-1323页

作者： Chen, Chao-Hong Chen, Ying-ping Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

estimation of distribution algorithms (EDAs). since they were introduced, have been successfully used to solve discrete optimization problems and hence proven to be an effective methodology for discrete optimization. To enhance the applicability of EDAs, researchers started to integrate EDAs with discretization methods such that the EDAs designed for discrete variables can be made capable of solving continuous optimization problems. In order to further our understandings of the collaboration between EDAs and discretization methods, in this paper, we propose a quality measure of discretization methods for EDAs. We then utilize the proposed quality measure to analyze three discretization methods: fixed-width histogram (FWH). fixed-height histogram (FHH), and greedy random split (GRS). Analytical measurements are obtained for FHH and FWH, and sampling measurements are conducted for FHH. FWH, and GRS. Furthermore, we integrate Bayesian optimization algorithm (BOA), a representative EDA, with the three discretization methods to conduct experiments and to observe the performance difference. A good agreement is reached between the discretization quality measurements and the numerical optimization results. The empirical results show that the proposed quality measure can be considered as an indicator of the suitability for a discretization method to work with EDAs.

关键词： quality analysis discretization distortion fixed-width histogram fixed-height histogram greedy random split estimation of distribution algorithm Bayesian optimization algorithm

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CONTROLLING CHAOS BY AN IMPROVED estimation of distribution algorithm

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MATHEMATICAL & COMPUTATIONAL APPLICATIONS 2010年第5期15卷 866-871页

作者： Huang, Xingli Jia, Peifa Liu, Bo Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Ctr Forecasting Sci Beijing 100190 Peoples R China

Control and synchronization of chaotic systems are important issues in nonlinear sciences. This paper proposes an effective estimation of distribution algorithm (EDA)-based memetic algorithm (MA) to direct the orbits of discrete chaotic dynamical systems as well as to synchronize chaotic systems, which could be formulated as complex multi-modal numerical optimization problems. In EDA-based MA (EDAMA), both EDA-based searching operators and simulated annealing (SA) based local searching operators are designed to balance the exploration and exploitation abilities. On the other hand, global information provided by EDA is combined with local information from SA to create better solutions. In particular, to enrich the searching behaviors and to avoid premature convergence, SA-based local search is designed and incorporated into EDAMA. To balance the exploration and exploitation abilities, after the standard EDA-based searching operation, SA-based local search is probabilistically applied to some good solutions selected by using a roulette wheel mechanism with a specified probability. Numerical simulations based on Henon Map demonstrate the effectiveness and efficiency of EDAMA, and the effects of some parameters are investigated as well.

关键词： estimation of distribution algorithm Chaotic dynamics

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Solving TSP Problems with Hybrid estimation of distribution algorithms

Solving TSP Problems with Hybrid Estimation of Distribution ...

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10th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Xiaoxia Ma, Yunyong Univ Sci & Technol Liaoning Coll Software Engn Anshan Peoples R China

ISBN: (纸本)9783319093338;9783319093321

In this paper, a hybrid estimation of distribution algorithms is proposed to solve traveling salesman problem, and a greedy algorithm is used to improve the quality of the initial population. It sets up a Bayes probabilistic model of the TSP. The roulette method is adopted to generate the new population. In order to prevent falling into local optimum, the mutation and limit were proposed to enhance the exploitation ability. At the same time, three new neighborhood search strategies and the second element optimization method are presented to enhance the ability of the local search. The simulation results and comparisons based on benchmarks validate the efficiency of the proposed algorithm.

关键词： estimation of distribution algorithm Traveling salesman problem Neighborhood search

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An estimation of distribution algorithm for resource-constrained project scheduling problem

An estimation of distribution algorithm for resource-constra...

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2010 Chinese Control and Decision Conference

作者： Chen Fang,Ling Wang,Ye Xu TNList,Department of Automation,Tsinghua University,Beijing,100084

An estimation of distribution algorithm(EDA) is proposed to solve resource-constrained project scheduling problem(RCPSP).In the EDA,individual is encoded based on the extended active list,and a probability model of the distribution for each activity in a project and its updating mechanism are *** algorithm determines the initial probability matrix according to an initial set of solutions generated by the regret-based sampling method and priority rule,and decodes the individuals by using serial schedule generation ***,a permutation based local search method is incorporated into the algorithm to enhance the exploitation ability so as to further improve the searching *** results based on benchmarks and comparisons with some existing algorithms demonstrate the feasibility and effectiveness of our proposed EDA.

关键词： estimation of distribution algorithm Resource-constrained project scheduling Probability model

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Histogram-Based estimation of distribution algorithm with RPCL Clustering in Continuous Domain

Histogram-Based Estimation of Distribution Algorithm with RP...

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Hong WU Wei-ping WANG School of Information System and Management National University of Defense Technology Chang Sha P.R.China

Designing efficient estimation of distribution algorithms for optimizing complex continuous problems is still a challenging task. Nowadays, histogram probabilistic model has become a hot topic in the field of estimation of distribution algorithms because of its intrinsic multimodality that makes it proper to describe the solution distribution of complex and multimodal continuous problems. To make histogram probabilistic model more efficiently explore and exploit the search space, rival penalized competitive learning (RPCL) clustering was brought into the algorithm, so that the algorithm could use the knowledge about distribution of values belong to each span. Experimental results showed that the improved algorithm in this paper can give comparable with or better performance than those improved algorithms.

关键词： estimation of distribution algorithm histogram probabilistic model RPCL clustering global optimum elitist strategy

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A New Fuzzy Classification Method Based estimation of distribution algorithm

A New Fuzzy Classification Method Based Estimation of Distri...

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2010 International Conference on Services Science, Management and Engineering (SSME 2010)

作者： GAO Xiao-xia The College of computer science and technology,Civil Aviation University of China Tianjin,China HUO Wei-gang The College of computer science and technology,Civil Aviation University of China Tianjin,China

In order to improve fuzzy classification model's accuracy and interpretability,a fuzzy classification method based on estimation of distribution algorithm was *** first constructs initial fuzzy rule set using Apriori principle in the field of data mining,then builds fuzzy classification model by extracting rule from initial fuzzy rule set automatically through Pittsburgh-style binary coding method and UMDA(Univariate Marginal distribution algorithm) estimation of distribution *** experiment on benchmark datasets show that the proposed approach has better performance than fuzzy classification model based on genetic algorithm

关键词： fuzzy classification estimation of distribution algorithm UMDA fuzzy rule

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Global Optimization Using estimation of distribution algorithm

Global Optimization Using Estimation of Distribution Algorit...

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2010 2nd International Conference on Intellectual Technology in Industrial Practice (ITIP2010)

作者： YANG Xiuxia, ZHAO Hongchao, ZHANG Yi Department of Control Engineering Naval Aeronautical and Astronautical University Yantai, China

estimation of distribution algorithms (EDA) is a new stochastic optimization algorithm in the field of evolutionary computation. Aiming at the disadvantages of EDA for multi-peak function optimization falling into local optimization easily and not retaining some excellent models, combining with genetic algorithm, the improved EDA is provided. The crossover and mutation operations are added. To maintain the diversity of population, the chaotic initialization is introduced and the individual diversity is adjusted based on the individual density. The new population is produced according to the probability estimation model and the elitist is reserved. A fast parallel EDA with the capacity of global search is designed. Simulation results show that the algorithm can quickly find the global extreme points.

关键词： estimation of distribution algorithm chaotic sequences individual density global optimization

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