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An estimation-of-distribution algorithm Approach to Redundancy Allocation Problem for a High-Security System

IEEJ JOURNAL OF INDUSTRY APPLICATIONS 2014年第4期3卷 358-367页

作者： Melo, Haydee Watada, Junzo Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu ku 2-7Hibikino Kitakyushu Fukuoka 8080135 Japan

Reliability is an issue that has recently captivated the attention of researchers. Its goal is to develop new techniques to design more reliable systems, which can operate without failing during operation. A result of this growth in technology is an increase in the complexity and susceptibility of more complex systems. The principal objective of redundancy allocation is to maximize the availability of a system while reducing the cost, volume or weight. This paper proposes an estimation-of-distribution algorithm (EDA) approach as a new meta-heuristic method to solve a redundancy allocation problem (RAP) for a high security control system.

关键词： reliability availability maintainability redundancy estimation of distribution algorithm PLC

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A hybrid optimization algorithm based on chaotic differential evolution and estimation of distribution

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COMPUTATIONAL & APPLIED MATHEMATICS 2017年第1期36卷 433-458页

作者： Zhao, Fuqing Shao, Zhongshi Wang, Junbiao Zhang, Chuck Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Northwestern Polytech Univ Key Lab Contemporary Design & Integrated Mfg Tech Minist Educ Xian 710072 Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

estimation of distribution algorithms (EDAs) and differential evolution (DE) are two types of evolutionary algorithms. The former has fast convergence rate and strong global search capability, but is easily trapped in local optimum. The latter has good local search capability with slower convergence speed. Therefore, a new hybrid optimization algorithm which combines the merits of both algorithms, a hybrid optimization algorithm based on chaotic differential evolution and estimation of distribution (cDE/EDA) was proposed. Due to its effective nature of harmonizing the global search of EDA with the local search of DE, the proposed algorithm can discover the optimal solution in a fast and reliable manner. Chaotic policy was used to strengthen the search ability of DE. Meantime the global convergence of algorithm was analyzed with the aid of limit theorem of monotone bounded sequence. The proposed algorithm was tested through a set of typical benchmark problems. The results demonstrate the effectiveness and efficiency of the proposed cDE/EDA algorithm.

关键词： Hybrid optimization estimation of distribution algorithm Chaotic differential evolution algorithm Convergence Global optimization

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Immune algorithm Combined with estimation of distribution for Traveling Salesman Problem

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2016年第Sup1期11卷 S142-S154页

作者： Xu, Zhe Wang, Yirui Li, Sheng Liu, Yanting Todo, Yuki Gao, Shangce Toyama Univ Fac Engn Toyama 9308555 Japan Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Taizhou Univ Coll Comp Sci & Technol Taizhou 225300 Jiangsu Peoples R China Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

This paper describes an artificial immune algorithm (IA) combined with estimation of distribution algorithm (EDA), named IA-EDA, for the traveling salesman problem (TSP). Two components are incorporated in IA-EDA to further improve the performance of the conventional IA. First, aiming to strengthen the information exchange during different solutions, two kinds of EDAs involving univariate marginal distribution algorithm and population-based incremental learning are altered based on the permutation representation of TSP. It is expected that new promising candidate solutions can be sampled from the constructed probabilistic model of EDA. Second, a heuristic refinement local search operator is proposed to repair the infeasible solutions sampled by EDA. Therefore, IA-EDA can alleviate the deficiencies of the conventional IA and can find better solutions for TSP by well balancing the exploitation and exploration of the search. Experiments are conducted based on a number of benchmark instances with size up to 100 000 cities. Simulation results show that IA-EDA is effective for improving the performance of the conventional IA and can produce better or competitive solutions than other hybrid algorithms. (C) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： estimation of distribution algorithm immune algorithm probabilistic model traveling salesman problem hybridization

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Improving the artificial bee colony algorithm with a proprietary estimation of distribution mechanism for protein-ligand docking

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APPLIED SOFT COMPUTING 2024年 161卷

作者： Song, Shuangbao Tang, Cheng Song, Zhenyu Qu, Jia Chen, Xingqian Changzhou Univ Sch Comp Sci & Artificial Intelligence Changzhou 213164 Peoples R China Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan Taizhou Univ Coll Informat Engn Taizhou 225300 Peoples R China Jiangsu Univ Technol Sch Comp Engn Changzhou 213001 Peoples R China

The protein-ligand docking problem plays an essential role in structure -based drug design. The challenge for a protein-ligand docking method is how to execute an efficient conformational search to explore a well -designed scoring function. In this study, we improved the artificial bee colony (ABC) algorithm and proposed an approach called ABC-EDM to solve the protein-ligand docking problem. ABC-EDM employs the scoring function of the classical AutoDock Vina to evaluate a solution during docking simulation. ABCEDM adopts the search framework of the canonical ABC algorithm to execute conformational search. By further investigating the characteristics of the protein-ligand docking problem, a proprietary search mechanism inspired by estimation of distribution algorithm, i.e., estimation of distribution mechanism (EDM), is designed to enhance the performance of ABC-EDM. To verify the effectiveness of the proposed ABC-EDM, we compare it with three variants of the ABC algorithm, three evolutionary computation algorithms, and AutoDock Vina. The experimental results show that ABC-EDM can effectively solve the protein-ligand docking problem, and it can achieve a success rate 5% higher than AutoDock Vina on the GOLD dataset. This study reveals that taking advantage of problem -specific information about the protein-ligand docking problem to enhance a docking method contributes to solving this problem.

关键词： Protein-ligand docking Artificial bee colony estimation of distribution algorithm Structure-based drug design Optimization

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estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem

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MEMETIC COMPUTING 2019年第4期11卷 423-437页

作者： Xue, Yue Rui, Zhijian Yu, Xianyu Sang, Xiuzhi Liu, Wenjie Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Res Ctr Soft Energy Sci Nanjing 211106 Peoples R China

With the increasing concern on greenhouse gas emissions, green scheduling decision in the manufacturing factory is gaining more and more attention. This paper addresses the unrelated parallel machine green scheduling problem (UPMGSP) with criteria ofminimizing themakespan and the total carbon emission. To solve the problem, the estimation of distribution evolution memetic algorithm (EDEMA) is proposed. Firstly, based on the minimum machine load first principle, the initialization of the population is proposed. Second, a multi-objective non-dominated sorting approach and the crowding distance are adopted to improve the diversity of individual. Third, to estimate the probability distribution of the solution space, a probability model is presented to enhance the searching ability. Third, five neighbourhood searching operators are designed to handle the job-to-machine assignment. Moreover, the population catastrophe is used to maintain the sustainable diversity of the population. Finally, based on the randomly generated instances of the UPMGSP, extensive computational tests are carried out. The obtained computational results show that the EDEMA has the better searching capability and the better objective value than those of the non-dominated sorting genetic algorithm II and the estimation of distribution evolution algorithm (EDEA) in solving the UPMGSP.

关键词： Green scheduling Unrelated parallel-machine estimation of distribution algorithm

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estimation of distribution algorithmS FOR CLUSTERING AND CLASSIFICATION

ESTIMATION OF DISTRIBUTION ALGORITHMS FOR CLUSTERING AND CLA...

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作者： HENRY EMANUEL LEAL CAGNINI PONTIFICAL CATHOLIC UNIVERSITY OF RIO GRANDE DO SUL

学位级别：硕士

Extracting meaningful information from data is not an easy task. Data can come in batches or through a continuous stream, and can be incomplete or complete, duplicated, or noisy. Moreover, there are several algorithms to perform data mining tasks, and the no-free lunch theorem states that there is not a single best algorithm for all problems. As a final obstacle, algorithms usually require hyper- parameters to be set in order to operate, which not surprisingly often demand a minimum knowledge of the application domain to be fine-tuned. Since many traditional data mining algorithms employ a greedy local search strategy, fine-tuning is a crucial step towards achieving better predictive models. On the other hand, estimation of distribution algorithms perform a global search, which often is more efficient than performing a wide search through the set of possible parameters. By using a quality function, estimation of distribution algorithms will iteratively seek better solutions throughout its evolutionary process. Based on the benefits that estimation of distribution algorithms may offer to clustering and decision tree-induction, two data mining tasks considered to be NP-hard and NP- hard/complete, respectively, this works aims at developing novel algorithms in order to obtain better results than traditional, greedy algorithms and baseline evolutionary approaches.

关键词： estimation of distribution algorithm decision-tree induction clustering optimization

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Large-Scale estimation of distribution algorithms with Adaptive Heavy Tailed Random Pro jection Ensembles

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Journal of Computer Science & Technology 2019年第6期34卷 1241-1257页

作者： Momodou L.Sanyang Ata Kabán School of Computer Science University of BirminghamEdgbastonBirmingham B152TTU.K. School of Information Technology and Communication University of The GambiaSerekundaThe Gambia

We present new variants of estimation of distribution algorithms (EDA) for large-scale continuous optimisation that extend and enhance a recently proposed random projection (RP) ensemble based approach. The main novelty here is to depart from the theory of RPs that require (sub-)Gaussian random matrices for norm-preservation, and instead for the purposes of high-dimensional search we propose to employ random matrices with independent and identically distributed entries drawn from a t-distribution. We analytically show that the implicitly resulting high-dimensional covariance of the search distribution is enlarged as a result. Moreover, the extent of this enlargement is controlled by a single parameter, the degree of freedom. For this reason, in the context of optimisation, such heavy tailed random matrices turn out to be preferable over the previously employed (sub-)Gaussians. Based on this observation, we then propose novel covariance adaptation schemes that are able to adapt the degree of freedom parameter during the search, and give rise to a flexible approach to balance exploration versus exploitation. We perform a thorough experimental study on high-dimensional benchmark functions, and provide statistical analyses that demonstrate the state-of-the-art performance of our approach when compared with existing alternatives in problems with 1000 search variables.

关键词： covariance adaptation estimation of distribution algorithm random projection ensemble t-distribution

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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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Regularized continuous estimation of distribution algorithms

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APPLIED SOFT COMPUTING 2013年第5期13卷 2412-2432页

作者： Karshenas, Hossein Santana, Roberto Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Fac Informat Computat Intelligence Grp E-28660 Madrid Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastian 20080 Spain

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings. (C) 2012 Elsevier B. V. All rights reserved.

关键词： estimation of distribution algorithm Regularized model estimation Continuous optimization High-dimensionality

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A Hybrid Multi-objective algorithm Using Genetic and estimation of distribution Based on Design of Experiments

A Hybrid Multi-objective Algorithm Using Genetic and Estimat...

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IEEE International Conference on Intelligent Computing and Intelligent Systems

作者： Dai, Guangming Wang, Jianwen Zhu, Jiankai China Univ Geosci Sch Comp Wuhan 430074 Peoples R China

ISBN: (纸本)9781424447541

In this paper, we design a hybrid multi-objective algorithm using genetic and estimation of distribution based on design of Experiments At first, we apply orthogonal design and uniform design to generate an initial population so that the population individual solutions scattered evenly in the feasible solutions space Second, we proposed a new convergence criterion to check whether the distribution of population has the obvious regularity When the population is convergence, we use the model-based method to reproduce new individual solutions, otherwise genetic operator was employed to generate offspring, The results of systematic experiments show that the hybrid algorithm this paper proposed capable of finding much better convergence near the Pareto-optimal solutions and better spread of solutions than RM-MEDA

关键词： Multi-objective optimizing estimation of distribution algorithm Orthogonal design Uniform design Optimal Design of Constellation

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