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An experimental analysis of the p-median problem under uncertainty: an evolutionary algorithm approach

EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2014年第4期8卷 554-578页

作者： Lopez-Monzalvo, Francisco Brizuela, Carlos A. CICESE Res Ctr Ensenada 22860 Baja California Mexico Univ Caribe Dept Ciencias Basicas & Ingn Benito Juarez 77528 Quintana Roo Mexico

Facility location under uncertain environments is an important and challenging problem. The problem deals with the optimal placement of facilities that serve a set of spatially distributed nodes. One way to deal with this problem is to model uncertainty by means of scenarios and to optimise some robustness criteria such as the average and maximum regrets over these scenarios. We propose to model the robust design as a bi-objective optimisation problem and to use a well-known multi-objective evolutionary algorithm, the NSGA-II, to solve it. We also propose to use the bi-objective optimisation framework to analyse the effects of variations in the number of facilities to install, and of nodes to be served, on the quality of the Pareto solutions. Computational experiments show that the proposal can be used to design robust solutions and to study the effects of changes in the system parameters on the quality of the generated solutions.

关键词： p-median problem PMP uncertainty scenarios robustness regret multi-objective evolutionary algorithm MOEA

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Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms

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APPLIED SOFT COMPUTING 2012年第1期12卷 28-39页

作者： Nandi, Arup Kumar Deb, Kalyanmoy Ganguly, Subhas Datta, Shubhabrata Cent Mech Engn Res Inst CSIR CMERI Durgapur 713209 W Bengal India Indian Inst Technol Kanpur Dept Mech Engn Kanpur 208016 Uttar Pradesh India Natl Inst Technol Dept Met Engn Raipur 492010 CG India Birla Inst Technol Deoghar 814142 Jharkhand India

To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of various metallic fillers in particulate reinforced flexible mould material composites, multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Here we have adopted two EA-based algorithms namely NSGAII and SPEA2 in order to solve the present problem independently. Comparative study of the results reveals that NSGAII performs better over SPEA2 for investigating the role of metallic fillers in particulate reinforced flexible mould material composites. A recently proposed innovization procedure is also used to unveil salient properties associated with the obtained trade-off solutions. These solutions are analyzed to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mould material. Based on the findings through investigations, the optimal selection of materials is suggested including the cost implication factor. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Particle reinforced flexible mould material composites Metallic filler multi-objective evolutionary algorithm Soft tooling process Reducing cooling time

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Parallel biclustering detection using strength Pareto front evolutionary algorithm

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INFORMATION SCIENCES 2017年 415卷 283-297页

作者： Golchin, Maryam Liew, Alan Wee Chung Griffith Univ Sch Informat & Commun Technol Gold Coast Campus Nathan Qld 4222 Australia

Biclustering has become a popular technique to analyse gene expression datasets and extract valuable information by clustering rows and columns of a dataset simultaneously. Using a good merit function together with a suitable local search can lead to the detection of interesting biclusters. In this paper, a multi-objective evolutionary algorithm with local search is proposed to search for multiple biclusters concurrently in a single run of the evolutionary algorithm. We call our method PBD-SPEA2 (Parallel Biclustering Detection using Strength Pareto front evolutionary algorithm 2). In our algorithm, a new dynamic encoding scheme is used to encode multiple biclusters in each individual. Our multi-objective function consists of three objectives that simultaneously optimizes the homogeneity of the elements in the bicluster, the size of the bicluster, and the variance of the column in the bicluster with respect to the entire dataset. Crossover is done by selecting and combining the best biclusters among the encoded biclusters from both parents through a strategy of exploration and exploitation. Finally, a sequential selection procedure is used to select the final set of biclusters from individuals that constitute the Pareto front. Experimental results are presented to compare the performance and biological enrichment of detected biclusters with several existing algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Biclustering Gene expression dataset multi-objective evolutionary algorithm Local search

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Novel soft subspace clustering with multi-objective evolutionary approach for high-dimensional data

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PATTERN RECOGNITION 2013年第9期46卷 2562-2575页

作者： Xia, Hu Zhuang, Jian Yu, Dehong Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

Many conventional soft subspace clustering techniques merge several criteria into a single objective to improve performance;however, the weighting parameters become important but difficult to set. In this paper, a novel soft subspace clustering with a multi-objective evolutionary approach (MOEASSC) is proposed to this problem. This clustering method considers two types of criteria as multiple objectives and optimizes them simultaneously by using a modified multi-objective evolutionary algorithm with new encoding and operators. An indicator called projection similarity validity index (PSVIndex) is designed to select the best solution and cluster number. Experiments on many datasets demonstrate the usefulness of MOEASSC and PSVIndex, and show that our algorithm is insensitive to its parameters and is scalable to large datasets. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Subspace clustering multi-objective evolutionary algorithm Determination of the best solution Determination of the cluster number

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PGA/MOEAD: a preference-guided evolutionary algorithm formulti-objective decision-making problems with interval-valued fuzzy preferences

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第3期49卷 595-616页

作者： Luo, Bin Lin, Lin Zhong, ShiSheng Harbin Inst Technol Sch Mechatron Engn Harbin Heilongjiang Peoples R China

In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.

关键词： multi-criteria decision-making Interval-valued fuzzy preferences Boundary intersection multi-objective evolutionary algorithm based on decomposition evolutionary Computation multi-objective optimisation

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Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem

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SOFT COMPUTING 2013年第2期17卷 275-284页

作者： Cruz-Ramirez, M. Hervas-Martinez, C. Gutierrez, P. A. Perez-Ortiz, M. Briceno, J. de la Mata, M. Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain Hosp Reina Sofia CIBERehd Liver Transplantat Unit Cordoba Spain

Donor-recipient matching constitutes a complex scenario difficult to model. The risk of subjectivity and the likelihood of falling into error must not be underestimated. Computational tools for the decision-making process in liver transplantation can be useful, despite the inherent complexity involved. Therefore, a multi-objective evolutionary algorithm and various techniques to select individuals from the Pareto front are used in this paper to obtain artificial neural network models to aid decision making. Moreover, a combination of two pre-processing methods has been applied to the dataset to offset the existing imbalance. One of them is a resampling method and the other is a outlier deletion method. The best model obtained with these procedures (with AUC = 0.66) give medical experts a probability of graft survival at 3 months after the operation. This probability can help medical experts to achieve the best possible decision without forgetting the principles of fairness, efficiency and equity.

关键词： Artificial neural networks Liver transplantation multi-objective evolutionary algorithm Organ allocations Pre-processing

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Gaussian Adaptive Strategy Based multi-objective evolutionary Optimization for Path Planning on Uneven Terrains

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第1期9卷 539-546页

作者： Zheng, Hui Lu, Yixiao Jie, Jing Hou, Beiping Zhang, Miao Zhang, Yitao Zhejiang Univ Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

To enable mobile robots to safely and effectively accomplish path planning tasks on uneven terrains, this letter proposes a Gaussian adaptive strategy-based multi-objective evolutionary optimization. Firstly, path solutions are generated by employing spline interpolation on the digital elevation model of the terrain map. Subsequently, a multi-objective optimization function is constructed, considering parameters such as path length, uniformity, slope, and relief. These parameters are computed using the elevation values of the terrain. Secondly, an evolutionary optimization algorithm based on a Gaussian adaptive strategy is introduced. This strategy ensures the uniform distribution, adaptability, and size stability of the reference points set. Additionally, it controls the selection rate of non-dominant contribution points during the optimization process. Finally, experimental results conducted in five different real environments demonstrate the suitability of the proposed algorithm for solving path planning problems on uneven terrain.

关键词： Path planning Optimization Interpolation Heuristic algorithms Task analysis Particle swarm optimization Statistics Agricultural automation motion and path planning optimization and optimal control multi-objective evolutionary algorithm uneven terrains

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multi-objective topology optimization using evolutionary algorithms

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ENGINEERING OPTIMIZATION 2011年第5期43卷 541-557页

作者： Kunakote, Tawatchai Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Khon Kaen 40002 Thailand

This article deals with the comparative performance of some established multi-objective evolutionary algorithms (MOEAs) for structural topology optimization. Four multi-objective problems, having design objectives like structural compliance, natural frequency and mass, and subjected to constraints on stress, etc., are posed for performance testing. The MOEAs include Pareto archive evolution strategy (PAES), population-based incremental learning (PBIL), non-dominated sorting genetic algorithm (NSGA), strength Pareto evolutionary algorithm (SPEA), and multi-objective particle swarm optimization (MPSO). The various MOEAs are implemented to solve the problems. The ground element filtering (GEF) technique is used to suppress checkerboard patterns on topologies. The results obtained from the various optimizers are illustrated and compared. It is shown that PBIL is far superior to the others. The optimal topologies from using PBIL can be compared with those obtained by employing the classical gradient-based approach. It can be considered as a powerful tool for structural topological design.

关键词： topology optimization multi-objective evolutionary algorithm ground element filtering compliance minimization population-based incremental learning

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Encouraging Behavioral Diversity in evolutionary Robotics: An Empirical Study

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evolutionary COMPUTATION 2012年第1期20卷 91-133页

作者： Mouret, J. -B. Doncieux, S. Univ Paris 06 CNRS UMR 7222 ISIR F-75252 Paris 05 France

evolutionary robotics (ER) aims at automatically designing robots or controllers of robots without having to describe their inner workings. To reach this goal, ER researchers primarily employ phenotypes that can lead to an infinite number of robot behaviors and fitness functions that only reward the achievement of the task and not how to achieve it. These choices make ER particularly prone to premature convergence. To tackle this problem, several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation;however, it also introduces new questions: how to compare behavior? should this comparison be task specific? and what is the best way to encourage diversity in this context? In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework. We compare each approach on three different tasks and two different genotypes. The results show that fostering behavioral diversity substantially improves the evolutionary process in the investigated experiments, regardless of genotype or task. Among the benchmarked approaches, multi-objective methods were the most efficient and the generic, Hamming-based, behavioral distance was at least as efficient as task specific behavioral metrics.

关键词： evolutionary robotics diversity multi-objective evolutionary algorithm neural networks

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multi-objective evolutionary emergency response optimization for major accidents

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JOURNAL OF HAZARDOUS MATERIALS 2010年第1-3期178卷 792-803页

作者： Georgiadou, Paraskevi S. Papazoglou, Ioannis A. Kiranoudis, Chris T. Markatos, Nikolaos C. Natl Tech Univ Athens Sch Chem Engn Athens 15780 Greece Natl Ctr Sci Res Demokritos Syst Reliabil & Ind Safety Lab Athens 15310 Greece

Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area: protection of the population in buildings: and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Emergency planning Major accident Evacuation multi-objective evolutionary algorithm Shelter-in-place

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