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Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2013年第2期55卷 515-544页

作者： Nesmachnow, Sergio Univ Republica Montevideo Uruguay

This article presents six parallel multiobjective evolutionary algorithms applied to solve the scheduling problem in distributed heterogeneous computing and grid systems. The studied evolutionary algorithms follow an explicit multiobjective approach to tackle the simultaneous optimization of a system-related (i.e. makespan) and a user-related (i.e. flowtime) objectives. Parallel models of the proposed methods are developed in order to efficiently solve the problem. The experimental analysis demonstrates that the proposed evolutionary algorithms are able to efficiently compute accurate results when solving standard and new large problem instances. The best of the proposed methods outperforms both deterministic scheduling heuristics and single-objective evolutionary methods previously applied to the problem.

关键词： multiobjective evolutionary algorithms Parallelism Heterogeneous computing Grid Scheduling

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Preventive maintenance and replacement optimization on CNC machine using multiobjective evolutionary algorithms

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第9-12期76卷 2131-2146页

作者： Moghaddam, Kamran S. Calif State Univ Fresno Dept Informat Syst & Decis Sci Craig Sch Business Fresno CA 93740 USA

In this paper, a multiobjective optimization model is formulated to determine the optimal preventive maintenance plans for a repairable multicomponent manufacturing system with increasing rate of occurrence of failure. The operational planning horizon is segmented into discrete and equally sized periods, and in each period, three possible maintenance actions (repair, replacement, or do nothing) have been considered for each component. The developed multiobjective nonlinear mixed-integer programming model is then utilized to find the Pareto-optimal preventive maintenance schedules for a computer numerical control (CNC) machine. In order to obtain the Pareto-optimal solutions, five multiobjective evolutionary algorithms are considered, and computational performance of the algorithms is evaluated using different metrics. It is found that three of these algorithms outperform the others in obtaining more and high-quality nondominated preventive maintenance and replacement schedules along with maintaining high level of diversity among generated solutions. Such a modeling approach and the comparison of algorithms would be useful for maintenance planners tasked with the problem of developing optimal maintenance plans for multicomponent manufacturing machines.

关键词： Preventive maintenance and replacement optimization CNC machine multiobjective evolutionary algorithms Comparison of algorithms

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2008年第4期E91D卷 1206-1210页

作者： Aguirre, Hernan Sato, Masahiko Tanaka, Kiyoshi Shinshu Univ Fac Engn Nagano 3808553 Japan

In this paper, we propose delta-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.

关键词： multiobjective evolutionary algorithms delta-similar elimination controlled elitism selection

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Simultaneous topology, shape and sizing optimisation of a three-dimensional slender truss tower using multiobjective evolutionary algorithms

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COMPUTERS & STRUCTURES 2011年第23-24期89卷 2531-2538页

作者： Noilublao, Norapat Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Fac Engn Khon Kaen 40002 Thailand

This paper presents an integrated design technique to carry out simultaneous topology, shape and sizing optimisation of a three-dimensional truss structure. Design objectives include mass, compliance, natural frequencies, frequency response function (FRF), and force transmissibility (FT). The Pareto fronts are explored by using: strength Pareto evolutionary algorithm (SPEA2), population-based incremental learning (PBIL), and archived multiobjective simulated annealing (AMOSA). The results obtained from using the optimisers are compared based upon the hypervolume (HV) and generational distance (GD). It is shown that PBIL is the best for optimising compliance and natural frequency, while SPEA2 is superior when dealing with FRF and FT. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Truss optimisation multiobjective evolutionary algorithms Topology optimisation Structural vibration Frequency response function

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A Survey of multiobjective evolutionary algorithms for Data Mining: Part I

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第1期18卷 4-19页

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Coello Coello, Carlos Artemio Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India CINVESTAV IPN Dept Comp Evolutionary Computat Grp Mexico City DF Mexico

The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionary algorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and to discover significant and meaningful information. Many real-life data mining problems involve multiple conflicting measures of performance, or objectives, which need to be optimized simultaneously. Under this context, multiobjective evolutionary algorithms are gradually finding more and more applications in the domain of data mining since the beginning of the last decade. In this two-part paper, we have made a comprehensive survey on the recent developments of multiobjective evolutionary algorithms for data mining problems. In this paper, Part I, some basic concepts related to multiobjective optimization and data mining are provided. Subsequently, various multiobjective evolutionary approaches for two major data mining tasks, namely feature selection and classification, are surveyed. In Part II of this paper, we have surveyed different multiobjective evolutionary algorithms for clustering, association rule mining, and several other data mining tasks, and provided a general discussion on the scopes for future research in this domain.

关键词： Classification feature selection multiobjective evolutionary algorithms Pareto optimality

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Synthesis of multiple cross-section pin fin heat sinks using multiobjective evolutionary algorithms

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2018年 118卷 462-470页

作者： Ramphueiphad, Sanchai Bureerat, Sujin Khon Kaen Univ Fac Engn Sustainable & Infrastruct Res & Dev Ctr Dept Mech Engn Khon Kaen 40002 Thailand

This paper demonstrates multiobjective optimization of a multi cross-section pin fin heat sink (MCSPFHS) for use in electronic devices. The design problem is assigned to optimize junction temperature and fan pumping power of the heat sink. Design variables are encoded to shape the fin geometry with several pin fin cross-sections. The heat sink is set to be side-inlet-side-outlet (SISO) while several multiobjective evolutionary algorithms (MOEAs) including hybrid real code population-based incremental learning and differential evolution (PBIL-DE), second version of strength Pareto evolutionary algorithm (SPEA2), and unrestricted population size evolutionary multiobjective optimization algorithm (UPSEMOA) are applied to solve the bi-objective optimization problem. The results obtained are superior to those conventional pin fin heat sinks. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： multiobjective evolutionary algorithms Side-inlet-side-outlet heat sink Pareto optimal solutions Computational fluid dynamics Multiple cross-section pin fin heat sink

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A Survey of Weight Vector Adjustment Methods for Decomposition-Based multiobjective evolutionary algorithms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2020年第4期24卷 634-649页

作者： Ma, Xiaoliang Yu, Yanan Li, Xiaodong Qi, Yutao Zhu, Zexuan Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China RMIT Univ Sch Sci Melbourne Vic 3001 Australia Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Shenzhen Univ SZU Branch Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518060 Peoples R China

multiobjective evolutionary algorithms based on decomposition (MOEA/D) have attracted tremendous attention and achieved great success in the fields of optimization and decision-making. MOEA/Ds work by decomposing the target multiobjective optimization problem (MOP) into multiple single-objective subproblems based on a set of weight vectors. The subproblems are solved cooperatively in an evolutionary algorithm framework. Since weight vectors define the search directions and, to a certain extent, the distribution of the final solution set, the configuration of weight vectors is pivotal to the success of MOEA/Ds. The most straightforward method is to use predefined and uniformly distributed weight vectors. However, it usually leads to the deteriorated performance of MOEA/Ds on solving MOPs with irregular Pareto fronts. To deal with this issue, many weight vector adjustment methods have been proposed by periodically adjusting the weight vectors in a random, predefined, or adaptive way. This article focuses on weight vector adjustment on a simplex and presents a comprehensive survey of these weight vector adjustment methods covering the weight vector adaptation strategies, theoretical analyses, benchmark test problems, and applications. The current limitations, new challenges, and future directions of weight vector adjustment are also discussed.

关键词： Sociology Pareto optimization evolutionary computation Computer science Shape Decomposition-based MOEA multiobjective evolutionary algorithms based on decomposition (MOEA D) multiobjective evolutionary algorithms weight vector adjustment

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Theoretical Analyses of multiobjective evolutionary algorithms on Multimodal Objectives

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evolutionary COMPUTATION 2023年第4期31卷 337-373页

作者： Zheng, Weijie Doerr, Benjamin Harbin Inst Technol Int Res Inst Artificial Intelligence Sch Comp Sci & Technol Shenzhen Peoples R China Inst Polytech Paris Ecole Polytech CNRS Lab Informat LIX Palaiseau France

multiobjective evolutionary algorithms are successfully applied in many real-world multiobjective optimization problems. As for many other AI methods, the theoretical understanding of these algorithms is lagging far behind their success in practice. In particular, previous theory work considers mostly easy problems that are composed of unimodal *** a first step towards a deeper understanding of how evolutionary algorithms solve multimodal multiobjective problems, we propose the OneJumpZeroJump problem, a bi-objective problem composed of two objectives isomorphic to the classic jump function benchmark. We prove that the simple evolutionary multiobjective optimizer (SEMO) with probability one does not compute the full Pareto front, regardless of the runtime. In contrast, for all problem sizes n and all jump sizes k is an element of[4..n/2-1], the global SEMO (GSEMO) covers the Pareto front in an expected number of Theta((n-2k)n(k)) iterations. For k=o(n), we also show the tighter bound 3/2en(k+1)+/- o(n(k+1)), which might be the first runtime bound for an MOEA that is tight apart from lower-order terms. We also combine the GSEMO with two approaches that showed advantages in single-objective multimodal problems. When using the GSEMO with a heavy-tailed mutation operator, the expected runtime improves by a factor of at least k(Omega(k)). When adapting the recent stagnation-detection strategy of Rajabi and Witt (2022) to the GSEMO, the expected runtime also improves by a factor of at least k(Omega(k)) and surpasses the heavy-tailed GSEMO by a small polynomial factor in k. Via an experimental analysis, we show that these asymptotic differences are visible already for small problem sizes: A factor-5 speed-up from heavy-tailed mutation and a factor-10 speed-up from stagnation detection can be observed already for jump size 4 and problem sizes between 10 and 50. Overall, our results show that the ideas recently developed to aid single-objective evolutionary algorit

关键词： multiobjective evolutionary algorithms multimodal problems theory of computing runtime analysis

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Analysis of Solution Quality of multiobjective evolutionary algorithms for Service Restoration in Distribution Systems

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2017年第6期28卷 796-805页

作者： Sanches, Danilo Sipoli Castoldi, Marcelo Favoretto Augusto London, Joao Bosco, Jr. Botazzo Delbem, Alexandre Claudio Univ Tecnol Fed Parana Av Alberto Carazzai Cornelio Procopio PR Brazil Univ Sao Paulo Sao Carlos Sch Engn Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Sao Carlos SP Brazil

This paper presents a comparative study of six multiobjective evolutionary algorithms (MOEAs) with the node-depth encoding (NDE) which have been used to solve the service restoration in distribution systems. The study has been divided into three steps: (1) the MOEAs have been evaluated taking into account the switching operations necessary to find adequate restoration plans considering multiple nonlinear constraints and objective functions;(2) the MOEAs have been employed to solve four different datasets with 3860, 7720, 15,440 and 30,880 buses, respectively;(3) comparisons have been performed using the hypervolume indicator and the results obtained with each approach are statistically compared using Kruskal-Wallis nonparametric tests and multiple comparisons. In addition, this paper provides a comprehensive evaluation of six combinations of MOEAs based on NDE and our objective is to identify the features of each approach that consistently produce best results applied to network reconfiguration for service restoration in distribution systems. Simulations results have shown that MOEA based on NDE with crowding distance and strength pareto found good configurations with low switching operations and explored the search space better than others approaches used in this paper, approximating better the pareto-optimal front.

关键词： Large-scale distribution systems Service restoration Node-depth encoding multiobjective evolutionary algorithms Differential evolution

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Solving Multicommodity Capacitated Network Design Problems Using multiobjective evolutionary algorithms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2012年第4期16卷 449-471页

作者： Kleeman, Mark P. Seibert, Benjamin A. Lamont, Gary B. Hopkinson, Kenneth M. Graham, Scott R. USAF Inst Technol Dept Elect & Comp Engn Grad Sch Engn & Management Wright Patterson AFB OH 45433 USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

evolutionary algorithms can be applied to a variety of constrained network communication problems with centric type models. This paper shows that with real-world complex network communication problems of this type, sophisticated statistical search is required. This situation occurs due to the fact that these optimization problems are at least NP-complete. In order to appreciate the formal modeling of realistic communication networks, historical network design problems are presented and evolved into more complex real-world models with associated deterministic and stochastic solution approaches discussed. This discussion leads into the design of an innovative multiobjective evolutionary algorithm (MOEA) to solve a very complex network design problem variation called the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid real-world communication architecture as reflected in real-world network centric models with directional communications, multiple objectives including costs, delays, robustness, vulnerability, and operating reliability within network constraints. Nodes in such centric systems can have multiple and varying link capacities as well as rates and information (commodity) quantities to be sent and received. Each commodity can also have independent prioritized bandwidth and spectrum requirements. The nondominated sorting genetic algorithm (NSGA-II) is selected as the MOEA framework which is modified and parallelized to solve the generic MCNDP. Since the MCNDP is highly constrained but with an enormous number of possible network communication topologies, a novel initialization procedure and mutation method are integrated resulting in reduced search space. Empirical results and analysis indicate that effective topological Pareto solutions are generated for use in highly constrained, communication-based network design.

关键词： Multicommodity capacitated networks multiobjective evolutionary algorithms network centric systems network topology Pareto optimization

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