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Genetic and evolutionary Computation Conference (GECCO)

作者： Ishibuchi, Hisao Imada, Ryo Setoguchi, Yu Nojima, Yusuke Southern Univ Sci & Technol SUSTech Shenzhen Guangdong Peoples R China Osaka Prefecture Univ Sakai Osaka Japan

ISBN: (纸本)9781450349208

Hypervolume has been frequently used as a performance indicator for comparing evolutionary multiobjective optimization (EMO) algorithms. Hypervolume has been also used in indicator-based algorithms. Whereas a reference point is needed for hypervolume calculation, its specification has not been discussed in detail from a viewpoint of fair comparison. This may be because a slightly worse reference point than the nadir point seems to work well. In this paper, we tackle this issue: How to specify a reference point for fair comparison. First we discuss an appropriate specification of a reference point for multiobjective problems. Our discussions are based on the well-known theoretical results about the optimal solution distribution for hypervolume maximization. Next we examine various specifications by computational experiments. Experimental results show that a slightly worse reference point than the nadir point works well only for test problems with triangular Pareto fronts. Then we explain why this specification is not always appropriate for test problems with inverted triangular Pareto fronts. We also report a number of solution sets obtained by SMS-EMOA with various specifications of a reference point.

关键词： evolutionary multiobjective optimization hypervolume

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Preference Incorporation to Solve Multi-Objective Mission Planning of Agile Earth Observation Satellites

Preference Incorporation to Solve Multi-Objective Mission Pl...

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IEEE Congress on evolutionary Computation (CEC)

作者： Li, Longmei Yao, Feng Jing, Ning Emmerich, Michael Natl Univ Def Technol Sch Elect Sci & Engn Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sch Informat Syst & Management Changsha 410073 Hunan Peoples R China Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands

ISBN: (纸本)9781509046010

This paper investigates earth observation scheduling of agile satellite constellation based on evolutionary multiobjective optimization (EMO). The mission planning of agile earth observation satellite (AEOS) is to select and specify the observation activities to acquire images on the earth surface. This should be done in accordance with operational constraints and in order to maximize certain objectives. In this paper three objectives are considered, i.e. profit, quality and timeliness. Preference-based EMO methods are introduced to generate solutions preferable for the decision maker, whose preference is expressed by a reference point. An improved algorithm based on R-NSGA-II, which is named CD-NSGA-II, is proposed and compared with other algorithms in various scheduling scenarios. Results show that the chosen preference modeling paradigm allows to focus search on the interesting part of the Pareto front. Moreover, the tested algorithms behave differently with the change of reference point and degree of conflicts, among them CD-NSGA-II has the best overall performance. Suggestions on applying these algorithms in practice are also given in this paper.

关键词： Agile Earth Observation Satellite Satellite Mission Planning evolutionary multiobjective optimization Preference Integration

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Accelerating MOEA/D by Nelder-Mead Method

Accelerating MOEA/D by Nelder-Mead Method

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IEEE Congress on evolutionary Computation (CEC)

作者： Zhang, Hanwei Zhou, Aimin Zhang, Guixu Singh, Hemant Kumar East China Normal Univ Dept Comp Sci & Technol 3663 N Zhongshan Rd Shanghai Peoples R China Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2610 Australia

ISBN: (纸本)9781509046010

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) converts a multiobjective optimization problem into a set of single-objective subproblems, and tackles them simultaneously. In MOEA/D, the offspring generation is a crucial part to increase the convergence of the algorithm and maintain the diversity of the solution set. Currently, the majority of reproduction operators consider the quality of neighborhood exploration, i.e., the capability to distribute along the population structure, while few operators have good capability for subproblem exploitation, i.e., the ability to push solutions forward along the subproblems. To address this issue in this paper, we introduce one of the derivative-free optimization methods, Nelder-Mead simplex (NMS) method, to MOEA/D to accelerate the algorithm convergence. The NMS operator is combined with a differential evolution (DE) operator in the offspring generation. The comparison study demonstrates that calling the NMS operator occasionally can help to accelerate the convergence.

关键词： evolutionary multiobjective optimization MOEA/D Nelder-Mead simplex method

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multiobjective Reliability-Based Design optimization Formulations Solved Combining NSGA-II and First Order Reliability Method 12th

Multiobjective Reliability-Based Design Optimization Formula...

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12th International Conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Celorrio, Luis Univ La Rioja Logrono La Rioja Spain

ISBN: (纸本)9783319596501;9783319596495

Uncertainties are inherent in realistic structural optimization problems. For example, geometric variables and material properties are uncertain parameters and have to be accounted to ensure safety and quality. A manner of considering uncertainties in structural design is using constraints written in terms of probability of failure or reliability index in the optimization problem. Usually structural optimization problems consider constraints as restrictions and optimize the cost or the weight of the structures. However, several types of problems can be formulated in the field of optimization under Uncertainty. This paper presents a computer program to solve two type of problems: In the first one, a bi-objective problem is solved, where the probability of system failure has been added as the second objective to the original cost objective. The second problem consists in optimising simultaneously two more performances or objective functions subject to reliability constraints. This formulation is named multiobjective Reliability Based Design optimization (MO-RBDO). Reliability analysis is carried out using a gradient based First Order Reliability Method (FORM). This structural reliability assessment method has shown efficient. An analytical example and a classical ten bar truss illustrate the application of this algorithm.

关键词： Fusion of soft computing and hard computing evolutionary multiobjective optimization Decision making NSGA-2 System reliability

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A New Self-Adaptive Approach for evolutionary multiobjective optimization

A New Self-Adaptive Approach for Evolutionary Multiobjective...

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2010 IEEE World Congress on Computational Intelligence

作者： Batista, Lucas S. Campelo, Felipe Guimaraes, Frederico G. Ramirez, Jaime A. Univ Fed Minas Gerais Dept Engn Eletr Av Antonio Carlos 6627 BR-31720010 Belo Horizonte MG Brazil Univ Fed Ouro Preto Dept Comp BR-35400000 Ourpreto MG Brazil

ISBN: (纸本)9781424481262

We propose in this paper a new strategy for self-adaptation in multiobjective evolutionary algorithms, which is based on information obtained from the implicit distribution created by a chaotic differential mutation operator. This technique is used to develop a self-adaptive evolutionary algorithm for multiobjective optimisation, and its efficiency is evaluated by means of a comparative study using well-known benchmark problems. The statistical analysis of the results shows that the proposed algorithm was able to outperform the NSGA-II in fourteen of the seventeen problems used. These results represent evidence for the adequacy of the proposed technique in solving the classes of multiobjective optimisation problems represented in the benchmark suites used.

关键词： adaptive systems chaotic differential mutation operator evolutionary computation evolutionary multiobjective optimization self-adaptive approach statistical analysis statistical analysis

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Getting Lost or Getting Trapped: On the Effect of Moves to Incomparable Points in multiobjective Hillclimbing [Workshop on Theoretical Aspects of evolutionary multiobjective optimization] 10

Getting Lost or Getting Trapped: On the Effect of Moves to I...

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12th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Emmerich, Michael Deutz, Andre Li, Rui Kruisselbrink, Johannes Leiden Univ LIACS NL-2333 CA Leiden Netherlands

ISBN: (纸本)9781450300735

Divergent behavior may occur in elitist multiobjective EAs which allow moves to incomparable solutions. We study under which conditions this is exhibited. For simple model landscapes stochastic dynamics are studied and quantified by means of Markov chains. The studies suggest that increasing the population size tempers divergent behavior. In addition, we study whether common elitist algorithms such as NSGA-II and SMS-EMOA have divergent behavior.

关键词： evolutionary multiobjective optimization Incomparability Markov Chains NSGA-II Convergence Analysis

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A Reference Vector Guided evolutionary Algorithm for Many-Objective optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2016年第5期20卷 773-791页

作者： Cheng, Ran Jin, Yaochu Olhofer, Markus Sendhoff, Bernhard Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Honda Res Inst Europe D-63073 Offenbach Germany

In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto-optimal solutions using a limited population size as the number of objectives increases. This paper proposes a reference vector-guided EA for many-objective optimization. The reference vectors can be used not only to decompose the original multiobjective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the reference vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art EAs for many-objective optimization. In addition, we show that reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization. Furthermore, a reference vector regeneration strategy is proposed for handling irregular PFs. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.

关键词： Angle-penalized distance (APD) convergence diversity evolutionary multiobjective optimization many-objective optimization preference articulation reference vector

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Mechanical design, multiple criteria decision making and Pareto optimality gap

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ENGINEERING COMPUTATIONS 2016年第3期33卷 876-895页

作者： Kaliszewski, Ignacy Kiczkowiak, Tomasz Miroforidis, Janusz Polish Acad Sci Syst Res Inst Dept Intelligent Syst PL-01447 Warsaw Poland Tech Univ Koszalin Inst Technol & Educ Koszalin Poland

Purpose - The purpose of this paper is to present an approach to multiple criteria mechanical design problems, for cases where problem complexity precludes derivation of the whole Pareto front (PF). For such problems the authors propose to limit search, and hence also derivation, of the PF exclusively to regions of the direct designer's interest, thus saving on computing efforts and gaining on tractable problem sizes. Design/methodology/approach - To achieve the purpose, the authors frame the decision making process (design) into a combination of three specific concepts, namely, decision maker's preference capture, local PF search and approximate multiobjective optimization (MO) with assessments of the Pareto optimality gap. The authors illustrate the approach with two small design problems, namely, Pareto optimal round tube beam and Pareto optimal pneumatic high-speed machine drive selection. The authors solve these problems in a setting which can be regarded as representative for problem solving in real environment. Findings - On the decision making side, the proposed approach has turned out to be a versatile tool for selecting designs from the Pareto suboptimal ones, where each such a Pareto suboptimal design has an explicit assessment of the Pareto optimality gap. On the technical (optimization) side, it has been demonstrated that the approach seamlessly works with evolutionary computations, structured to the specific needs of the approach. Research limitations/implications - It has been shown that the navigation over the PF can be achieved with limited effort, both on the cognitive and the computing side. Moreover, navigation over the PF can be focussed from the very beginning of the design selection process on the regions of the PF which are of the direct designer's interest. This eliminates the need to derive (or only approximate) the whole PF, a tangible asset as the derivation of that set is the main factor precluding scalability of design selection problem

关键词： Mechanical design Approximate multiobjective optimization evolutionary multiobjective optimization Multiple criteria decision making Pareto optimality gap Preference capture

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evolutionary multi-objective resource allocation and scheduling in the Chinese navigation satellite system project

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第2期251卷 662-675页

作者： Xiong, Jian Leus, Roel Yang, Zhenyu Abbass, Hussein A. Natl Univ Def Technol Coll Informat Syst & Management Changsha 410073 Hunan Peoples R China Katholieke Univ Leuven Fac Econ & Business Louvain Belgium Univ New S Wales Sch Engn & Informat Technol Canberra ACT Australia

The development of appropriate project management techniques for Research and Development (R&D) projects has received significant academic and practical attention over the past few decades. Project managers typically face the problem of allocating resources and scheduling activities, for which the underlying combinatorial problem is NP-hard. The inherent uncertainty in many R&D environments increases the complexity of the problem. This paper addresses the problem of resource allocation and activity scheduling with a focus on R&D projects. The work is different from the existing literature in at least three aspects: (1) the problem formulation is based on a real-world Chinese aerospace project, (2) each individual resource unit can have a different resource efficiency, and (3) the uncertainty of the duration of an activity is time-dependent (efficiency-dependent) in nature. The problem is formulated as a multi-objective optimization model with simultaneous consideration of malcespan and balance of resource efficiency. A cooperative coevolutionary multi-objective algorithm (CC-MOA) is designed to produce high-quality solutions. Two chromosome representations and three resource selection policies are tested for the algorithm. The proposed CCMOA is found to be competitive when compared to MOEA/D and NSGA-II, which are two popular algorithms for multi-objective optimization. (C) 2015 Elsevier B.V. All rights reserved.

关键词： evolutionary multiobjective optimization Resource allocation and scheduling Time-dependent uncertainty Cooperative coevolutionary algorithm Aerospace project

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Entropy-Based Termination Criterion for multiobjective evolutionary Algorithms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2016年第4期20卷 485-498页

作者： Saxena, Dhish Kumar Sinha, Arnab Duro, Joao A. Zhang, Qingfu Indian Inst Technol Roorkee Dept Mech & Ind Engn Roorkee 247667 Uttar Pradesh India Singapore Univ Technol & Design Singapore Singapore Univ Bath Dept Comp Sci Bath BA2 7AY Avon England City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

multiobjective evolutionary algorithms evolve a population of solutions through successive generations toward the Pareto-optimal front (POF). One of the most critical questions faced by the researchers and practitioners in this domain relates to the number of generations that may be sufficient for an algorithm to offer a good approximation of the POF for a given problem. Ironically, to date, this question largely remains unanswered and the number of generations are arbitrarily fixed a priori, with potentially punitive implications. If the a priori fixed generations are insufficient, then the algorithm reports suboptimal solutions. In contrast, if the a priori fixed generations are far too many, it implies waste of computational resources. This paper proposes a novel entropy-based dissimilarity measure that helps identify on the fly the number of generations beyond which an algorithm stabilizes, implying that either a good approximation has been obtained or that it cannot be obtained due to the stagnation of the algorithm in the search space. Given that in either case no further improvement in the approximation can be obtained, despite additional computational expense, the proposed dissimilarity measure provides a termination criterion and facilitates a termination detection algorithm. The generality, on-the-fly implementation, low-computational complexity, and the demonstrated efficacy of the proposed termination detection algorithm, on a wide range of multiobjective and many-objective test problems, define the novel contribution of this paper.

关键词： Entropy evolutionary multiobjective optimization many-objective optimization termination detection algorithm

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