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An advanced multiobjective genetic algorithm design for the time and space assembly line balancing problem

COMPUTERS & INDUSTRIAL ENGINEERING 2011年第1期61卷 103-117页

作者： Chica, Manuel Cordon, Oscar Damas, Sergio European Ctr Soft Comp Mieres 33600 Spain ETS Informat & Telecomunicac Dept Comp Sci & Artificial Intelligence Granada 18071 Spain

Time and space assembly line balancing considers realistic multiobjective versions of the classical assembly line balancing industrial problems involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. In addition to their multi-criteria nature, the different problems included in this field inherit the precedence constraints and the cycle time limitations from assembly line balancing problems, which altogether make them very hard to solve. Therefore, time and space assembly line balancing problems have been mainly tackled using multiobjective constructive metaheuristics. Global search algorithms in general - and multiobjective genetic algorithms in particular - have shown to be ineffective to solve them up to now because the existing approaches lack of a proper design taking into account the specific characteristics of this family of problems. The aim of this contribution is to demonstrate the latter assumption by proposing an advanced multiobjective genetic algorithm design for the 1/3 variant of the time and space assembly line balancing problem which involves the joint minimization of the number and the area of the stations given a fixed cycle time limit. This novel design takes the well known NSGA-II algorithm as a base and considers the use of a new coding scheme and sophisticated problem specific operators to properly deal with the said problematic questions. A detailed experimental study considering 10 different problem instances (including a real-world instance from the Nissan plant in Barcelona, Spain) will show the good yield of the new proposal in comparison with the state-of-the-art methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Assembly lines Time and space assembly line balancing evolutionary multiobjective optimization NSGA-II

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Aquifers Management through evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)

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WATER RESOURCES MANAGEMENT 2011年第14期25卷 3883-3909页

作者： Molina, Jose-Luis Farmani, Raziyeh Bromley, John Univ Politecn Valencia Inst Water Engn & Environm Valencia 46022 Spain Univ Exeter Ctr Water Syst Exeter EX4 4QF Devon England Univ Oxford Ctr Water Res Oxford OX1 3QY England

An approach for the integration of Object-Oriented Bayesian Networks (OOBNs) and evolutionary multiobjective optimization (EMO) is proposed for integrated water resource management and decision support. Bayesian Networks (BNs) offer a novel and powerful tool for modelling complex water systems, facilitating the use of hierarchical modelling by improving the efficiency and communication between the different parts of a model. EMO offers a range of non-dominated optimal management solutions on a Pareto front that facilitate the identification of tradeoffs among conflicting criteria regarding stakeholder's preferences. The integrated tool provides new possibilities for undertaking an integrated analysis where stakeholder participation can play an important role. It is used for simultaneously analysing the whole water system, characterising uncertainty as probabilities and evaluating different management options. The tool is applied to an overexploited water system located in Southern Spain that is supplied totally by groundwater. In this study, a complex model based on BNs is designed and used as the core of the study. The transition to evolutionary Bayesian networks (EOBNs) allows stakeholder involvement to be utilized more effectively for designing and evaluating the model's consistency, and taking into account their conflicting interests.

关键词： Stakeholders' participation Aquifers management Decision support systems Bayesian Networks evolutionary multiobjective optimization

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Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2003年第2期7卷 204-223页

作者： Ishibuchi, H Yoshida, T Murata, T Osaka Prefecture Univ Dept Ind Engn Sakai Osaka 5998531 Japan Kansai Univ Fac Informat Dept Informat Osaka 5691095 Japan

This paper shows how the performance of evolutionary multiobjective optimization (EMO) algorithms can be improved by hybridization with local search. The main positive effect of the hybridization is the improvement in the convergence speed to the Pareto front. On the other hand, the main negative effect is the increase in the computation time per generation. Thus, the number of generations is decreased when the available computation time is limited. As a result, the global search ability of EMO algorithms is not fully utilized. These positive and negative effects are examined by computational experiments on multiobjective permutation flowshop scheduling problems. Results of our computational experiments clearly show the importance of striking a balance between genetic search and local search. In this paper, we first modify our former multiobjective genetic local search (MOGLS) algorithm by choosing only good individuals as initial solutions for local search and assigning an appropriate local search direction to each initial solution. Next, we demonstrate the importance of striking a balance between genetic search and local search through computational experiments. Then we compare the modified MOGLS with recently developed EMO algorithms: strength Pareto evolutionary algorithm and revised nondominated sorting genetic algorithm. Finally, we demonstrate that local search can be easily combined with those EMO algorithms for designing multiobjective memetic algorithms.

关键词： evolutionary multiobjective optimization genetic local search memetic algorithms multiobjective optimization permutation flowshop scheduling

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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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Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial multiobjective evolutionary Algorithm

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evolutionary COMPUTATION 2022年第2期30卷 131-164页

作者： Huizinga, Joost Clune, Jeff OpenAI San Francisco CA 94110 USA Univ British Columbia Vancouver BC V6T 1Z4 Canada

An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of subtasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial multiobjective evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III, and epsilon -Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks, and a simulated robot maze-navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.

关键词： Many-objective optimization evolutionary multiobjective optimization multimodal problems evolutionary algorithms robotics stepping stones

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An Indicator-Based multiobjective evolutionary Algorithm With Reference Point Adaptation for Better Versatility

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2018年第4期22卷 609-622页

作者： Tian, Ye Cheng, Ran Zhang, Xingyi Cheng, Fan Jin, Yaochu Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230039 Anhui Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.

关键词： Adaptive reference point evolutionary multiobjective optimization indicator-based selection many-objective optimization

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A new approach for solving Nonlinear equations systems

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2008年第3期38卷 698-714页

作者： Grosan, Crina Abraham, Ajith Univ Babes Bolyai Dept Comp Sci Cluj Napoca 400084 Romania Norwegian Univ Sci & Technol Ctr Excellence Q2S N-7491 Trondheim Norway

This paper proposes a new perspective for solving systems of complex nonlinear equations by simply viewing them as a multiobjective optimization problem. Every equation in the system represents an objective function whose goal is to minimize the difference between the right and left terms of the corresponding equation. An evolutionary computation technique is applied to solve the problem obtained by transforming the system into a multiobjective optimization problem. The results obtained are compared with a very new technique that is considered as efficient and is also compared with some of the standard techniques that are used for solving nonlinear equations systems. Several well-known and difficult applications (such as interval arithmetic benchmark, kinematic application, neuropsychology application,, combustion application, and chemical equilibrium application) are considered for testing the performance of the new approach. Empirical results reveal that the proposed approach is able to deal with high-dimensional equations systems.

关键词： computational intelligence evolutionary multiobjective optimization metaheuristics nonlinear equation systems

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Reliability-Based optimization Using evolutionary Algorithms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2009年第5期13卷 1054-1074页

作者： Deb, Kalyanmoy Gupta, Shubham Daum, David Branke, Juergen Mall, Abhishek Kumar Padmanabhan, Dhanesh Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India Helsinki Sch Econ Helsinki Finland MIT Ctr Operat Res Cambridge MA 02139 USA Swiss Fed Inst Technol Solar Energy & Bldg Phys Lab CH-1015 Lausanne Switzerland Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England Deutsch Bank Mumbai Maharashtra India Hewlett Packard Corp Decis Support & Analyt Serv Div Bangalore Karnataka India

Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist several mathematical approximations of a solution's reliability. These techniques are coupled in various ways with optimization in the classical reliability-based optimization field. This paper demonstrates how classical reliability-based concepts can be borrowed and modified and, with integrated single and multiobjective evolutionary algorithms, used to enhance their scope in handling uncertainties involved among decision variables and problem parameters. Three different optimization tasks are discussed in which classical reliability-based optimization procedures usually have difficulties, namely 1) reliability-based optimization problems having multiple local optima, 2) finding and revealing reliable solutions for different reliability indices simultaneously by means of a bi-criterion optimization approach, and 3) multiobjective optimization with uncertainty and specified system or component reliability values. Each of these optimization tasks is illustrated by solving a number of test problems and a well-studied automobile design problem. Results are also compared with a classical reliability-based methodology.

关键词： Ditlevsen's bound evolutionary multiobjective optimization most probable point Pareto-optimal front reliability-based optimization reliable front system reliability

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multiobjective structural optimization using a microgenetic algorithm

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2005年第5期30卷 388-403页

作者： Coello, CAC Pulido, GT CINVESTAV IPN Evolutionary Computat Grp Dept Ingn RlectSecc Computac Mexico City 07360 DF Mexico

In this paper, we present a genetic algorithm with a very small population and a reinitialization process (a microgenetic algorithm) for solving multiobjective optimization problems. Our approach uses three forms of elitism, including an external memory (or secondary population) to keep the nondominated solutions found along the evolutionary process. We validate our proposal using several engineering optimization problems taken from the specialized literature and compare our results with respect to two other algorithms (NSGA-II and PAES) using three different metrics. Our results indicate that our approach is very efficient (computationally speaking) and performs very well in problems with different degrees of complexity.

关键词： evolutionary multiobjective optimization genetic algorithms multiobjective optimization vector optimization

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EvoOligo: Oligonucleotide Probe Design With multiobjective evolutionary Algorithms

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2009年第6期39卷 1606-1616页

作者： Shin, Soo-Yong Lee, In-Hee Cho, Young-Min Yang, Kyung-Ae Zhang, Byoung-Tak Seoul Natl Univ Hosp Med Informat Ctr Seoul 110744 South Korea Seoul Natl Univ Sch Comp Sci & Engn Biointelligence Lab Seoul 151742 South Korea Univ Calif San Diego La Jolla CA 92093 USA Seoul Natl Univ CBIT Sch Comp Sci & Engn Seoul 151742 South Korea

Probe design is one of the most important tasks in successful deoxyribonucleic acid microarray experiments. We propose a multiobjective evolutionary optimization method for oligonucleotide probe design based on the multiobjective nature of the probe design problem. The proposed multiobjective evolutionary approach has several distinguished features, compared with previous methods. First, the evolutionary approach can find better probe sets than existing simple filtering methods with fixed threshold values. Second, the multiobjective approach can easily incorporate the user's custom criteria or change the existing criteria. Third, our approach tries to optimize the combination of probes for the given set of genes, in contrast to other tools that independently search each gene for qualifying probes. Lastly, the multiobjective optimization method provides various sets of probe combinations, among which the user can choose, depending on the target application. The proposed method is implemented as a platform called EvoOligo and is available for service on the web. We test the performance of EvoOligo by designing probe sets for 19 types of Human Papillomavirus and 52 genes in the Arabidopsis Calmodulin multigene family. The design results from EvoOligo are proven to be superior to those from well-known existing probe design tools, such as OligoArray and OligoWiz.

关键词： evolutionary multiobjective optimization EvoOligo microarray probe design epsilon-multiobjective evolutionary algorithm (epsilon-MOEA)

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