Evolutionary many-objective optimization

Ishibuchi, Hisao; Tsukamoto, Noritaka; Nojima, Yusuke

doi:10.1109/gefs.2008.4484566

Cited by 181 publications

(89 citation statements)

References 35 publications

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“…7. It is clear that the probability decreases rapidly with the increasing number of objectives, and it becomes almost impossible that solutions with more than 12 objectives in a random population can be dominated [35]. It is necessary to note that non-dominated sorting can still determine a few candidate solutions in the combined population unsuitable for surviving for next population in solving MaOPs, which is helpful for promoting population of KnEA to converge to the Pareto fronts.…”

Section: Effectiveness For Many-objective Optimizationmentioning

confidence: 99%

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Tian

Wang

Zhang

et al. 2017

Complex Intell. Syst.

104

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Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary algorithms have employed non-dominated sorting as one of the major criteria in their environmental selection for solving multi-and many-objective optimization problems. In this paper, we focus on analyzing the effectiveness and efficiency of nondominated sorting in multi-and many-objective evolutionary algorithms. The effectiveness of non-dominated sorting is verified by considering two popular evolutionary algorithms, NSGA-II and KnEA, which were designed for solving multiand many-objective optimization problems, respectively. The efficiency of non-dominated sorting is evaluated by comparing several state-of-the-art non-dominated sorting algorithms for multi-and many-objective optimization problems. These results provide important insights to adopt non-dominated sorting in developing novel multi-and many-objective evolutionary algorithms.

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Section: Effectiveness For Many-objective Optimizationmentioning

confidence: 99%

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Tian

Wang

Zhang

et al. 2017

Complex Intell. Syst.

104

View full text Add to dashboard Cite

show abstract

“…Though such multi-objective problems are prevalent, it is well known that optimisation problems often comprise a large set of objectives that must be simultaneously optimised [10]. Problems with four or more objectives are often called many-objective problems.…”

Section: Introductionmentioning

confidence: 99%

Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

Walker

Keedwell

2016

Parallel Problem Solving From Nature – PPSN XIV

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Abstract. The use of hyper-heuristics is increasing in the multi-objective optimisation domain, and the next logical advance in such methods is to use them in the solution of many-objective problems. Such problems comprise four or more objectives and are known to present a significant challenge to standard dominance-based evolutionary algorithms. We incorporate three comparison operators as alternatives to dominance and investigate their potential to optimise many-objective problems with a hyper-heuristic from the literature. We discover that the best results are obtained using either the favour relation or hypervolume, but conclude that changing the comparison operator alone will not allow for the generation of estimated Pareto fronts that are both close to and fully cover the true Pareto front.

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“…In di Pierro et al, 2007;Li and Wong, 2009;Sülflow et al, 2007) relations are presented that distinguish between solutions that are incomparable if the Dominates relation is considered. An overview and a comparison of these methods is given in (Corne and Knowles, 2007;Ishibuchi et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…(Fleming et al, 2005;Corne and Knowles, 2007;Ishibuchi et al, 2008;Brockhoff and Zitzler, 2009;Bader and Zitzler, 2011)). A promising approach in evolutionary many-objective optimization is objective reduction based on the Dominates relation ).…”

Section: Introductionmentioning

confidence: 99%

Incorporating user preferences in many-objective optimization using relation ε-preferred

Drechsler

Sülflow²,

Drechsler

2014

Nat Comput

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Abstract:During the last 10 years, many-objective optimization problems, i.e. optimization problems with more than three objectives, are getting more and more important in the area of multi-objective optimization. Many realworld optimization problems consist of more than three mutually dependent subproblems, that have to be considered in parallel. Furthermore, the objectives have different levels of importance. For this, priorities have to be assigned to the objectives. In this paper we present a new model for many-objective optimization called Prio-ε-Preferred, where the objectives can have different levels of priorities or user preferences. This relation is used for ranking a set of solutions such that an ordering of the solutions is determined. Prio-ε-Preferred is controlled by a parameter ε, that is problem specific and has to be adjusted experimentally by the designer. Therefore we also present an extension called Adapted-ε-Preferred (AEP), that determines the ε values automatically without any user interaction. To demonstrate the efficiency of our approach, experiments are performed.

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Evolutionary many-objective optimization

Cited by 181 publications

References 35 publications

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization

Towards Many-Objective Optimisation with Hyper-heuristics: Identifying Good Heuristics with Indicators

Incorporating user preferences in many-objective optimization using relation ε-preferred

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