An improved Multiobjective Evolutionary Algorithm based on decomposition with fuzzy dominance

Nasir, .; Mondal, Arnab Kumar; Sengupta, Shamik; Das, Swagatam; Abraham, Ajith

doi:10.1109/cec.2011.5949696

Cited by 26 publications

(12 citation statements)

References 22 publications

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“…There exist some delicate distinctions between the proposed method and three other fuzzy-based approaches [14][15][16]. The distinctions can be roughly divided into three steps: fuzzification, inference and fuzzy rules, and defuzzification.…”

Section: F Comparison Between Our Methods With Othersmentioning

confidence: 99%

“…In [15], a fuzzy Pareto dominance concept is introduced to compare two solutions and uses the scalar decomposition method of MOEA/D only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level.…”

Section: B Previous Work In Fuzzy Dominancementioning

confidence: 99%

“…3) Method by Nasir et al, [15] • Fuzzification If one solution is better than the other in one objective, its membership function value of this objective is 1; otherwise, this value will be determined by the difference between two solutions in this objective through a pre-defined membership function.…”

Section: • Defuzzificationmentioning

confidence: 99%

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A new fitness evaluation method based on fuzzy logic in multiobjective evolutionary algorithms

Yen

2012

2012 IEEE Congress on Evolutionary Computation

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Evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general. However, when encounter problems with many objectives (more than five), nearly all algorithms performs poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto domination. In this paper, we introduce a new fitness evaluation mechanism to continuously differentiate solutions into different degrees of optimality beyond the classification of the original Pareto dominance. Here, the concept of fuzzy logic is adopted to define fuzzy-dominated relation. As a case study, the fuzzy concept is incorporated into the NSGA-II, instead of the original Pareto dominance principle. Experimental results show that the proposed method exhibits a better performance in both convergence and diversity than the original NSGA-II for solving manyobjective optimization problems. More importantly, it enables a fast convergence process.Index Terms-Pareto optimality, multiobjective evolutionary algorithm, fuzzy logic, NSGA-II I.

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Section: F Comparison Between Our Methods With Othersmentioning

confidence: 99%

Section: B Previous Work In Fuzzy Dominancementioning

confidence: 99%

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A new fitness evaluation method based on fuzzy logic in multiobjective evolutionary algorithms

Yen

2012

2012 IEEE Congress on Evolutionary Computation

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“…The same two well-known algorithms are also engaged altogether for dealing with a hard multiobjective optimization problem in [134]. The concepts of fuzzy dominance are recently introduced in the MOEA/D framework [143] for enhancement of MOEA/D paradigm. The impact of the ensemble use of the different neighbourhood sizes are recently investigated in [188] based on self-adaptive procedures.…”

Section: Decomposition Based Multi-objective Evolutionary Algorithmmentioning

confidence: 99%

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

Khan¹,

Salhi²,

Jan³

et al. 2016

ijacsa

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Abstract-In the last two decades, multiobjective optimization has become mainstream because of its wide applicability in a variety of areas such engineering, management, the military and other fields. Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions. They aim at finding a set of representative Pareto optimal solutions in a single run. Classical MOEAs are broadly in three main groups: the Pareto dominance based MOEAs, the Indicator based MOEAs and the decomposition based MOEAs. Those based on decomposition and indicator functions have shown high search abilities as compared to the Pareto dominance based ones. That is possibly due to their firm theoretical background. This paper presents state-of-the-art MOEAs that employ decomposition and indicator functions as fitness evaluation techniques along with other efficient techniques including those which use preference based information, local search optimizers, multiple ensemble search operators together with self-adaptive strategies, metaheuristics, mating restriction approaches, statistical sampling techniques, integration of Fuzzy dominance concepts and many other advanced techniques for dealing with diverse optimization and search problems

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“…These two algorithms have also been used in [48] to solve hard multiobjective optimization problems. Fuzzy Dominance (FD) concepts have been introduced in [50] to further improve the algorithmic behavior of the MOEA/D paradigm. The effect of the combined use of neighbourhood sizes with a self-adaptive strategy has been investigated in [75].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Hybrid adaptive evolutionary algorithm based on decomposition

Khan

Salhi

Yeniay

et al. 2017

Applied Soft Computing

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The performance of search operators varies across the different stages of the search/optimisation process of Evolutionary Algorithms (EA). In general, a single search operator may not do well in all these stages when dealing with different optimization and search problems. To mitigate this, adaptive search operator schemes have been introduced. The idea is that when a search operator hits a difficult patch (under-performs) in the search space, the EA scheme "reacts" to that by potentially calling upon a different search operator. Hence, several multiple-search operator schemes have been proposed and employed within EA. In this paper, a Hybrid Adaptive Evolutionary Algorithm Based on Decomposition (HAEA/D) that employs four different crossover operators is suggested. Its performance has been evaluated on the well-known IEEE CEC'09 test instances. HAEA/D has generated promising results which compare well against several well-known algorithms including MOEA/D, on a number of metrics such as the Inverted Generational Distance (IGD), the hyper-volume, the Gamma and Delta functions. These results are included and discussed in this paper.

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An improved Multiobjective Evolutionary Algorithm based on decomposition with fuzzy dominance

Cited by 26 publications

References 22 publications

A new fitness evaluation method based on fuzzy logic in multiobjective evolutionary algorithms

A new fitness evaluation method based on fuzzy logic in multiobjective evolutionary algorithms

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

Hybrid adaptive evolutionary algorithm based on decomposition

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