Decomposition-Based Multiobjective Evolutionary Algorithm With an Ensemble of Neighborhood Sizes

Zhao, Shi-Zheng; Suganthan, Ponnuthurai Nagaratnam; Zhang, Qingfu

doi:10.1109/tevc.2011.2166159

Cited by 217 publications

(82 citation statements)

References 8 publications

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“…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]. For more details please refer to [38,41,76].…”

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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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 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. Very recently, the impact of multiple crossovers are examined in [131] using MOEA/D-DRA [185] as global search technique and experiment carried over CEC'09 test instances [187].…”

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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“…Zhu et al [17] design a novel recombination operator possessing the advantages of both the DE and the SBX. Its adaptive parameter control strategy is to allocate probabilities for SBX and DE according to the search period, whereas Zhao et al [20] suggest that the different neighborhood sizes have an unavoidable influence on the search power of operators based on the framework of MOEA/D, and the experiments imply that adaptive selection of neighborhood sizes works very well.…”

Section: Introductionmentioning

confidence: 99%

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Gao

Qing-hua

Fan

et al. 2017

Mathematical Problems in Engineering

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The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems.

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Decomposition-Based Multiobjective Evolutionary Algorithm With an Ensemble of Neighborhood Sizes

Cited by 217 publications

References 8 publications

Hybrid adaptive evolutionary algorithm based on decomposition

Hybrid adaptive evolutionary algorithm based on decomposition

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

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

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