A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation

Khan, Wali Ullah; Salhi, Abdellah

doi:10.1016/j.asoc.2012.03.067

Cited by 57 publications

(29 citation statements)

References 10 publications

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“…In [73], resources are allocated dynamically to each sub-problem as used in the MOEA/D paradigm. In [29,42,28,46,73], the impact of multiple search operators coupled to a self-adaptive scheme has been studied. It has then been tested on instances designed for the special session on MOEA competition at the Congress of Evolutionary Computing of 2009 (CEC'09), [74].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…In general, MOEAs can be divided into three main categories based on fitness assignment strategies; they are the Pareto dominance based MOEAs (e.g., [9,13,78,77,51,21]), the Decomposition based MOEAs (e.g., [22,33,73,38,42,44,24,25,26]), and the Indicator based algorithms (e.g., [80,5,23,3,2,14]). Pareto dominance MOEAs use explicitly the Pareto dominance concept in order to determine the reproduction probability of each individual of its population.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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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%

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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“…they have successfully tackled various types of MOPs [16], [25], [35], [37], [33]. In general, classical MOEAs can be divided into three main different classes, namely, the Pareto dominance based MOEAs (e.g., [11], [13], [61], [60], [44], [19], [18], [9], [27]), the decomposition based MOEAs (e.g., [21], [20], [54], [8], [7], [30], [56], [58], [55], [1], [34], [32], [39], [42], [41], [26]), and Indicator Based algorithms (e.g., [63], [5], [3], [22], [4], [2], [14]). All these algorithms try to obtain a set of Pareto optimal solutions with three main features, firstly, It should close as much as possible to the true PF.…”

Section: Introductionmentioning

confidence: 99%

“…MOEAs are highly effective and powerful stochastic techniques which can find a set optimal solutions in a single simulation run due to their population-based nature, unlike traditional mathematical programming.In the past two decades, and since the inception of vector evaluated GA (VEGA) [48], different types of MOEAs have been suggested, the Pareto dominance based MOEAs [11], [13], [61], [60], [44], [19], [18], [9], [27]), the decomposition based MOEAs [21], [20], [54], [8], [7], [30], [56], [58], [55], [1], [34], [32], [39], [42], [41], [26], [25], [35], [37], [33], [41]), and Indicator Based algorithms [63], [5], [3], [22], [4], [2], [14]. They mainly emphasize three conflicting goals: firstly, the final approximate Pareto front (PF) should be as close as possible to the true PF; secondly, the final set of Pareto optimal solutions should be uniformly distributed and diverse over the true PF of the problem (1); thirdly, the approximated PF should capture the whole spectrum of the true PF.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization

Khan¹,

Salhi²,

Asif³

et al. 2015

ijacsa

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Abstract-Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a selfadaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA's competition at the Congress of Evolutionary Computing of 2009 (CEC'09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator.

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Improved strength Pareto evolutionary algorithm based on reference direction and coordinated selection strategy

Chen

Jiang

et al. 2021

Int J of Intelligent Sys

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In the field of evolutionary algorithms, Pareto‐based algorithms are less effective when more than three objectives are encountered, due to the lack of sufficient selection pressure. In this paper, a Pareto‐based many‐objective evolutionary algorithm with reference direction and coordinated selection strategy, abbreviated as SPEACSS, is proposed. The algorithm inherits the fitness calculation strategy of the strength Pareto evolutionary algorithm, while it applied an efficient reference direction‐based density estimator and a novel selection strategy. Moreover, mating selection and environmental selection are complementary and coordinated in the evolutionary process and have better performance than be used alone. In the criteria of mating selection, a method is given to improve the effectiveness of the parent combination. Experimental results on benchmark functions show that the proposed algorithm is superior to several state‐of‐the‐art designs, and demonstrate the effectiveness of the improved algorithm in balancing diversity and convergence.

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A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation

Cited by 57 publications

References 10 publications

Hybrid adaptive evolutionary algorithm based on decomposition

Hybrid adaptive evolutionary algorithm based on decomposition

Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization

Improved strength Pareto evolutionary algorithm based on reference direction and coordinated selection strategy

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