Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions

Ishibuchi, Hisao; Tsukamoto, Noritaka; Sakane, Yuji; Nojima, Yusuke

doi:10.1145/1830483.1830578

Cited by 67 publications

(35 citation statements)

References 20 publications

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“…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%

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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“…After several decades of effort to the field of evolutionary computation, a huge number of MOEAs are available to date. They can be classified into three main groups: Pareto-based approaches (Deb et al, 2002;Knowles and Corne, 1999;Zitzler et al, 2002;Deb and Jain, 2014), indicator-based approaches (Bader and Zitzler, 2011;Beume et al, 2007;Ishibuchi et al, 2010;Zitzler and Kunzli, 2004), and decomposition-based approaches (Zhang and Li, 2007;Li and Zhang, 2009;Asafuddoula et al, 2015). The MOEA based on decomposition (MOEA/D) is the most well-known representative of decomposition-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

Yang

Jiang

2016

Soft Comput

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It has been increasingly reported that the multiobjective optimization evolutionary algorithm based on decomposition (MOEA/D) is promising for handling multiobjective optimization problems (MOPs). MOEA/D employs scalarizing functions to convert an MOP into a number of single-objective subproblems. Among them, penalty boundary intersection (PBI) is one of the most popular decomposition approaches and has been widely adopted for dealing with MOPs. However, the original PBI uses a constant penalty value for all subproblems and has difficulties in achieving a good distribution and coverage of the Pareto front for some problems. In this paper, we investigate the influence of the penalty factor on PBI, and suggest two new penalty schemes, i.e., adaptive penalty scheme (APS) and subproblem-based penalty scheme (SPS), to enhance the spread of Pareto optimal solutions. The new penalty schemes are examined on several complex MOPs, showing that PBI with the use of them is able to provide a better approximation of the Pareto front than the original one. The SPS is further integrated into two recently developed MOEA/D variants to help balance the population diversity and convergence. Experimental results show that it can significantly enhance the algorithm's performance.

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“…For example, the Indicator Based Evolutionary Algorithms (IBEAs) [41,100,102] use performance indicators as a method of introducing selection pressure in place of dominance based selection, such as [21]. Amongst these indicators the hypervolume indicator, see e.g.…”

Section: Introductionmentioning

confidence: 99%

Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm

Rostami

Neri

2016

ICA

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Abstract. Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective optimisation problems, are especially challenging when more than three objectives are considered simultaneously. This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel selection mechanism is introduced and integrated within the framework. This selection mechanism makes use of an adaptive grid to perform a local approximation of the hypervolume indicator which is then used as a selection criterion. The proposed implementation, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA-PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems. The performance of CMA-PAES-HAGA has been compared with that of a competition winning meta-heuristic, representing the state-of-the-art in this sub-field of multi-objective optimisation.The proposed algorithm has been tested in a seven-objective real-world application, i.e. the design of an aircraft lateral control system. In this optimisation problem, CMA-PAES-HAGA greatly outperformed its competitors.

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Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions

Cited by 67 publications

References 20 publications

Hybrid adaptive evolutionary algorithm based on decomposition

Hybrid adaptive evolutionary algorithm based on decomposition

Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm

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