A multi-objective evolutionary hyper-heuristic based on multiple indicator-based density estimators

Falcón-Cardona, Jesús Guillermo; Coello, Carlos A. Coello

doi:10.1145/3205455.3205463

Cited by 15 publications

(9 citation statements)

References 25 publications

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“…The selection is conducted through an indicator called s-energy, which measures the even distribution of a set of points in k-dimensional manifolds. Combining different performance indicators within an indicator-based multi-objective solver is the proposal of [132], in which IGD + , +, ∆ p and R2 are adopted as possible density estimators (i.e., the low-level heuristics). Another strategy connected to hyper-heuristics and automatic algorithm composition is the combination of different off-the-shelf algorithms under a single control mechanism, as done in e.g.…”

Section: Multi-and Many-objective Optimizationmentioning

confidence: 99%

Bio-inspired computation: Where we stand and what's next

Ser

Osaba

Molina

et al. 2019

Swarm and Evolutionary Computation

496

106

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Section: Multi-and Many-objective Optimizationmentioning

confidence: 99%

Bio-inspired computation: Where we stand and what's next

Ser

Osaba

Molina

et al. 2019

Swarm and Evolutionary Computation

496

106

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“…Although there has been some research activity in this regard (see for example [113,140]) none of these other performance indicators has become as popular as the hypervolume. Another interesting idea is the combination of performance indicators in order to take advantage of their strengths and compensate for their limitations (see for example [48]). Nevertheless, a more relevant question in this area is the following: can we design MOEAs in a different way?…”

Section: Challengesmentioning

confidence: 99%

“…This paper concluded that applying selection hyper-heuristics to any of the three major components of a MOEA (selection, mutation and acceptance), can exponentially speed up the optimization process. Another interesting idea is to combine different performance indicators within an indicator-based MOEA as proposed by Falcón-Cardona and Coello [48]. In this case, IGD + , +, Δ p and R2 are adopted as possible density estimators (i.e., the low-level heuristics).…”

Section: Challengesmentioning

confidence: 99%

Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

Coello¹,

Brambila²,

Gamboa³

et al. 2019

Complex Intell. Syst.

126

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Evolutionary multiobjective optimization has been a research area since the mid-1980s, and has experienced a very significant activity in the last 20 years. However, and in spite of the maturity of this field, there are still several important challenges lying ahead. This paper provides a short description of some of them, with a particular focus on open research areas, rather than on specific research topics or problems. The main aim of this paper is to motivate researchers and students to develop research in these areas, as this will contribute to maintaining this discipline active during the next few years.

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“…In AGE, the fitness value of an individual is based on the contribution to the additive indicator [6] (not I + in IBEA [7]) using the unbounded external archive. Unlike most indicator-based MOEAs, MIHPS [49] adaptively uses multiple indicators. DIVA [50] uses an indicator that integrates the solution space diversity into the hypervolume indicator.…”

Section: Indicator-based Moeas For Multi-objective Optimizationmentioning

confidence: 99%

A niching indicator-based multi-modal many-objective optimizer

Tanabe

Ishibuchi

2019

Swarm and Evolutionary Computation

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Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi-and manyobjective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms.

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A multi-objective evolutionary hyper-heuristic based on multiple indicator-based density estimators

Cited by 15 publications

References 25 publications

Bio-inspired computation: Where we stand and what's next

Bio-inspired computation: Where we stand and what's next

Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

A niching indicator-based multi-modal many-objective optimizer

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