GD-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Generational Distance Indicator

Menchaca-Méndez, Adriana; Coello, Carlos A. Coello

doi:10.1007/978-3-319-15934-8_11

Cited by 29 publications

(13 citation statements)

References 22 publications

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“…For example, Trautmann et al developed an R2 indicator in [34] for solving MOPs, and Gómez and Coello Coello proposed two extensions of R2 based MOEAs for solving MaOPs, termed MOMBI [35] and MOMBI-II [21], respectively; Menchaca-Mendez and Coello Coello proposed a GD indicator based MOEA, termed GD-MOEA [36], and an improved version GDE-MOEA [20] by incorporating the ϵ-dominance into the GD-MOEA algorithm; Rudolph et al [37] developed an MOEA based on the ∆ p indictor, termed AS-MOEA, where ∆ p is a modified composition of GD and IGD [38]- [40].…”

Section: A Indicator Based Moeasmentioning

confidence: 99%

An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

Tian

Cheng

Zhang

et al. 2018

IEEE Trans. Evol. Computat.

546

157

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Abstract-During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multi-objective and many-objective optimization.

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Section: A Indicator Based Moeasmentioning

confidence: 99%

An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

Tian

Cheng

Zhang

et al. 2018

IEEE Trans. Evol. Computat.

546

157

View full text Add to dashboard Cite

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“…Indicator-based MOEAs directly employ a performance indicator, like hypervolume (HV), generational distance (GD), and R2, to effectively guide the selection of promising solutions for next generation. IBEA [25], MOMBI-II [26], HyPE [8], GD-MOEA [27], R2-IBEA [28], and DDE [29] are representatives for the indicator-based MOEAs. In these MOEAs, performance indicators are used as selection criterion to rank nondominated solutions that cannot be distinguished by traditional Pareto dominance.…”

Section: Indicator-based Moeasmentioning

confidence: 99%

Decomposition-Based Multiobjective Evolutionary Optimization with Adaptive Multiple Gaussian Process Models

Zhang

Gong

et al. 2020

Complexity

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In recent years, a number of recombination operators have been proposed for multiobjective evolutionary algorithms (MOEAs). One kind of recombination operators is designed based on the Gaussian process model. However, this approach only uses one standard Gaussian process model with fixed variance, which may not work well for solving various multiobjective optimization problems (MOPs). To alleviate this problem, this paper introduces a decomposition-based multiobjective evolutionary optimization with adaptive multiple Gaussian process models, aiming to provide a more effective heuristic search for various MOPs. For selecting a more suitable Gaussian process model, an adaptive selection strategy is designed by using the performance enhancements on a number of decomposed subproblems. In this way, our proposed algorithm owns more search patterns and is able to produce more diversified solutions. The performance of our algorithm is validated when solving some well-known F, UF, and WFG test instances, and the experiments confirm that our algorithm shows some superiorities over six competitive MOEAs.

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“…The most representative scalarization-based method is MOEA/D (Ishibuchi et al, 2006), which has received increasing attention due to its computational simplicity and attractive search performance (Denysiuk et al, 2015;. Meanwhile, indicator-based approaches (Zitzler and Künzli, 2004;Bader and Zitzler, 2011;Rodríguez Villalobos and Coello Coello, 2012;Menchaca-Mendez and Coello, 2015), as a relatively recent trend, employ performance indicators for fitness assignment. More studies about the development and application of MOEAs are well reviewed by Zhou et al (2011) or Eiben and Smith (2015).…”

Section: Introductionmentioning

confidence: 99%

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

Zuo

Zhao-fu

et al. 2017

International Journal of Applied Mathematics and Computer Science

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The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.

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GD-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Generational Distance Indicator

Cited by 29 publications

References 22 publications

An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility

Decomposition-Based Multiobjective Evolutionary Optimization with Adaptive Multiple Gaussian Process Models

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

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