Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D

Ishibuchi, Hisao; Hitotsuyanagi, Yasuhiro; Ohyanagi, Hiroyuki; Nojima, Yusuke

doi:10.1007/978-3-642-19893-9_12

Cited by 26 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Aggregation-based MOEAs such as MOEA/D [18] decompose an MaOP into a number of singleobjective optimization problems using a set of pre-defined weight vectors, thereby avoiding the convergence problem. However, a limited number of weight vectors in the highdimensional space lead to poor diversity for MaOPs [19], [20]. Indicator-based MOEAs use an indicator as their fitness function to optimize an MaOP, which can be classified into three categories (distance-, hypervolume-, R2-based MOEAs) [10].…”

mentioning

confidence: 99%

Diversity Assessment in Many-Objective Optimization

Wang

Jin

Yao

2017

IEEE Trans. Cybern.

208

View full text Add to dashboard Cite

Abstract-Maintaining diversity is one important aim of multiobjective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multi-objective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for manyobjective optimization, which is an accumulation of the dissimilarity in the population, where an Lp-norm-based (p < 1) distance is adopted to measure the dissimilarity of solutions. Empirical results demonstrate our proposed metric can more accurately assess the diversity of solutions in various situations. We compare the diversity of the solutions obtained by four popular many-objective evolutionary algorithms using the proposed diversity metric on a large number of benchmark problems with two to ten objectives. The behaviors of different diversity maintenance methodologies in those algorithms are discussed in depth based on the experimental results. Finally, we show that the proposed diversity measure can also be employed for enhancing diversity maintenance or reference set generation in many-objective optimization.

show abstract

mentioning

confidence: 99%

Diversity Assessment in Many-Objective Optimization

Wang

Jin

Yao

2017

IEEE Trans. Cybern.

208

View full text Add to dashboard Cite

show abstract

“…Recentemente, há um crescente interesse na aplicação de AEMOs para resolver problemas de otimização com muitos objetivos [2,102,101,99,197], aqueles que tratam de quatro ou mais objetivos. No entanto, em geral, AEMOs não obtém soluções adequadas com grande número de critérios de seleção [102].…”

Section: Problemas De Otimização Com Muitos Objetivosunclassified

“…No entanto, no trabalho de Ishibuchi (2011) [99], foi verificado que o seu desempenho em problemas multiobjetivos com objetivos altamente correlacionados é prejudicado, enquanto que os algoritmos NSGA-II e SPEA2 apresentam poucos efeitos negativos quando tratam de objetivos com alta similaridade. Também na literatura não há resultados de sucesso do MOEA-D para problemas com mais de dez objetivos.…”

Section: Problemas De Otimização Com Muitos Objetivosunclassified

“…Observe que a ideia de usar diversas funções de ponderação em AEMOs de muitos objetivos (em geral com três a dez objetivos) foi também empregada por Ishibuchi em [99] com o método MOEA-D, descrito no Capítulo 4, o qual também aplica a ponderação entre os objetivos. A diferença está justamente na ponderação dos objetivos no MOEA-D, baseada na Decomposição de Tchebycheff [130], utilizando as soluções de subproblemas vizinhos para realizar as operações de reprodução (cruzamento e mutação).…”

Section: Descrição Do Protpredunclassified

“…Problemas com mais que três objetivos têm sido classificados com prolemas de muitos objetivos [99]. O aspecto de muitos objetivos pode reduzir drasticamente o desempenho de um AEMO desenvolvido para até três objetivos.…”

Section: Introductionunclassified

See 2 more Smart Citations

Algoritmo evolutivo de muitos objetivos para predição ab initio de estrutura de proteínas

Brasil¹

View full text Add to dashboard Cite

Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction

2013

View full text Add to dashboard Cite

This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment-based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well-designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with β-sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called "Multiobjective evolutionary algorithms with many tables" (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPFCG , I-PAES, and Quark) that use different levels of earlier knowledge.

show abstract

Effects of the Existence of Highly Correlated Objectives on the Behavior of MOEA/D

Cited by 26 publications

References 33 publications

Diversity Assessment in Many-Objective Optimization

Diversity Assessment in Many-Objective Optimization

Algoritmo evolutivo de muitos objetivos para predição ab initio de estrutura de proteínas

Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction

Contact Info

Product

Resources

About