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C-Multi: A competent multi-swarm approach for many-objective problems

NEUROCOMPUTING 2016年 180卷 68-78页

作者： Castro, Olacir R., Jr. Santana, Roberto Pozo, Aurora Fed Univ Parana UFPR Dept Comp Sci PO 19081 BR-81531970 Curitiba Parana Brazil Univ Basque Country UPV EHU Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence Paseo Manuel de Lardizabal 1 San Sebastian 20080 Guipuzcoa Spain

One of the major research topics in the evolutionary multi-objective community is handling a large number of objectives also known as many-objective optimization problems (MaOPs). Most existing methodologies have demonstrated success for problems with two and three objectives but face significant challenges in many-objective optimization. To tackle these challenges, a hybrid multi-swarm algorithm called C-Multi was proposed in a previous work. The project of C-Multi is based on two phases;the first uses a unique particle swarm optimization (PSO) algorithm to discover different regions of the Pareto front. The second phase uses multiple swarms to specialize on a dedicate part. On each sub swarm, an estimation of distribution algorithm (EDA) is used to focus on convergence to its allocated region. In this study, the influence of two critical components of C-Multi, the archiving method and the number of swarms, is investigated by empirical analysis. As a result of this investigation, an improved variant of C-Multi is obtained, and its performance is compared to I-Multi, a multi-swarm algorithm that has a similar approach but does not use EDAs. Empirical results fully demonstrate the superiority of our proposed method on almost all considered test instances. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization Many-objective Estimation of distribution algorithm competent algorithm

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A hybrid competent multi-swarm approach for many-objective problems 3

A hybrid competent multi-swarm approach for many-objective p...

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3rd Brazilian Conference on Intelligent Systems (BRACIS)

作者： Castro, Olacir R., Jr. Pozo, Aurora Univ Fed Parana Dept Comp Sci BR-80060000 Curitiba Parana Brazil

ISBN: (纸本)9781479978595

Many-objective optimization problems (MaOPs) are a class of multi-objective problems that presents more than three functions to be optimized. As most Pareto based algorithms scale poorly according to the number of objectives, researchers are working on alternatives to overcome these limitations. An algorithm that has shown good results in solving MaOPs is the Iterated Multi-swarm (I-Multi) which presents a clever multi-swarm strategy to spread the solutions across different areas of the objective space while keeping a good convergence. As the I-Multi is a very recent algorithm, alternative approaches are yet to be explored. Here we investigate the use of an Estimation of Distribution algorithm (EDA) in the multi-swarm stage of I-Multi. EDAs create a model based on the best solutions found and sample new solutions based in this model. An EDA that presents good performance is the rBOA which is a real-valued version of the Bayesian optimization algorithm. This work presents an algorithm called C-Multi consisting of a hybrid between the I-Multi and the rBOA with the aim to join the diversity strength of I-Multi and the convergence characteristic of rBOA. An experimental study is conducted using the seven well-known DTLZ test functions with 3, 5, 10, 15 and 20 objectives to evaluate the performance of the algorithms as the number of objectives scales up. The results point that the new algorithm presents superior convergence and diversity on hard problems.

关键词： Particle Swarm Optimization Many-objective Estimation of density algorithm competent algorithm

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