On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

IEEE Transactions on Evolutionary Computation, vol. 25, pp. 681–695

Abstract

For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB-MOEA based on a single QI has a limited scope of multiobjective optimization problems (MOPs) in which it is expected to have a good performance. This issue is emphasized when the associated Pareto front geometries are highly irregular. In order to overcome these issues, we propose here an island-based multiindicator algorithm (IMIA) that takes advantage of the search biases of multiple IB-MOEAs through a cooperative scheme. Our experimental results show that the cooperation of multiple IB-MOEAs allows IMIA to perform more robustly (considering several QIs) than the panmictic versions of its baseline IB-MOEAs as well as several state-of-the-art MOEAs. Additionally, IMIA shows a Pareto-front-shape invariance property, which makes it a remarkable optimizer when tackling MOPs with complex Pareto front geometries.

Authors 4

1. Jesús Guillermo Falcón-Cardona

   Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional

   Affiliation as printed

   Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

   CINVESTAV-IPN, Mexico City, Mexico

2. Hisao Ishibuchi

   Southern University of Science and Technology

   Affiliation as printed

   Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

   Southern University of Science and Technology, Shenzhen, China

3. Carlos A. Coello Coello

   Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional · Ikerbasque · Basque Center for Applied Mathematics

   Affiliation as printed

   Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

   Basque Center for Applied Mathematics, Bilbao, Spain

   CINVESTAV-IPN, Mexico City, Mexico

   Ikerbasque, Bilbao, Spain

4. Michael Emmerich Aachen Multiobjective Optimization and Decision Analysis Group Leiden Institute of Advanced Computer Science

   Leiden University · University of Jyväskylä

   Affiliation as printed

   Multiobjective Optimization and Decision Analysis Group, Leiden Institute of Advanced Computer Science, Leiden, The Netherlands

   Multiobjective Optimization Group, Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland