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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2011: Evolutionary Multi-Criterion Optimization pp 182–196Cite as

Improved Random One-Bit Climbers with Adaptive ε-Ranking and Tabu Moves for Many-Objective Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6576))

Abstract

Multi-objective random one-bit climbers (moRBCs) are one class of stochastic local search-based algorithms that maintain a reference population of solutions to guide their search. They have been shown to perform well in solving multi-objective optimization problems. In this work, we analyze the performance of moRBCs when modified by introducing tabu moves. We also study their behavior when the selection to update the reference population and archive is replaced with a procedure that provides an alternative mechanism for preserving the diversity among the solutions. We use several MNK-landscape models as test instances and apply statistical testings to analyze the results. Our study shows that the two modifications complement each other in significantly improving moRBCs’ performance especially in many-objective problems. Moreover, they can play specific roles in enhancing the convergence and spread of moRBCs.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Shinshu University, 4-17-1 Wakasato, Nagano, 380-8553, Japan

    Joseph M. Pasia, Hernán Aguirre & Kiyoshi Tanaka

  2. Institute of Mathematics, University of the Philippines-Diliman, 1101, Quezon City, Philippines

    Joseph M. Pasia

Authors
  1. Joseph M. Pasia
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  2. Hernán Aguirre
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  3. Kiyoshi Tanaka
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Federal University of Minas Gerais, Av. Antônio Carlos 6627, 31270-901, Belo Horizonte, MG, Brazil

    Ricardo H. C. Takahashi

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Department of Computer Engineering, Centro Federal de Educaçao Tecnológica de Minas Gerais, Av. Amazonas 5253, 30421-169, Belo Horizonte, MG, Brazil

    Elizabeth F. Wanner

  4. Faculty of Economics, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

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© 2011 Springer-Verlag Berlin Heidelberg

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Pasia, J.M., Aguirre, H., Tanaka, K. (2011). Improved Random One-Bit Climbers with Adaptive ε-Ranking and Tabu Moves for Many-Objective Optimization. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds) Evolutionary Multi-Criterion Optimization. EMO 2011. Lecture Notes in Computer Science, vol 6576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_13

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  • DOI: https://doi.org/10.1007/978-3-642-19893-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19892-2

  • Online ISBN: 978-3-642-19893-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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