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Multi-objective Particle Swarm Optimization with Alternate Learning Strategies

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Recent Trends in Mechatronics Towards Industry 4.0

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 730))

Abstract

An improved multi-objective particle swarm optimization (MOPSO) variant known as the MOPSO with alternate learning strategies (MOPSOALS) is proposed to overcome the drawbacks of most existing MOPSO variants that can only solve the selected categories of optimization problems with good performance due to the limited directional information brought by search operators. Particularly, both of the current and memory swarm evolution are incorporated into MOPSOALS as the more robust mechanisms in handling different types of problems. Two search operators are introduced in current swarm evolution to determine the particle’s new velocity, while three operators are proposed to fine tune the particle’s personal best position. These five proposed search operators are anticipated to guide all MOPSOALS particles to perform thorough searching in the solution search spaces with various exploration and exploitation strengths by fully utilizing all useful information contained in the non-dominated solution set. The proposed MOPSOALS is reported to have better performance in solving all selected test functions than the five peer algorithms.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Kuala Lumpur, Malaysia

    Wee Sheng Koh, Wei Hong Lim, Koon Meng Ang, Sew Sun Tiang, Chun Kit Ang & Mahmud Iwan Solihin

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Nibong Tebal, Malaysia

    Nor Ashidi Mat Isa

Authors
  1. Wee Sheng Koh
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  2. Wei Hong Lim
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  3. Koon Meng Ang
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  4. Nor Ashidi Mat Isa
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  5. Sew Sun Tiang
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  6. Chun Kit Ang
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  7. Mahmud Iwan Solihin
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Corresponding author

Correspondence to Wei Hong Lim .

Editor information

Editors and Affiliations

  1. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ahmad Fakhri Ab. Nasir

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ahmad Najmuddin Ibrahim

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ismayuzri Ishak

  4. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Nafrizuan Mat Yahya

  5. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Muhammad Aizzat Zakaria

  6. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Anwar P. P. Abdul Majeed

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Koh, W.S. et al. (2022). Multi-objective Particle Swarm Optimization with Alternate Learning Strategies. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_2

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