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On the Hybridization of Particle Swarm Optimization Technique for Continuous Optimization Problems

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Advances in Swarm Intelligence (ICSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9712))

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Abstract

A hybrid particle swarm optimization (PSO) algorithm is proposed. In literature, the optimization algorithms that hybridize one PSO variant with another PSO variant are rare. In this paper, linear decreasing inertia PSO (LPSO) and random inertia weight PSO (RPSO) are hybridized to form a new hybrid PSO (NHPSO) algorithm. This algorithm addresses premature convergence associated with PSO technique when handling continuous optimization problems. RPSO periodically makes NHPSO jump out of any local optima and strengthens its searching ability while LPSO enhances the convergence ability of NHPSO. The performance of NHPSO is experimentally tested to verify the practicability and profitability of hybridizing two separate existing PSO variants to effectively handle continuous optimization problems. Results show that NHPSO is very successful, compared to some existing PSO variants. This implies that many more efficient algorithms could be built from hybridizing two or more existing PSO variants.

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Notes

  1. 1.

    The name used by the authors that proposed this variant is “Hybrid topology”.

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Authors and Affiliations

  1. School of Mathematics, Statistics and Computer Science, University of Kwazulu-Natal, Private Bag X54001, Durban, 4000, South Africa

    Akugbe Martins Arasomwan & Aderemi Oluyinka Adewumi

Authors
  1. Akugbe Martins Arasomwan
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  2. Aderemi Oluyinka Adewumi
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Correspondence to Aderemi Oluyinka Adewumi .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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© 2016 Springer International Publishing Switzerland

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Arasomwan, A.M., Adewumi, A.O. (2016). On the Hybridization of Particle Swarm Optimization Technique for Continuous Optimization Problems. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_36

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  • DOI: https://doi.org/10.1007/978-3-319-41000-5_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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