abstract

Hybrid swarm of particle swarm with firefly for complex function optimization

Authors:

Heng Xiao,

Toshiharu HatanakaAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 73 - 74

https://doi.org/10.1145/3205651.3208776

Published: 06 July 2018 Publication History

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Abstract

Swarm intelligence is rather a simple implementation but has a good performance in function optimization. There are a variety of instances of swarm model and has its inherent dynamic property. In this study we consider a hybrid swarm model where agents complement each other using its native property. Employing popular swarm intelligence model Particle swarm and Firefly we consider hybridization methods in this study. This paper presents a hybridization that agents in Particle swarm selected by a simple rule or a random choice are changing its property to Firefly. Numerical studies are carried out by using complex function optimization benchmarks, the proposed method gives better performance compared with standard PSO.

References

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J. Kennedy, and R. C. Eberhart. 1995. Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, 4, 1942--1948.

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Yang, X-S. 'Firefly algorithms for multimodal optimization', Lecture Notes Computer Science 5792, Springer, 169--178, 2009.

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H. Xiao, T. Hatanaka 'Heterogeneous Swarm with First and Second Order Dynamics for Function Optimization', Proceedings of the 2016 IEEE Congress on Evolutionary Computation, pp. 2077--2082, 2016.

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H.Xiao and T.Hatanaka 'Hybrid Swarm Model with Changing Agent of Particle Swarm and Firefly', 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, Paper ID: #218, 2017.

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Q. Chen, B. Lin and Q. Zhang et al. 'Problem Definitions and Evaluation Criteria for CEC 2015 Special Session on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization', Technical Report of Computational Intelligence Laboratory of Zhengzhou University, and Technical Report of Nanyang Technological University, 2014.

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Index Terms

Hybrid swarm of particle swarm with firefly for complex function optimization
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization
      2. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 06 July 2018

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GECCO '18

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SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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A Modified Quantum-Behaved Particle Swarm Optimization

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Example-based learning particle swarm optimization for continuous optimization

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