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A parallel hybrid GA-PSO approach with dynamic rule-based parameter setting

Published: 15 July 2017 Publication History

Abstract

We present an new optimisation framework combining two meta-heuristics: Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO). In contrast to the usual hybridisation models in which the second algorithm is applied to work on the final results of the first one, our approach uses both algorithms in parallel on the same population in a competetive manner. The algorithms can work on and improve the solutions of each other, thus more diversity and better quality can be achieved in the population. Another improving factor is resetting the population size and the parameters in every iteration according to the diversity and quality of the solutions in the last population. Our approach is tested on five well-known benchmark problems. The merit of our approach is verified by comparing its performance with the pure GA and PSO, hybrids where PSO works after GA, and vice versa, as well as another hybrid approach of these algorithms from the literature.

References

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W. Chen, M. Nguyen, W. Chiu, T. Chen, and P. Tai. 2016. Optimization of the plastic injection molding process using the Taguchi method, RSM, and hybrid GA-PSO. The International Journal of Advanced Manufacturing Technology 83, 9 (2016), 1873--1886.
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H. Garg. 2016. A hybrid PSO-GA algorithm for constrained optimization problems. Appl. Math. Comput. 274 (2016), 292--305.
[3]
M. Ghovvati, G. Khayati, H. Attar, and A. Vaziri. 2016. Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization. Biotechnology & Biotechnological Equipment 30, 2 (2016), 404--410.
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R. L. Haupt and S. E. Haupt. 2004. Practical Genetic Algorithms. Wiley.
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J. Kennedy, R. C. Eberhart, and Y. Shi. 2001. Swarm intelligence. Morgan Kaufman.
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G. R. Raidl. 2006. A Unified View on Hybrid Metaheuristics. Springer, 1--12.
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M. Settles and T. Soule. 2005. Breeding Swarms: A GA/PSO Hybrid. In ACM Annual Conference on Genetic and Evolutionary Computation (GECCO). 161--168.

Cited By

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  • (2018)Nature Inspired Methods and Their Industry Applications—Swarm Intelligence AlgorithmsIEEE Transactions on Industrial Informatics10.1109/TII.2017.278678214:3(1004-1015)Online publication date: Mar-2018

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cover image ACM Conferences
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2017
1934 pages
ISBN:9781450349390
DOI:10.1145/3067695
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

Publication History

Published: 15 July 2017

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Author Tags

  1. dynamic parameter setting
  2. genetic algorithm
  3. parallel hybridaration
  4. particle swarm optimisation

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GECCO '17
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Cited By

View all
  • (2018)Nature Inspired Methods and Their Industry Applications—Swarm Intelligence AlgorithmsIEEE Transactions on Industrial Informatics10.1109/TII.2017.278678214:3(1004-1015)Online publication date: Mar-2018

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