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Hybridizing particle swarm optimization with simulated annealing and differential evolution

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Abstract

Based on the algorithm structure, each metaheuristic algorithm may have its pros and cons, which may result in high performance in some problems and low functionality in some others. The idea is to hybridize two or more algorithms to cover each other’s weaknesses. In this study, particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) are combined to develop a more powerful search algorithm. First, the temperature concept of SA is applied to balance the exploration/exploitation capability of the hybridized algorithm. Then, the DE’s mutation operator is used to improve the exploration capability of the algorithm to escape the local minimums. Next, DE’s mutation operator has been modified so that past experiences can be used for smarter mutations. Finally, the PSO particles’ tendency to their local optimums or the global optimum, which balances the algorithm’s random and greedy search, is affected by the temperature. The temperature influences the algorithm’s behavior so that the random search is more significant at the beginning, and the greedy search becomes more important as the temperature is reduced. The results are compared with the basic PSO, SA, DE, cuckoo search (CS), and hybridized CS-PSO algorithm on 20 benchmark problems. The comparison reveals that, in most cases, the new algorithm outperforms others.

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Acknowledgements

The authors thank the editors and the anonymous referees for their valuable and constructive suggestions on this work, which improved the content substantially.

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Authors and Affiliations

  1. Computer Center, Institute for Advanced Studies in Basic Sciences (IASBS), 45136-66731, Zanjan, Iran

    Emad Mirsadeghi

  2. Mechanical Engineering Department, Engineering Faculty, University of Tehran, 14174-14418, Tehran, Iran

    Emad Mirsadeghi

  3. Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), 45136-66731, Zanjan, Iran

    Salman Khodayifar

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  1. Emad Mirsadeghi
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  2. Salman Khodayifar
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Correspondence to Salman Khodayifar.

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Mirsadeghi, E., Khodayifar, S. Hybridizing particle swarm optimization with simulated annealing and differential evolution. Cluster Comput 24, 1135–1163 (2021). https://doi.org/10.1007/s10586-020-03179-y

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