Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization With Ring Topology

  • Michal Pluháček Tomas Bata University in Zlin
  • Anezka Kazikova Tomas Bata University in Zlin
  • Tomas Kadavy Tomas Bata University in Zlin
  • Adam Viktorin Tomas Bata University in Zlin
  • Roman Senkerik Tomas Bata University in Zlin
Keywords: Particle Swarm Optimization, Population Diversity, Neighborhood, Ring Topology, LPSO

Abstract

Measuring the population diversity in metaheuristics has become a common practice for adaptive approaches, aiming mainly to address the issue of premature convergence. Understanding the processes leading to a diversity loss in a metaheuristic algorithm is crucial for designing successful adaptive approaches. In this study, we focus on the relation of the neighborhood size and the rate of diversity loss in the Particle Swarm Optimization algorithm with local topology (also known as LPSO). We argue that the neighborhood size is an important input to consider when designing any adaptive approach based on the change of population diversity. We used the extensive benchmark suite of the IEEE CEC 2014 competition for experiments.

References

Cheng, S., Shi, Y., Qin, Q., Zhang, Q., and Bai, R. Population diversity maintenance in brain storm optimization algorithm. Journal of Artificial Intelligence and Soft Computing Research 4, 2 (2014), 83–97.

Cleghorn, C. W., and Engelbrecht, A. P. Particle swarm convergence: An empirical investigation. In 2014 IEEE Congress on Evolutionary Computation (CEC) (2014), IEEE, pp. 2524–2530.

Engelbrecht, A. P. Particle swarm optimization: Global best or local best? In 2013 BRICS congress on computational intelligence and 11th Brazilian congress on computational intelligence (2013), IEEE, pp. 124–135.

Kennedy, J., and Eberhart, R. Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks (1995), vol. 4, IEEE, pp. 1942–1948.

Liang, J. J., Qu, B. Y., and Suganthan, P. N. Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore 635 (2013), 490.

Lin, C. An adaptive genetic algorithm based on population diversity strategy. In 2009 Third International Conference on Genetic and Evolutionary Computing (2009), IEEE, pp. 93–96.

Matousek, R., Dobrovsky, L., and Kudela, J. How to start a heuristic? utilizing lower bounds for solving the quadratic assignment problem. International Journal of Industrial Engineering Computations 13, 2 (2022), 151–164.

McGinley, B., Maher, J., O’Riordan, C., and Morgan, F. Maintaining healthy population diversity using adaptive crossover, mutation, and selection. IEEE Transactions on Evolutionary Computation 15, 5 (2011), 692–714.

Morrison, R. W., and De Jong, K. A. Measurement of population diversity. In International conference on artificial evolution (evolution artificielle) (2001), Springer, pp. 31–41.

Polakova, R., Tvrdik, J., and Bujok, P. Differential evolution with adaptive mechanism of population size according to current population diversity. Swarm and Evolutionary Computation 50 (2019), 100519.

Pol´akov´a, R., Tvrd´ık, J., Bujok, P., and Matousek, R. Population-size adaptation through diversity-control mechanism for differential evolution. In MENDEL, 22th International Conference on Soft Computing (2016), pp. 49–56.

Sun, N., and Lu, Y. A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity. Neural Computing and Applications 31, 5 (2019), 1435–1443.

Tang, J., Lim, M. H., and Ong, Y. S. Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Computing 11, 9 (2007), 873–888.

Van den Bergh, F., and Engelbrecht, A. P. A study of particle swarm optimization particle trajectories. Information sciences 176, 8 (2006), 937–971.

Van den Bergh, F., and Engelbrecht, A. P. A convergence proof for the particle swarm optimiser. Fundamenta Informaticae 105, 4 (2010), 341–374.

Zavala, A. E. M. A comparison study of pso neighborhoods. In EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II. Springer, 2013, pp. 251– 265.

Published
2021-12-21
How to Cite
[1]
Pluháček, M., Kazikova, A., Kadavy, T., Viktorin, A. and Senkerik, R. 2021. Relation of Neighborhood Size and Diversity Loss Rate in Particle Swarm Optimization With Ring Topology. MENDEL. 27, 2 (Dec. 2021), 74-79. DOI:https://doi.org/10.13164/mendel.2021.2.074.
Section
Articles