Abstract
This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model, each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards the sense it got from both its own history and other particle. The sense factor is calculated by famous Weber-Fechner Law. All these psychology factors will influence the next action of the particle. The resulting algorithm, known as Emotional PSO (EPSO), is shown to perform significantly better than the original PSO algorithm on different benchmark optimization problems. Avoiding premature convergence allows EPSO to continue search for global optima in difficult multimodal optimization problems, reaching better solutions than PSO with a much more fast convergence speed.
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References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. on Neural Networks, pp. 1942–1948 (1995)
Eberharyt, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proc. 6th international Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Service Center, Piscataway (1995)
Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Difference. In: 1998 Annual Conference on Evolutionary Programming, San Diego, pp. 601–610 (1998)
Shi, Y., Eberhart, R.C.: Parmeter Selection in Particle Swarm Optimization. In: Proc. 7th Annual Conf. on Evolutionary Programming, San Diego, pp. 591–600 (1998)
Shi, Y., Eberhart, R.C.: Empirical study of Particle Swarm Optimization. In: Proc. Congress on Evolutionary Computation, pp. 1945–1950 (1999)
Cristian, T.I.: The Particle Swarm Optimization Algorithm: Convergence Analysis and Parameter Selection. Information Processing Letters 85, 317–325 (2003)
Carlisle, A., Dozier, G.: Adapting Particle Swarm Optimization to Dynamic Environments. In: Proc. International Conf. on Artificial Intelligence, Las Vegas, Nevada, pp. 429–434 (2000)
Morten, L., Krink, T.: Extending Particle Swarm Optimizers with Self-Organized Criticality. In: Proc. 4th Congress on Evolutionary Computation (CEC) (2002)
Riget, J., VestertrØm, J.S.: A Diversity-Guided Partilce Swarm Optimizer – the arPSO. EVALife Technical Report. Denmark, vol. 2 (2002)
Morten, L., Rasmussen, T.K.: Hybrid Particle Swarm Optimiser with Breeding and Subpopulations. EVALife Project Group, Dept. of Computer Science (2001)
Kennedy, J., Mendes, R.: Population Structure and Particle Swarm Performance. In: Proc. Conf. Evolutionary Computation, vol. 2, pp. 1671–1676 (2002)
Fogel, D., Beyer, H.: A note on the empirical Evaluation of intermediate recombination. Evolutionary Computation 3(4), 491–495 (1995)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 303–308 (1997)
Eberhart, R., Shi, Y.: Particle Swarm Optimization: Developments, Applications and resources. In: Proc. IEEE Int. Conf. on Evolutionary Computation, pp. 81–86 (2001)
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Ge, Y., Rubo, Z. (2005). An Emotional Particle Swarm Optimization Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_67
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DOI: https://doi.org/10.1007/11539902_67
Publisher Name: Springer, Berlin, Heidelberg
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