Abstract
The paper gives an adaptive particle swarm optimization algorithm with new random inertia weight (RIW-PSO). The new random inertia weight (RIW) is presented by simulated annealing idea to improve the global search ability of PSO and the one to solve the high dimensional and complex nonlinear optimization problems. The PSO with linearly decreasing inertia weight (LDWPSO) and RIW-PSO are tested with six benchmark functions. The experiments show that the convergent speed and accuracy of RIW-PSO is significantly superior to the one of LDW-PSO.
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References
Eberhart, R.C., Shi, Y.H.: Particle Swarm Optimization: Developments,Applications and Resources. In: Proceedings of the IEEE Congress on Evolutionary Computation [C], pp. 81–86. IEEE Service Center, Piscataway, USA (2001)
Ray, T., Liew, K.M.: A Swarm with an Effective Information Sharing Mechanism for Unconstrained and Constrained Single Objective Optimization Problems. In: Proc. IEEE Int. Conf. on Evolutionary Computation, Seoul, pp. 75–80 (2001)
Shi, Y.H., Eberhart, R.: Parameter Selection in Particle Swarm Optimization. In: Proc. of the 7th Annual Conf on Evolutionary Programming, Washington, DC, pp. 591–600 (1998)
Elegbede, C.: Structural Reliability Assessment Based on Particle Swarm Optimization. Structural Safety, 171–186 (2005)
Shi, Y.H., Eberhart, R.: A Modified Particle Swarm Optimizer. In: Proc. IEEE Int. Conf. on Evolutionary Computation, Anchorage, pp. 69–73 (1998)
Shi, Y.H., Eberhart, R.: Empirical Study of Particle Swarm Optimization. In: International Conference on Evolutionary Computation, pp. 1945–1950. IEEE, Washington, USA (1999)
Shi, Y.H., Eberhart, R.: Fuzzy Adaptive Particle Swarm Optimization. In: The IEEE Congress on Evolutionary Computation, pp. 101–106. IEEE, San Francisco, USA (2001)
Shi, Y.H., Eberhart, R.: Tracking and Optimizing Dynamic Systems with Particle Swarms. In: The IEEE Congress on Evolutionary Computation, pp. 94–100. IEEE, San Francisco, USA (2001)
Lu, Z.S., Hou, Z.R.: Particle Swarm Optimization with Adaptive Mutation. Acta Electronica Sinica, 416–420 (2004)
Miranda, V., Fonseca, N.: EPSO-best-of-two-worlds Meta-heuristic Applied to Power System Problems. In: Proceedings of the IEEE Congress on Evolutionary Computation Honollulu, pp. 1080–1085. IEEE Press, Hawaii, USA (2002)
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Gao, Y., Duan, Y. (2007). An Adaptive Particle Swarm Optimization Algorithm with New Random Inertia Weight. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_39
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DOI: https://doi.org/10.1007/978-3-540-74282-1_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74281-4
Online ISBN: 978-3-540-74282-1
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