Summary
This chapter proposes the Discrete Multi-Phase Particle Swarm Optimization (DiMuPSO) algorithm, extending the PSO approach to problems coded with discrete binary representations. The main features of DiMuPSO are in utilizing multiple groups of particles with different goals that are allowed to change with time, alternately moving toward or away from the best solutions found recently. DiMuPSO also enforces steady improvement in solution quality, accepting only moves that improve fitness. Experimental simulations show that DiMuPSO outperforms a genetic algorithm and a previous discrete version of PSO on several benchmark problems.
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Al-kazemi, B., Mohan, C. (2005). Discrete Multi-Phase Particle Swarm Optimization. In: Wu, X., Jain, L., Graña, M., Duro, R.J., d’Anjou, A., Wang, P.P. (eds) Information Processing with Evolutionary Algorithms. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-117-2_20
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DOI: https://doi.org/10.1007/1-84628-117-2_20
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