Abstract
Particle swarm optimization (PSO) is a popular population based approach used to solve nonlinear and complex optimization problems. It is simple to implement and swarm based probabilistic algorithm but, it also has drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to reduce the chance of stagnation, while improving the convergence speed, a new position updating phase is incorporated with PSO, namely fitness based position updating in PSO. The proposed phase is inspired from the onlooker bee phase of Artificial Bee Colony (ABC) algorithm. In the proposed position updating phase, solutions update their positions based on probability which is a function of fitness. This strategy provides more position updating chances to the better solutions in the solution search process. The proposed algorithm is named as fitness based particle swarm optimization (FPSO). To show the efficiency of FPSO, it is compared with standard PSO 2011 and ABC algorithm over 15 well known benchmark problems and three real world engineering optimization problems.
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Sharma, K., Chhamunya, V., Gupta, P.C. et al. Fitness based particle swarm optimization. Int J Syst Assur Eng Manag 6, 319–329 (2015). https://doi.org/10.1007/s13198-015-0372-4
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DOI: https://doi.org/10.1007/s13198-015-0372-4