A discrete particle swarm optimization algorithm with fully communicated information
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Bare bones particle swarm optimization with Gaussian or cauchy jumps
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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This paper deals with the problem of unconstrained optimization An improved probability particle swarm optimization algorithm is proposed Firstly, two normal distributions are used to describe the distributions of particle positions, respectively One is the normal distribution with the global best position as mean value and the difference between the current fitness and the global best fitness as standard deviation while another is the distribution with the previous best position as mean value and the difference between the current fitness and the previous best fitness as standard deviation Secondly, a disturbance on the mean values is introduced into the proposed algorithm Thirdly, the final position of particles is determined by employing a linear weighted method to cope with the sampled information from the two normal distributions Finally, benchmark functions are used to illustrate the effectiveness of the proposed algorithm.