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A new amelioration Particle Swarm Optimization (SARPSO) based on simulated annealing (SA), asynchronously changed learning genes (ACLG) and roulette strategy was proposed because the classical Particle Swarm Optimization (PSO) algorithm was easily plunged into local minimums. SA had the ability of probability mutation in the search process, by which the search processes of PSO plunging into local...
Particle swarm optimization (PSO) often traps in the local optimal solutions. In this paper, an opposition-based disturbance procedure was introduced into a basic PSO, which was abbreviated as PSOOD. For this proposed algorithm, opposition-based disturbance was implemented according to the probability when the personal best position was updated for each particle. Such procedure not only avoids the...
PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation...
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