Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
Particle swarm optimization with triggered mutation and its implementation based on GPU
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
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One of significant improvement for particle swarm optimization (PSO) is through the implementation of metaheuristics hybridization that combines different metaheuristics paradigms. By using metaheuristics hybridization, the weaknesses of one algorithm can be compensated by the strengths of other algorithms. Therefore, researchers have given a lot of interest in hybridizing PSO with mutation concept from genetic algorithm (GA). The reason for incorporating mutation into PSO is to resolve premature convergence problem due to some kind of stagnation by PSO particles. Although PSO is capable to produce fast results, particles stagnation has led the algorithm to suffer from low-optimization precision. Thus, this paper introduces time-varying mutation techniques for resolving the PSO problem. The different time-varying techniques have been tested on some benchmark functions. Results from the empirical experiments have shown that most of the time-varying mutation techniques have significantly improved PSO performances not just to the results accuracy but also to the convergence time.