FAIPSO: fuzzy adaptive informed particle swarm optimization
Conventional particle swarm optimization (PSO) is an appropriate optimization method, yet it suffers from some drawbacks. Trapping in local minimums or premature convergence of particles leads to unsatisfactory levels of optimization. In this paper, a new method for improving PSO is provided. In the proposed method (FAIPSO), the acceleration coefficients c 1 and c 2 are adaptively adjusted for each particle in each iteration. For the adaptive controlling of the acceleration coefficients, a fuzzy inference system is used. This fuzzy inference system comprises six inputs, two outputs, and ten rules. In order to reduce inertia weight (?), a parabolic model is used. In addition to this, a range of vision (Mu) is defined for each of the particles and every one of the particles searches within this range. This range of vision changes adaptively. In order to adaptively control the range of vision, a fuzzy inference system is employed. This system has two inputs, one output, and 14 rules. To test the proposed method, 16 benchmarks, each inheriting special characteristics, are used. The performance of the proposed method was compared with that of ten types of PSOs (each of which are among the reputable works of the PSO subject). According to the results, the proposed method shows a good performance and is more appropriate than other methods.
adaptation particle swarm optimization optimization fuzzy inference system swarm intelligence