An orthogonal-array-based particle swarm optimizer with nonlinear time-varying evolution

Particle swarm optimization (PSO) is a population-based heuristic optimization technique. It has been developed to be a prominent evolution algorithm due to its simplicity of implementation and ability to quickly converge to a reasonable solution. However, it has also been reported that the algorithm has a tendency to get stuck in a near-optimal solution in multi-dimensional spaces. To overcome the stagnation in searching a globally optimal solution, a PSO method with nonlinear time-varying evolution (PSO-NTVE) is proposed to approach the optimal solution closely. When determining the parameters in the proposed method, matrix experiments with an orthogonal array are utilized, in which a minimal number of experiments would have an effect that approximates the full factorial experiments. To demonstrate the performance of the proposed PSO-NTVE method, five well-known benchmarks are used for illustration. The results will show the feasibility and validity of the proposed method and its superiority over several previous PSO algorithms.

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