An Improved Co-evolutionary Strategy for Nonlinear Constrained System Based on Particle Swarm Optimization
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概要
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The particle swarm optimization (PSO) algorithm has been used in various applications because of its powerful ability to tackle complex optimization problems. The optimization process of PSO has to satisfy constraints according to the requirements of actual nonlinear systems. An adaptive finite impulse response (FIR) filter design based on the PSO algorithm was proposed in our previous work. The adaptive FIR filter is a nonlinear constrained system. However, the performance of the adaptive filter is unsatisfactory. A high side lobe level and noise leakage is the main drawback of previous adaptive filters based on PSO. This paper proposes an improved co-evolutionary strategy based on a reinitialization method as a constraint technique for side lobe suppression. Two PSOs have been included in the co-evolutionary strategy to improve the constrained optimization. One is for self-adaption and the other is for side lobe suppression. The reinitialization method can overcome the decline of accuracy due to the difference between the two PSOs. The simulation results illustrate that the co-evolutionary strategy with reinitialization further enhances the accuracy in comparison with the original co-evolutionary strategy. With the improved co-evolutionary strategy, the adaptive FIR filter considerably strengthens side lobe suppression.
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