Investigation of horizontal crossover and stability- based adaptive inertia weight strategies for CLPSO

This paper investigates horizontal crossover (HC) and stability-based adaptive inertia weight (SAIW) strategies for comprehensive learning particle swarm optimization. HC applies arithmetic crossover on all the dimensions of two different personal best positions. SAIW adaptively adjusts the inertia weight and acceleration coefficient for each particle on each dimension. Experimental results on various benchmark functions demonstrate that HC can significantly improve the convergence performance for the optimizer, while SAIW cannot. The results also indicate that HC and SAIW need to be further improved.