Semi-self-adaptive harmony search algorithm

Abstract

This paper proposes a semi-self-adaptive harmony search algorithm (SSaHS) with the self-adaptive adjustment of the bandwidth and the elitist learning strategy of particle swarm optimization. SSaHS employs a self-adaptive adjusting strategy for with the difference between the maximum and minimum components in the harmony memory as the bandwidth. It can dynamically adjust the bandwidth for the specific problem to strengthen local exploitation ability and improve the accuracy of optimization results. Comparison results show that the semi-self-adaptive harmony search algorithm can find better solutions when comparing with both basic harmony search algorithm and several enhanced harmony search algorithms, including an improved harmony search, a global-best harmony search and a novel global harmony search.