Abstract
The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.
Highlights
The Harmony Search (HS) algorithm is one of the Evolutionary Algorithms (EA), taking inspiration from the music improvisation process, which was proposed by Geem et al [1] in 2001
This paper presents an adaptive harmony search algorithm with differential evolution mutation, periodic learning and linear population size reduction strategy
We present a general framework for defining the pitch adjustment operator with the differential mutation (DE/best/2) [41], which can provide a more effective direction than the constant bandwidth to the searching landscape
Summary
Xinchao Zhao 1,2, * , Rui Li 1 , Junling Hao 3 , Zhaohua Liu 1 and Jianmei Yuan 2,4, *.
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