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

Read more

Summary

A New Differential Mutation Based Adaptive

Xinchao Zhao 1,2, * , Rui Li 1 , Junling Hao 3 , Zhaohua Liu 1 and Jianmei Yuan 2,4, *.

Introduction
Adaptive Harmony Search with Differential Evolution
Differential Evolution
Linear Population Size Reduction
Differential Mutation in the Pitch Adjustment Operator
Self-Adaptive PAR and F
Experimental Comparison and Analysis
Parameters and Benchmark Functions
How HMS Changes
Effect ofAsDifferential
Effect ofofDifferential
Change
Theofpossible
Combined
Experimental Comparison with HS Variants and Well-Known EAs
Overall Statistical Comparison among HS Variants
Comparison with Other Well-Known EAs
Conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.