A Reward Population-Based Differential Genetic Harmony Search Algorithm

• Published in: Algorithms Vol. 15; no. 1; p. 23
• Main Authors: Zhang, Yang, Li, Jiacheng, Li, Lei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Accuracy; Bandwidths; differential evolution algorithm; Electronic computers. Computer science; Evolution; genetic algorithm; Genetic algorithms; harmony search algorithm; harmony search algorithm; reward population; differential evolution algorithm; mutation strategy; genetic algorithm; Heuristic; Industrial engineering. Management engineering; Mathematical functions; Mathematical programming; Musical instruments; Musical performances; Musicians & conductors; mutation strategy; Optimization algorithms; Population; Process planning; QA75.5-76.95; reward population; Search algorithms; T55.4-60.8; Variables
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a15010023

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.