# A Reward Population-Based Differential Genetic Harmony Search Algorithm
###### Abstract  
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. h 202211 January 202211 January 202  
impossible or difficult to find the optimal solution of the problem, a heuristic algorithm is an efficient method to obtain the feasible solution [8]. Harmony search is a heuristic global search algorithm introduced in 2001 by [PERSON], [PERSON], and [PERSON] [9]. Inspired by the improvisation of musicians, it simulates musicians' improvisation process to achieve subtle harmonies. The HS algorithm is characterized by merits such as a simple structure, less demand for parameters, a high convergence rate, and strong robustness, which facilitates its application in optimizing a system's continuous and discrete variables [10]. We hope that the HS algorithm can effectively solve some optimization problems. In recent years, scholars have constantly improved the algorithm and made some progress [11; 12; 13; 14]. [PERSON] et al. used a new method for generating new solution vectors and set the pitch adjustment rate and distance bandwidth as dynamic changes, improving the accuracy and convergence rate of the HS algorithm [15]. [PERSON] et al. changed all key parameters of the HS algorithm into a dynamic mode without predefining any parameters. This algorithm is referred to as the global dynamic harmony search, and such modification imbued the algorithm with outstanding performance on unimodal functions and multimodal functions [16]. In view of the drawbacks of HS such as a low convergence rate and solving accuracy in solving complex problems, [PERSON] et al. proposed an adaptive global modified harmony search (MHS) that fuses local searches [17]. [PERSON] et al. proposed the concepts of the maximum harmony memory consideration rate, the minimum harmony memory consideration rate, the maximum regulation bandwidth, the minimum regulation bandwidth, etc. on the basis of the standard HS algorithm, and they achieved an automatic regulation mechanism for the relevant parameters of the harmony search algorithm by improving the current harmony memory consideration rate for each iteration and changing the bandwidth with the number of iterations [18]. To improve the efficiency of the HS algorithm and make up for its drawback of easily getting into local search, [PERSON] et al. improved the generation of the initial solution vector's harmony memory and proposed an improved harmony algorithm, ALHS [19]. Given the low evolution efficiency of single populations and fewer studies on multi-population improvement, [PERSON] combined dynamic multi-swarm particle swarm optimization (DMS-PSO) with the HS algorithm and simplified it into the dynamic multi-swarm particle swarm optimization harmony search (DMS-PSO-HS).