Abstract
The metaheuristic optimization algorithm Harmony search (HS), which imitates the process of music improvisation, is becoming widely used due to its simplicity and easy operation. However, the basic HS has shortcomings of low optimization accuracy and risk of easy falling into local optimum. To overcome these problems, this article develops an ameliorated harmony search algorithm with hybrid convergence mechanism, namely AHS-HCM. For the new method, the so-called hybrid convergence mechanism mainly includes two important schemes. The first scheme is to introduce a convergence coefficient in the harmony improvisation to further adjust the optimization performance, which can improve the final accuracy. The second scheme is to put forward a non-linear convergence domain for the global exploration, it also helps the optimization accuracy and efficiency. Besides, the operation process of new harmony variables generation is modified to enrich the search behavior and contribute to find the global optimum. Fifteen typical CEC’s benchmark functions are selected for experiments, and the results clearly proved the effectiveness of the AHS-HCM. It is shown that, in most cases, the proposed AHS-HCM algorithm is superior to other HS variants as well as some similar famous population-based algorithms in terms of optimization accuracy and stability.
Highlights
In today’s production and engineering fields, many issues involve the need for optimization to get better quality, higher efficiency, maximum benefit, etc
Various evolutionary laws in nature provide inspiration for optimization matters and many metaheuristic intelligent algorithms are putting forward, which can be divided into four categories [3]: (1) local search-based algorithms, such as Genetic algorithm (GA) [4] and simulated annealing (SA) [5], etc. (2) evolutionary search-based algorithms, like ant colony optimization (ACO) [6], etc. (3) swarm search-based algorithm, for example artificial bee colony (ABC) [7], particle swarm optimization (PSO) [8] and differential evolution (DE) [9], etc. (4) hybrid algorithms, like dynamic membrane-driven bat algorithm (DMBA) [10], hybridized salp swarm algorithm (HSSA) [11], etc
For the basic harmony search (HS), the convergence accuracy is not too high compared with some other advanced metaheuristic algorithms, and there is the risk of premature convergence due to easy falling into local optimum [30], [31]
Summary
In today’s production and engineering fields, many issues involve the need for optimization to get better quality, higher efficiency, maximum benefit, etc. In global dynamic harmony search (GDHS) [37], all the key parameters are changed to dynamic mode, which do not need to predefine and are suitable for various problems Experimental results show it outperforms the other methods including different family algorithms. Local opposition-based learning selfadaption global harmony search (LHS) [42] utilized a new improvisation process that integrated adaptive global pitch adjustment as well as an opposition-based learning approach, and results proved its superiority on extensive benchmark functions. The concept of effective global harmony search (EGHS) [44] integrated the HS and PSO algorithm, has stronger space exploration capacity, and better results in solving two kinds of optimization problems for gas turbine. Based on the research of various HS variants and through in-depth analysis, an ameliorated harmony search algorithm with hybrid convergence mechanism, namely AHS-HCM, is proposed in this article.
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