Performance Quantification of Search Operators in Hybrid Harmony Search Algorithms
Meta-heuristic algorithms have been developed to solve various mathematical and engineering optimization problems. However, meta-heuristic algorithms show different performances depending on the characteristics of each problem. Therefore, there have been
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Abstract Meta-heuristic algorithms have been developed to solve various mathematical and engineering optimization problems. However, meta-heuristic algorithms show different performances depending on the characteristics of each problem. Therefore, there have been many kinds of research to decrease the performance gap for the different optimization problems by developing new algorithms, improving the search operators, and considering self-adaptive parameters setting on their algorithms. However, the previous studies only focused on improving the performance of each problem category (e.g., mathematical problem, engineering problem) without the quantitative evaluation for the operator performance. Therefore, this study proposes a framework for the quantitative evaluation to solve the no free lunch problem using the operators of the representative meta-heuristic algorithms (such as genetic algorithm and harmony search algorithm). Moreover, based on the quantitative analysis results for each operator, there are several types of hybrid optimization algorithms, which combined the operator of harmony search algorithm (HSA), genetic algorithm (GA), and particle swarm optimization (PSO). The optimization process to find the optimal solution is divided into five sections based on the number of function evaluations to see the performance of the search operator according to the section. Representative mathematical problems were applied to quantify the performance and operators. None of the five evaluated applied to mathematical benchmark problems were the best algorithms. Hybrid HSAs showed advanced performance for T. Kim Dept. of Civil, Environmental and Architectural Engineering, Korea University, Seoul, South Korea e-mail: [email protected] Y. H. Choi Department of Civil Engineering, Gyeongnam National University of Science and Technology, Jinju, South Korea J. H. Kim (B) School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, South Korea e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. M. Nigdeli et al. (eds.), Proceedings of 6th International Conference on Harmony Search, Soft Computing and Applications, Advances in Intelligent Systems and Computing 1275, https://doi.org/10.1007/978-981-15-8603-3_1
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problems where traditional HSA did not show good performance. However, it still has not escaped the No Free Lunch theorem. Keywords Operator · Meta-heuristic algorithm · Hybrid algorithm · Harmony search algorithm · Performance quantification
1 Introduction Optimization problem has long been a problem that many researchers have tried to solve. Meta-heuristic algorithms are developed and have emerged as an effective way to solve optimization problem. Meta-heuristic algorithms are applied to various fields such as mathematics, economics, and engineering to solve optimization problems. Research continues to increase the performance of algorithms in various ways to solve the optim
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