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Boxing Match Algorithm: a new meta-heuristic algorithm
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AI
DOI：10.1007/s00500-022-07518-6.png)
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Abstract
En 中文
This study presents a boxing match algorithm (BMA) as a new efficient meta-heuristic method to solve numerical optimization and NP-hard problems, such as knapsack. The proposed algorithm finds the best position (i.e., best solution) in the ring during the match (i.e., feasible solutions) through a simulation of the boxer's behavior, who can attract good support from his coach and fans to defeat his rival. The proposed algorithm is seeking good solutions by dividing the solution space into different sections and generating new solutions in each section through a semi-zigzag search. This division causes a proper and targeted search in the solution space to reach an efficient solution. Also, the unique boxer's movement in the ring (generating new solutions) enables the proposed BMA to give an acceptable level of performance in exploring the problem space. Several unconstrained mathematical, knapsack, and engineering benchmark problems undergo a standard test designed to confirm the powerful performance of the proposed BMA. A wide variety of complex numerical problems are solved and analyzed by a comparison between the results obtained from the test and the other well-known optimization methods. Quantitative data also indicate the proper performance of the proposed algorithm compared to the other ones. The results show that this algorithm can achieve 95% and 100% optimal solutions in 20 mathematical benchmark functions and eight examples of the knapsack problem, respectively.
Keywords:
Meta-heuristics
Boxing match algorithm
Optimization