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An Enhanced Multi-Strategy Mantis Shrimp Optimization Algorithm and Engineering Implementations
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DOI：10.3390/sym17091453.png)
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Abstract
En 中文
This paper proposes a novel intelligent optimization algorithm, ICPMSHOA, that effectively balances population diversity and convergence performance by integrating an iterative chaotic map with infinite collapses (ICMIC), centroid opposition-based learning, and periodic mutation strategy. To verify its performance, we adopted benchmark functions from the IEEE CEC 2017 and 2022 standard test suites and compared it with six algorithms, including OOA and BWO. The results show that ICPMSHOA has significant improvements in convergence speed, global search capability, and stability, with statistically significant advantages. Furthermore, the algorithm performs outstandingly in three practical engineering constrained optimization problems: Haverly's pooling problem, hybrid pooling-preparation problem, and optimization design of industrial refrigeration systems. This study confirms that ICPMSHOA provides efficient and reliable solutions for complex optimization tasks and has strong practical value in engineering scenarios.
Keywords:
mantis shrimp optimization algorithm
iterative chaotic map with infinite collapses
centroid opposition-based learning
periodic mutation strategy
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