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Grid-based multi-objective cheetah optimization for engineering applications
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DOI：10.1007/s10586-024-04907-4.png)
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This paper introduces a novel Grid-based Multi-Objective Cheetah Optimizer (MCO) algorithm for engineering applications. The MCO algorithm is derived from the hunting strategy of cheetahs and builds upon its single-objective predecessor, the Cheetah Optimizer (CO). The MCO employs a combination of non-dominated sorting, a grid mechanism, and an archive to maintain and distribute solutions effectively. Firstly, non-dominance selection is applied to identify the best set of solutions. Secondly, an archive is maintained to preserve these optimal solutions. Thirdly, a grid-based method ensures a better distribution and selection of the solution set. The viability of the proposed MCO algorithm is verified through simulation studies on twenty two benchmark test functions and five engineering problems, evaluated against five performance metrics. Comparative analysis with five well-established multi-objective algorithms demonstrates that the MCO algorithm surpasses these in terms of achieving closer approximations to the Pareto front. The results confirm that the MCO algorithm can provide a diverse and effective set of optimal solutions, making it a superior choice for complex engineering problems requiring multi-objective optimization.
Keyword:
Multi-objective cheetah optimizer
Multiobjective algorithm
Multi-objective optimization
Meta-heuristics
Optimization
期刊
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4.1
论文数:
5.0K
被引数:
7.5K
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