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A Novel Hybrid Differential Evolution-based Hiking Optimization Algorithm
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Abstract
En 中文
Hiking optimization algorithm (HOA) is an algorithm inspired by hiking. Aiming at the shortcomings of the original hiking optimization algorithm, such as easy to fall into local optimum and limited convergence accuracy of complex optimization problems, based on the performance enhancement of the hiking optimization algorithm, a hybrid differential evolution hiking optimization algorithm is proposed, which is called DEHOA. Firstly, by introducing the differential evolution crossover operator, the hiking optimization algorithm is deeply integrated with the crossover idea of differential evolution. The candidate solution is generated by the differential vector and the dimension crossover is performed, which significantly enhances the population diversity and search ability. Secondly, a dual strategy parallel search of population information interaction optimization is designed to balance exploration and development. At the same time, the update mechanism of the original algorithm and the new solution generated by crossover are maintained, and the optimal solution is retained by greedy strategy selection. Finally, according to the requirements of different iteration stages, an adaptive boundary processing function is introduced to ensure that all boundary-crossing values are forced to be constrained within the upper and lower bounds, so as to avoid the generation of invalid solutions. The three strategies work together to effectively improve the global exploration ability and local convergence performance of the algorithm. In addition, in order to verify the optimization performance of DEHOA algorithm, DEHOA is compared with four advanced meta-heuristic algorithms on the test function CEC2022. Experiments show that the new algorithm shows better optimization accuracy and efficiency on multiple sets of benchmark test functions, and provides a more competitive solution for complex optimization scenarios.
Keywords:
hiking optimization algorithm
differential evolution crossover operator
double guided greedy strategy
adaptive boundary function degrees-of-freedom
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E
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0.6
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149
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