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Improved prairie dog optimization algorithm by dwarf mongoose optimization algorithm for optimization problems
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DOI：10.1007/s11042-023-16890-w.png)
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Abstract
En 中文
Recently, optimization problems have been revised in many domains, and they need powerful search methods to address them. In this paper, a novel hybrid optimization algorithm is proposed to solve various benchmark functions, which is called IPDOA. The proposed method is based on enhancing the search process of the Prairie Dog Optimization Algorithm (PDOA) by using the primary updating mechanism of the Dwarf Mongoose Optimization Algorithm (DMOA). The main aim of the proposed IPDOA is to avoid the main weaknesses of the original methods; these weaknesses are poor convergence ability, the imbalance between the search process, and premature convergence. Experiments are conducted on 23 standard benchmark functions, and the results are compared with similar methods from the literature. The results are recorded in terms of the best, worst, and average fitness function, showing that the proposed method is more vital to deal with various problems than other methods.
Keywords:
Prairie dog optimization algorithm
Dwarf mongoose optimization algorithm
Meta-heuristics
Benchmark functions
Optimization problems
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3
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3.2W
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