Hybrid Cooperative Crayfish and Mountain Gazelle Algorithm for Global Optimization
B. H. Lee, Y. J. Lee, Kok–Chin Khor · 2024
The crayfish optimization algorithm (COA) and mountain gazelle optimization algorithm (MGO) have emerged as two powerful metaheuristics for global optimization. It has been shown that these two metaheuristics outperform conventional metaheuristics and are capable of finding near-optimal solutions for most of the benchmark functions. However, in certain benchmark functions, COA and MGO do not perform better than the conventional metaheuristics. To this end, a new Hybrid Cooperative COA-MGO algorithm (HCCMGA) is proposed. In the proposed HCCMGA algorithm, the solution vector is split into two solution subvectors of smaller dimensions, one optimized by COA and the other by MGO. The fitness of each search agent in COA and MGA is evaluated in a cooperative manner, whereby the solution subvector in COA is combined with the best solution subvector from MGO and vice versa. The proposed HCCMGA is evaluated using 13 benchmark functions and is compared with several state-of-the-art metaheuristics. Results show that the proposed HCCMGA outperforms state-of-the-arts optimization algorithms in solution quality, search stability and convergence speed, as the HCCMGA achieves solutions closest to the optimum in eight out of the 13 benchmark functions with low standard deviations and less numbers of iterations.