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Coevolutionary ant colony optimization algorithm for mixed-variable optimization problem
WEI Mingyan, CHEN Yu, ZHANG Liang
Journal of Computer Applications
2021, 41 (5):
1412-1418.
DOI: 10.11772/j.issn.1001-9081.2020081200
For Mixed-Variable Optimization Problem (MVOP) containing both continuous and categorical variables, a coevolution strategy was proposed to search the mixed-variable decision space, and a Coevolutionary Ant Colony Optimization Algorithm for MVOP (CACOA
MV) was developed. In CACOA
MV, the continuous and categorical sub-populations were generated by using the continuous and discrete Ant Colony Optimization (ACO) strategies respectively, the sub-vectors of continuous and categorical variables were evaluated with the help of cooperators, and the continuous and categorical sub-populations were respectively updated to realize the efficient coevolutionary search in the mixed-variable decision space. Furthermore, the ability of global exploration to the categorical variable solution space was improved by introducing a smoothing mechanism of pheromone, and a "best+random cooperators" restart strategy facing the coevolution framework was proposed to enhance the efficiency of coevolutionary search. By comparing with the Mixed-Variable Ant Colony Optimization (ACO
MV) algorithm and the Success History-based Adaptive Differential Evolution algorithm with linear population size reduction and Ant Colony Optimization (L-SHADE
ACO), it is demonstrated that CACOA
MV is able to perform better local exploitation, so as to improve approximation quality of the final results in the target space; the comparison with the set-based Differential Evolution algorithm with Mixed-Variables (DE
MV) shows that CACOA
MV is able to better approximate the global optimal solutions in the decision space and has better global exploration ability. In conclusion, CACOA
MV with the coevolutionary strategy can keep a balance between global exploration and local exploitation, which results in better optimization ability.
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