An Adaptive Recombination-Based Extension of the iMOACOR Algorithm
Ashraf M. Abdelbar, Khalid M. Salama, Jesús Guillermo Falcón-Cardona, Carlos A. Coello Coello · 2018
Commonly, Ant Colony Optimization algorithms have been applied to the solution of single-and multi-objective optimization problems (MOPs). However, in recent years, a number of approaches have been proposed to solve problems with continuous search spaces. One remarkable proposal is the indicator-based Multi-Objective Ant Colony Optimizer for continuous search spaces (iMOACOR) which is based on the ACORalgorithm and the $R2$ performance indicator, aiming to solve continuous many-objective optimization problems (i.e., MOPs having more than three objective functions). In previous work, we presented an extension of iMOACOR, called iMOACOR-R, in which a recombination operator is employed for solution construction with a fixed, externally-specified probability. In the present work, we introduce a further adaptive variation, called iMOACOR-AR, in which the frequency of applying recombination is dynamically adapted based on the recent past performance of the recombination operator. Our proposal is compared to iMOACOR and iMOACOR-R using 64 standard problems from the multi-objective optimization literature with a number of objectives ranging from 3 to 10. Experimental results show that iMOACOR-AR outperforms iMOACOR and iMOACOR-R in most of the test problems.