Countering the negative search bias of ant colony optimization in subset selection problems
In their quest to find a good solution to a given optimization problem, metaheuristic search algorithms intend to explore the search space in a useful and efficient manner. Starting from an initial state or solution(s), they are supposed to evolve towards high-quality solutions. For some types of genetic algorithms (GAs), it has been shown that the population of chromosomes can converge to very bad solutions, even for trivial problems. These so-called deceptive effects have been studied intensively in the field of GAs and several solutions to these problems have been proposed. Recently, similar problems have been noticed for ant colony optimization (ACO) as well. As for GAs, ACO's search can get biased towards low-quality regions in the search space, probably resulting in bad solutions. Some methods have been proposed to investigate the presence and strength of this negative bias in ACO. We present a framework that is capable of eliminating the negative bias in subset selection problems. The basic Ant System algorithm is modified to make it more robust to the presence of negative bias. A profound simulation study indicates that the modified Ant System outperforms the original version in problems that are susceptible to bias. Additionally, the proposed methodology is incorporated in the MaxMin AS and applied to a real-life subset selection problem.
search bias ant colony optimization evolutionary computations combinatorial optimization subset problems