Niching for population-based ant colony optimization

Most Ant Colony Optimization (ACO) algorithms are able to find a single (or few) optimal, or near-optimal, solutions to difficult (NP-hard) problems. An issue though is that a small change to the problem can have a large impact on a specific solution by decreasing its quality, or worse still, by rendering it infeasible. Niching methods, such as fitness sharing and crowding, have been implemented with success in the field of Evolutionary Computation (EC) and are aimed at simultaneously locating and maintaining multiple optima to increase search robustness - typically in multi-modal function optimization. In this paper it is shown that a niching technique applied to an ACO algorithm permits the simultaneous location and maintenance of multiple areas of interest in the search space.