Handling Time-Varying TSP Instances

Multimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in population-based algorithms devoted to multimodal optimization: simultaneous maintenance of multiple local optima in the population; and self-regulation of the population size along the search. The optimization surface may be subject to variations motivated by one of two main reasons: modification of the objectives to be fulfilled and change in parameters of the problem. An immune-inspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time. The proposal presents favorable results when compared to the results produced by a high-performance ant colony optimization algorithm of the literature.