The Sim-EA Algorithm with Operator Autoadaptation for the Multiobjective Firefighter Problem

The firefighter problem is a graph-based optimization problem that can be used for modelling the spread of fires, and also for studying the dynamics of epidemics. Recently, this problem gained interest from the softcomputing research community and papers were published on applications of ant colony optimization and evolutionary algorithms to this problem. Also, the multiobjective version of the problem was formulated. This paper proposes a multipopulation algorithm Sim-EA for the multiobjective version of the firefighter problem. The algorithm optimizes firefighter assignment for a predefined set of weight vectors which determine the importance of individual objectives. A migration mechanism based on similarity of search directions is used for improving the effectiveness of the algorithm. Obtained results confirm that the multipopulation approach works better than the decomposition approach in which a single specimen is assigned to each direction. Given less computational resources than the decomposition approach, the Sim-EA algorithm produces better results. The migration strategy based on ranking of subpopulations according to similarity of search directions seems to work best among the migration strategies tested in this paper.