Multi-method based algorithm for multi-objective problems under uncertainty

Abstract Many real-world decision problems consider more than one objective. These objectives usually conflict with each other. In the problem environments, many data and parameters are usually uncertain at the time of planning. So multi-objective optimization combined with uncertainty is a challenging research topic. To deal with the basic search process in such problems, in this paper, a new approach has been proposed that combines multiple population based algorithms under a single algorithm structure. To deal with the uncertainty, a dynamic scenario-based approach is developed and integrated with the search process, in which the number of scenarios is dynamically set during the solution process. This is a new way of solving multi-objective optimization problems under uncertainty. To judge the performance of the proposed approach, we have solved a set of standard test problems without uncertainty and a number of practical problems with uncertainty. The practical problems are the well-known dynamic economic and emission dispatch problems, with different combinations of energy sources. The experimental studies demonstrated that the proposed approach performs better than other well-known algorithms compared in this paper.