Interactive multi-objective optimization algorithms and their applications in power economic dispatch

Linear weighted methods are effective methods to solve multi-objective optimization problems, but it is difficult for these methods to determine appropriate objective weights. To solve the above problems, an interactive multi-objective optimization algorithm is proposed in this paper. In the algorithm, decision-makers rectify objectives' weights dynamically based on their satisfaction with the obtained non-inferior solutions by interface, and a set of non-inferior solutions which can well reflect decision-makers' preference are obtained. A particle swarm optimization is applied to optimize a weighted single objective with fixed weights, and an interactive genetic algorithm (IGA) is applied to optimize all weights in order to look for some weights which meet the user's preference in the algorithm. The algorithm proposed in this paper is compared with NSGA-II and linear weighted method on solving the power economic dispatch problem. The experimental results show that the former is more suitable for solving power economic dispatch problem.