An Improved Particle Swarm Optimization Algorithm for Dynamic Power Allocation Problem

Abstract

In this paper, we propose a fast multiobjective particle swarm optimization algorithm (called CBR-fMOPSO for short). In the algorithm, a case-based reasoning (CBR) technique is used to retrieve history optimization results and experts’ experience and add them into the population of multiobjective particle swarm optimization algorithm (MOPSO) in dynamic environment. The optimal solutions found by CBR-fMOPSO are used to mend the case library to improve the accuracy of solving based on CBR in next solving. The results from a suit of experiments in electric furnaces show that the proposed algorithm maintains good performances however the environment changes.