A multiobjective bacterial optimization method based on comprehensive learning strategy for environmental/economic power dispatch

Abstract

This article extends the bacterial foraging optimization (BFO) for addressing the multi-objective environmental/economic power dispatch (EED) problem. This new approach, abbreviated as MCLBFO, is proposed based on the comprehensive learning strategy to improve the search capability of BFO for the optimal solution. Besides, the fitness survival mechanism based on a health sorting technique is employed and embedded in reproduction mechanism to enhance the quality of the bacteria swarm. The diversity of the solutions is achieved by the combination of two typical techniques, i.e. non-dominance sorting and crowded distance. Experimental tests on the stan-dard IEEE 30-bus, 6-generator test system demonstrate that the novel algorithm, MCLBFO, is superior to other well developed methods such as MOEA/D, SMS-EMOA and FCPSO. The results of the comparison indicate that MCLBFO is outstanding in handling optimization problems with multiple conflicting objectives.