An Improved Evolutionary Algorithm for Solving Multimodal Function Global Optimization Problem on a Bounded Area
Wei Li · Journal of Wuhan University · 2007
In this paper,we propose an improved evolutionary algorithm combining diversity maintaining mechanism and accelerating operators,which focuses on the contradiction between the maintenance of population diversity and search efficiency in solving multimodal function global optimization problem on a bounded area.The convergence analysis shows our algorithm can converge to global optimization under some circumstance.When dealing with low dimensional problems,our algorithm is prone to converge to global optimization and outperforms canonical genetic algorithm,while with high dimensional problems,the converging condition cannot be satisfied,our algorithm can converge to the so-called e-satisfied result and outperforms canonical particle swarm optimization algorithm.