Cloud Model-Based Differential Evolution Algorithm for Optimization Problems
Changming Zhu, Jun Ni · 2012
Differential Evolution (DE) is one of the current best evolutionary algorithms. It becomes important in many fields such as evolutionary computing and intelligent optimization. At present, DE has successfully been applied to diverse domains of science and engineering, such as signal processing, neural network optimization, pattern recognition, machine intelligence, chemical engineering and medical science. However, almost all the DE-related evolutionary algorithms still suffer from the problems such as premature convergence, slow convergence rate and difficult parameter setting. To overcome these drawbacks, we propose a novel Cloud Model-Based Differential Evolution Algorithm (CMDE) in which the pheromone and the sensitivity model of free search algorithm replaces the traditional roulette wheel selection model. The model incorporates Opposition-Based Leaning (OBL) to present an improved artificial bee colony algorithm. Experimental results verify the superiority of CMDE is over several state-of-the-art evolutionary optimizers.