A Novel Quantum-inspired Multi-Objective Evolutionary Algorithm Based on Cloud Theory
In the previous papers, Quantum-inspired multi-objective evolutionary algorithm (QMEA) was proved to be better than conventional genetic algorithms for multi-objective optimization problem. To improve the quality of the non-dominated set as well as the diversity of population in multi-objective problems, in this paper, a Novel Cloud-based quantum-inspired multi-objective evolutionary Algorithm (CQMEA) is proposed. CQMEA is proposed by employing the concept and principles of Cloud theory. The algorithm utilizes the random orientation and stability of the cloud model, uses a self-adaptive mechanism with cloud model of Quantum gates updating strategy to implement global search efficient. By using the self-adaptive mechanism and the better solution which is determined by the membership function uncertainly, Compared with several well-known algorithms such as NSGA-II，QMEA. Experimental results show that (CQMEA) is more effective than QMEA and NSGA-II.
Multi-Objective Optimization Problem Quantum-Inspired Multi-Objective Evolutionary Algorithm Cloud Model Evolutionary Algorithm
Bo Xu WANG CHENG Jian-Ping Yu Yong Wang
Department of Computer Science and Technology, Guangdong University of Petrochemical Technology,Maom Wells Fargo Bank, USA College of Mathematics and Computer Science, Hunan Normal University,Changsha,Hunan,410081 College of Electrical and Information Engineering, Hunan University,Changsha,Hunan,410082
国内会议
合肥
英文
1-7
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）