A New Multi-objective Optimization Method Based on QCEA
Most of the quantum inspired evolution algorithms (QEA) is improved and used for the optimization of continuous functions with multi-peak now, However, they are easy to be trapped into the local deceptive peak. In this paper, a new improved quantum evolution algorithm is proposed to overcome the shortcoming of traditional QEA. The new improved QEA combines the main mechanisms of clone (QCEA). Every individual of each chromosome will make its own dynamic clone to build its new sub-swarm; then every new chromosome will be mutation in its low bit; at last, the QCEAwill update the whole swarm by using random strategy. The algorithm not only has the global searching capacity, but alsoimproves the local searching capacity of algorithm by using quantum probabilistic search, Experiments are implemented and compared with other QEAs. The result indicates that the new algorithm in this paper can search and get the global optimum solution in a shorter time.
ARFIMA Quantum Clone Evolution Algorithm multi-objective optimization mutation
Bing Wang Fangzhao Zhou
School of Management, Donghua University, Shanghai, 200051, Shanghai
国际会议
第八届武汉电子商务国际会议(The Eighth Wuhan International Conference on E-Business)
武汉
英文
2048-2053
2009-05-30（万方平台首次上网日期，不代表论文的发表时间）