A New Quantum Clone Evolutionary Algorithm for Multi-objective Optimization
Hongjian Qu, Dawei Zhao, Fangzhao Zhao · 2008
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 QCEA will update the whole swarm by using random strategy. The algorithm not only has the global searching capacity but also improves 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.