期刊
NEURAL COMPUTING & APPLICATIONS
卷 25, 期 6, 页码 1369-1381出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-014-1624-y
关键词
Complex-valued encoding; Bat algorithm; Diploid; Test functions
资金
- National Science Foundation of China [61165015]
- Guangxi Science Foundation [2012GXNSF DA053028]
- Guangxi High School Science Foundation - Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China [20121ZD008, 201203YB072, IPIU01201100]
- Guangxi Graduate Education [gxun-chx2012103]
Bat algorithm is a recent optimization algorithm with quick convergence, but its population diversity can be limited in some applications. This paper presents a new bat algorithm based on complex-valued encoding where the real part and the imaginary part will be updated separately. This approach can increase the diversity of the population and expands the dimensions for denoting. The simulation results of fourteen benchmark test functions show that the proposed algorithm is effective and feasible. Compared to the real-valued bat algorithm or particle swarm optimization, the proposed algorithm can get high precision and can almost reach the theoretical value.
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