Monkey Algorithm for Global Numerical Optimization
نویسندگان
چکیده
In this paper, monkey algorithm (MA) is designed to solve global numerical optimization problems with continuous variables. The algorithm mainly consists of climb process, watch-jump process, and somersault process in which the climb process is employed to search the local optimal solution, the watch-jump process to look for other points whose objective values exceed those of the current solutions so as to accelerate the monkeys’ search courses, and the somersault process to make the monkeys transfer to new search domains rapidly. The proposed algorithm is applied to effectively solve the benchmark problems of global optimization with 30, 1000 or even 10000 dimensions. The computational results show that the MA can find optimal or near-optimal solutions to the problems with a large dimensions and very large numbers of local optima. c ©2008 World Academic Press, UK. All rights reserved.
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