Abstract An Alopex based evolutionary algorithm is proposed. Its salient feature is randomly selecting two individuals and computing their objective values. According to the information of the two individuals, the probability of search direction is ascertained. By iterative computing, the global optimum is obtained. It has the advantages of both gradient methods and simulation anneal algorithm to some extent. The anneal temperature is self-adjusting over the proceeding of evolution. The proposed algorithm is used to optimize the benchmark functions and the kinetic parameters of 2-chlorophenol oxidation in supercritical water. The experimental results demonstrate that the proposed algorithm is superior to the original evolutionary algorithms, especially for the multi-apices function problems.
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2009, Vol. 22 
