Abstract
AbstractIn this paper, a knowledge-based Artificial Fish-Swarm (AFA) optimization algorithm with crossover, CAFAC, is proposed to enhance the optimization efficiency and combat the blindness of the search of the AFA. In our CAFAC, the crossover operator is first explored. The knowledge in the Culture Algorithm (CA) is next utilized to guide the evolution of the AFA. Both the normative knowledge and situational knowledge is used to direct the step size as well as direction of the evolution in the AFA. Ten high-dimensional and multi-peak functions are employed to investigate this new algorithm. Numerical simulation results demonstrate that it can indeed outperform the original AFA.
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