A Hybrid Optimization Algorithm Based on Ant Colony and Immune Principles

This paper proposes a hybrid optimization method based on the ant colony and clonal selection principles , in which the cloning and mutation operations are embedded in the ant colony to enhance its search capability . The novel algorithm is employed to deal with a few benchmark optimization problems under both static and dynamic environments. Simulation results demonstrate the remarkable advantage s of our approach in diverse optimal solutions, closely tracking varying optimum, as well as improved convergence speed.

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