An immune inspired framework for optimization in dynamic environment

Most real-world optimization problems are dynamic in nature. To deal with the optimization problems in a dynamic environment, a general framework inspired from immune system is proposed. Three types of subpopulations mimicking the states of B-cells are introduced. Among them, innate population globally explores the promising area, while adaptive population locally searches the optima, and the memory population reuses the history information. Moreover, activation rule is brought to transfer the global search to local search and suppression works for eliminating the redundancy. Experimental simulation shows that the proposed algorithm is competitive comparing with the state-of-the-art designs in the tested dynamic environments modeled by the most commonly used test function-moving peaks benchmark.

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