J. Gao, H. Li, and Z. Fang (PR China)
Immune Algorithm, Multiobjective Optimization, Similar Individuals, Evolutionary Algorithm
This study presents a novel immune algorithm based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. In this proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search algorithm is incorporated to facilitate the exploitation of the search space. Especially, a new truncation algorithm with similar individuals (TASI) is proposed to preserve the diversity of the population and eliminate similar individuals. Also, a new selection operator is presented to create the new population based on TASI. Simulation results on five standard problems (ZDT2, ZDT6, DEB, VNT, and OSY) show the proposed algorithm is able to ?nd much better spread of solutions and better convergence near the true Pareto-optimal front compared to the vector immune algorithm and the elitist non-dominated sorting genetic system.
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