An Improved Immune Genetic Algorithm for Multiobjective Optimization

摘要

The study presents a novel weight-based multiobjective immune genetic algorithm(WBMOIGA), which is an improvement of its first version. In this proposed algorithm, there are distinct characteristics as follows. First, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search procedure is utilized to facilitate the exploitation of the search space. Second, a new mate selection scheme, called tournament selection algorithm with similar individuals (TSASI), and a new environmental selection scheme, named truncation algorithm with similar individuals (TASI), are presented. Third, we also suggest a new selection scheme to create the new population based on TASI. Simulation results on three standard problems (ZDT3, VNT, and BNH) show WBMOIGA can find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist non-dominated sorting genetic algorithm (NSGA-II).