WBMOIGA: Weight-based multiobjective immune genetic algorithm and its application

In this paper, we present a weight-based multi-objective immune genetic algorithm (WBMOIGA). Compared to other weight-based multiobjective genetic algorithms, the proposed algorithm shows the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function, and (2) a local search procedure is utilized to improve the quality of the population, and an immune operator is adopted to increase the diversity of the population, and (3) specifically, a new mate selection operator, called tournament selection algorithm with similar individuals (TSASI), and a new environmental selection operator, named truncation algorithm with similar individuals (TASI), are presented. Simulation results on six standard test problems (ZDT1,ZDT2,ZDT3,ZDT6,SCH2,and FON) 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) and the random weight genetic algorithm (RWGA). Moreover, when applied to parallel machine scheduling, WBMOIGA also demonstrates better performance than NSGA-II and RWGA.