Sub-Population Artificial Immune System for Multi-objective Flowshop Scheduling Problems

This research presents a Sub-Population Artificial Immune System (SPAIS) approach to solve the Multi-Objective Flowshop Scheduling Problems (MOFSP). We divide the populations in several sub-groups for different weighted objective functions. Than new AIS developed in this paper is incorporated after. In AIS field, most researchers use the clonal selection of B cells during the evolving processes to solve various optimization problems. Instead, we try to develop the T helper cell and T suppressor cell in T cell combining B cell to solve the MOFSP. Where T helper cell is used to help improving the solution and then T suppressor cell is generated to increase the diversity of the population. The total difference of completion time of each job is applied as the affinity function instead of the difference of makespan of the schedule. By integrate the subpopulation concept and the proposed AIS, both the diversity and the convergence can be concerned. This SPAIS method can supplement the flaw of SPGA using fitness as the basis and a new Lifespan which will keep good diversified chromosomes within the population to extend the searching spaces. The experimental tests show that this novel SPAIS method is very effective when comparing with SPGA [16], NSGA-II [10], and SPEA-II [3].