Multi-objective transportation optimization based on fmcica

A new fuzzy multi-population cooperative immune clone algorithm, called fmcica for the multi-objective optimization problems is proposed in this paper. We firstly make both of fuzzy rules and greedy algorithm infuse into the antibody decoding, sequentially enhancing the intelligent learning ability of antibody; And with the introducing of concepts of the vector affinity it can solve the difficult to search for a non-convex solution; Moreover with the introduction of master-slave multi-antibody population coordination and the cloning mechanism it can enhance the optimization ability of the algorithm. From the examples we can see that fmcica has not only obtained a better Pareto front, but also demonstrated better practicability and stronger intelligence from both the convergence and distribution compared to existing algorithms fuzzy-GA, st-GA and m-GA. In addition, fmcica has the mode of master-slave multi-antibody population collaboration and the mechanism of protecting the non-dominate solution, which can prevent the phenomena of prematurity and degradation.