Multi-Objective Fixed-Charged Transportation Optimization Based on Lam-GA

A new genetic algorithm based on the theory of lamarckian evolution (Lam-GA) to solve multi-objective fixed-charged transportation optimization problem (mfcTP) is presented in the paper. The algorithm carries out some local mutation according to certain rules after distributing transportation counts on the fuzzy rule basis, which can increase the intensity for searching better solution. Experimental data show that after strengthening the mutation locally, the new algorithm can get better Pareto front and Pareto optimal solutions in solving mfcTP in the real-world problems even if there is nonlinear and discontinuous, so that Lam-GA is more effective than Fuzzy-GA, st-GA, and m-GA. It also demonstrates that lamarckian evolutionary theory is significantly important for guiding in solving practical problems.