Improving the Performance of the Pareto Fitness Genetic Algorithm for Multi-Objective Discrete Optimization

To efficiently solve multi-objective discrete optimization problems, combining evolutionary computation with local search, an improved Pareto fitness genetic algorithm (IPFGA) was proposed. In the IPFGA, some features have been added to the original PFGA. The IPFGA after genetic optimization applies a local search on every solution, and adopts an external set truncation strategy to improve search efficiency of evolutionary algorithms. Additionally, the fitness assignment was modified to get more extensive Pareto optimal solutions. The experimental results show that the IPFGA, compared with the PFGA, can improve search efficiency of optimization and find more approximate Pareto optimal solutions.