Solving Expensive Multiobjective Optimization Problems: A Fast Pareto Genetic Algorithm Approach

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FPGA). FPGA uses a new ranking strategy for the simultaneous optimization of multiple objectives where each solution evaluation is computationally expensive. New genetic operators are employed to enhance the algorithm’s performance in terms of convergence behavior and computational effort. Computational results for a number of benchmark test problems indicate that FPGA is a promising approach and it outperforms the improved nondominated sorting genetic algorithm (NSGA-II), which can be considered a widely-accepted benchmark in the MOEA research community, within a relatively small number of solution evaluations.