Morteza Andalib Sahnehsaraee - Master of Science in Mechanical Engineering, National Iranian Gas Company (NIGC);
Mohammad Javad Mahmoodabadi - Ph.D. Student in Mechanical Engineering, University of Guilan;
Ahmad Bagheri - Associated Professor in Mechanical Engineering, University of Guilan

In this paper, first a multiple-crossover genetic algorithm is presented. Its operators such as reproduction, crossover and mutation are introducedcompletely. Some multi-objective benchmark problems are selected to challenge the ability of the proposed method. Optimization is based on the non-dominated sorting idea. Simulation results are presented. The results are compared with true Pareto-optimal solutions to evaluate the performance of the proposed method

genetic algorithm, multiple-crossover, multi-objective optimization, Pareto-optimal