Multi-Objective Optimization Using Multi Parent Crossover Operators

The crossover operator has always been regarded as the primary search operator in genetic algorithm (GA) because it exploits the available information from the population about the search space. Moreover, it is one of the components to consider for improving the behavior of the GA. To improve performance of GA multi parent crossover operators have been used. Multi parent crossover operators involve sampling of features of more than two parent solution into the offspring that accelerated speed of convergence to global optima. These operators are based on some probability distribution and are gene-level parent centric crossover operators. In this work, we have used MPX (multi-parent crossover with polynomial distribution) and MLX (multi-parent cross-over with lognormal distribution) operators for multi-objective optimization. The performance of these operators is investigated on commonly used multi-objective functions. GA used for experimentation is Non-dominated Sort Genetic Algorithm-II (NSGA-II). It is observed that these operators work well with NSGA-II and have given encouraging results. Keywords—Multi-objective optimization, Non-dominated Sort Genetic Algorithm-II (NSGA-II), Crossover operator, MPX (multiparent polynomial distribution crossover), MLX (multi-parent lognormal distribution crossover).