An improved SPEA2 Multi objective algorithm with non dominated elitism and Generational Crossover

Multi-objective Optimization Problem (MOP) is an essential and challenging area for scientific research of real life problem. Multi-objective Optimization Problem (MOP) can be effectively solved by Multi-objective Evolutionary Algorithm (MOEA). In this paper, enhancements to a renowned Multi-objective Evolutionary algorithm SPEA2 are proposed. The proposed enhancements are useful to improve convergence and diversity simultaneously. In present study, for better convergence, Generational Crossover and Non-dominated solutions (based on SPEA2 fitness) based elitism are used. K-nearest neighbor density estimation technique is used to maintain diversity among solutions. The proposed algorithm is tested on widely used test problems of ZDT family. Simulation results suggest that proposed algorithm outperforms SPEA2 and gives better or comparative performance with other preeminent Multi-objective algorithms. The proposed Generational Crossover concept is generic and can be used with other MOEAs as well.