Multi-objective optimization based on clonal selection algorithm
Nannan Liu, Xuhua Shi · 2012
This paper proposes a multi-objective optimization based on clonal selection algorithm. The use of cloning and mutation operators can promote the local and global searching performance. Adopting the non-uniform mutation operator can improve the searching for optimal solutions in the local region and assure the diversity of the solutions. At the same time, multiple cloning which is based on crowding distance is proposed to promote the diversity of the population. External archive is set to store the non-dominated solutions and crowding distance and delete operator are combined to complete the update of the external archive. Final results show that the proposed approach's performances are better than those proposed by NSGA-II. It gets a closer Pareto curve which is a set of the uniform and widespread solution.