A new archive based steady state genetic algorithm

In this paper we have proposed a new archive based steady state multi-objective genetic algorithm, which performs well, especially in higher dimensional space. An improved archive maintenance strategy has been introduced in this algorithm which is adaptive as well as dynamic in size. The archive maintenance strategy tries to maintain only the set of nondominated solutions in the archive. However, it maintains a minimum size of population when the nondominated solutions are not sufficient to fill the population. In this algorithm we have proposed a new environmental selection and a new mating selection. The mating selection reduces the exploration in less probable search region enhancing the exploitation of existing solutions. A new crossover operator DE-3 has also been proposed in this article. The proposed algorithm has been compared with three other existing multi-objective optimization algorithms NSGA-II, SPEA2 and AbYSS. Our algorithm outperforms the other three algorithms for its better diversity and convergence to true Pareto optimal front.