An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms

Muneendra Ojha, Krishna Pratap Singh, Pavan Chakraborty and Shekhar Verma
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Muneendra Ojha: DSPM International Institute of Information Technology - Naya Raipur, Atal Nagar, India
Krishna Pratap Singh: Indian Institute of Information Technology - Allahabad, Allahabad, India
Pavan Chakraborty: Indian Institute of Information Technology - Allahabad, Allahabad, India
Shekhar Verma: Indian Institute of Information Technology - Allahabad, Allahabad, India

International Journal of Swarm Intelligence Research (IJSIR), 2019, vol. 10, issue 1, 15-37

Abstract: Multi-objective optimization algorithms using evolutionary optimization methods have shown strength in solving various problems using several techniques for producing uniformly distributed set of solutions. In this article, a framework is presented to solve the multi-objective optimization problem which implements a novel normalized dominance operator (ND) with the Pareto dominance concept. The proposed method has a lesser computational cost as compared to crowding-distance-based algorithms and better convergence. A parallel external elitist archive is used which enhances spread of solutions across the Pareto front. The proposed algorithm is applied to a number of benchmark multi-objective test problems with up to 10 objectives and compared with widely accepted aggregation-based techniques. Experiments produce a consistently good performance when applied to different recombination operators. Results have further been compared with other established methods to prove effective convergence and scalability.

Date: 2019
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