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

Author

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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)

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.

Suggested Citation

  • Muneendra Ojha & Krishna Pratap Singh & Pavan Chakraborty & Shekhar Verma, 2019. "An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 10(1), pages 15-37, January.
    • Handle: RePEc:igg:jsir00:v:10:y:2019:i:1:p:15-37
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