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

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

Muneendra Ojha, Krishna Pratap Singh, Pavan Chakraborty, Shekhar Verma
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 23
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781522566366|DOI: 10.4018/IJSIR.2019010102
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MLA

Ojha, Muneendra, et al. "An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms." IJSIR vol.10, no.1 2019: pp.15-37. http://doi.org/10.4018/IJSIR.2019010102

APA

Ojha, M., Singh, K. P., Chakraborty, P., & Verma, S. (2019). An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms. International Journal of Swarm Intelligence Research (IJSIR), 10(1), 15-37. http://doi.org/10.4018/IJSIR.2019010102

Chicago

Ojha, Muneendra, et al. "An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms," International Journal of Swarm Intelligence Research (IJSIR) 10, no.1: 15-37. http://doi.org/10.4018/IJSIR.2019010102

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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.

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