A Ranking Policy Based on Many Objective Optimization
A Ranking Policy Based on Many Objective Optimization |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-7 |
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| Year of Publication : 2024 | ||
| Author : Pratyusha Rakshit, Archana Chowdhury |
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| DOI : 10.14445/22315381/IJETT-V72I7P118 | ||
How to Cite?
Pratyusha Rakshit, Archana Chowdhury, "A Ranking Policy Based on Many Objective Optimization," International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 168-177, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I7P118
Abstract
Many objective optimizations have more competing objectives than multi-objective optimizations (MOO); thus, they are more challenging to solve. In order to solve the many objective optimizations (MaOO), a novel strategy based on a ranking policy is put forth in this study. In many objective optimizations, a solution might not be effective for all goals; hence a new ranking scheme is suggested in place of pareto ranking. Artificial Bee Colony (ABC) is the algorithm that was employed in this study. The procedure is initially conducted in parallel with each of the multiple objective optimization problem's objectives. The following phase involves sifting through and choosing the high-quality solutions that are produced by simultaneously optimizing each of the multiple objectives. The proposed ranking system is used to grade the constituents of high-quality solutions. Performance was assessed using DTLZ and WFG tests, and the findings imply that the suggested approach performs better than cutting-edge techniques.
Keywords
Various objective optimization, Multi-objective optimization, Fitness function, Artificial Bee Colony, Pareto ranking.
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