A Multi-objective Approach based on Competitive Optimization Algorithm and its Engineering Applications | |
| Journal of AI and Data Mining | |
| دوره 9، شماره 4، بهمن 2021، صفحه 497-514 اصل مقاله (2.83 M) | |
| نوع مقاله: Original/Review Paper | |
| شناسه دیجیتال (DOI): 10.22044/jadm.2021.10698.2204 | |
| نویسندگان | |
| Y. Sharafi1؛ M. Teshnelab* 2؛ M. Ahmadieh Khanesar3 | |
| 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran | |
| 2Intelligent Systems Laboratory, Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran. | |
| 3Faculty of Engineering, University of Nottingham, Nottingham, UK. | |
| چکیده | |
| A new multi-objective evolutionary optimization algorithm is presented based on the competitive optimization algorithm (COOA) to solve multi-objective optimization problems (MOPs). Based on nature-inspired competition, the competitive optimization algorithm acts between animals such as birds, cats, bees, ants, etc. The present study entails main contributions as follows: First, a novel method is presented to prune the external archive and at the same time keep the diversity of the Pareto front (PF). Second, a hybrid approach of powerful mechanisms such as opposition-based learning and chaotic maps is used to maintain the diversity in the search space of the initial population. Third, a novel method is provided to transform a multi-objective optimization problem into a single-objective optimization problem. A comparison of the result of the simulation for the proposed algorithm was made with some well-known optimization algorithms. The comparisons show that the proposed approach can be a better candidate to solve MOPs. | |
| کلیدواژهها | |
| Multi-objective optimization؛ Competitive optimization algorithm؛ Initial population؛ Engineering design problems؛ Proposed crowding distance | |
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