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Elite-guided multi-objective artificial bee colony algorithm
Affiliation:1. Department of Computing Technology at University of Alicante, Alicante, Spain;2. Department of Computational Sciences and Artificial Intelligence at University of Alicante, Alicante, Spain;1. School of Mathematical Sciences, Fudan University, 200433, China;2. School of Statistics, Dongbei University of Finance and Economics, 116025, China;3. School of Mathematics and Statistics, Lanzhou University, 730000, China;1. Department of Dermatology and Venereolgy, Maharishi Markandeshwar Institute of Medical Sciences and Research, Mullana, Ambala, India;2. Department of Dermatology and Venereology, All India Institute of Medical Sciences and Research, New Delhi, India
Abstract:Multi-objective optimization has been a difficult problem and a research focus in the field of science and engineering. This paper presents a novel multi-objective optimization algorithm called elite-guided multi-objective artificial bee colony (EMOABC) algorithm. In our proposal, the fast non-dominated sorting and population selection strategy are applied to measure the quality of the solution and select the better ones. The elite-guided solution generation strategy is designed to exploit the neighborhood of the existing solutions based on the guidance of the elite. Furthermore, a novel fitness calculation method is presented to calculate the selecting probability for onlookers. The proposed algorithm is validated on benchmark functions in terms of four indicators: GD, ER, SPR, and TI. The experimental results show that the proposed approach can find solutions with competitive convergence and diversity within a shorter period of time, compared with the traditional multi-objective algorithms. Consequently, it can be considered as a viable alternative to solve the multi-objective optimization problems.
Keywords:Multi-objective optimization  Evolutionary algorithm  Artificial bee colony  Multi-objective artificial bee colony
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