Abstract
Continuous real-valued optimization problems are existed in various engineering optimization scenarios. The artificial bee colony algorithm (ABC) which is inspired by the foraging behavior of bee colonies is an efficient optimization algorithm to address complex continuous real-valued optimization problems. In this paper, the maximum entropic epistasis (MEE) in the exploratory landscape analysis (ELA) is adopted in the ABC algorithm (MEEABC) to improve the performance of the ABC algorithm. The dimension interaction of the continuous functions computed by the MEE is introduced to guide the search process of the MEEABC algorithm during the employed bee phase and onlooker bee phase. The adaptive mutation methods and the strategy of dynamic population size reduction are implemented to increase the convergence speed and dynamically ameliorate the local exploitation capability. The solutions in the fitness landscape are automatically divided into different clusters to explore the local basin of the fitness landscape via the collaboration between MEE and adaptive mutation methods. The performance of MEEABC is tested on CEC 2017 benchmark test suite. From the experimental results, the MEEABC is superior to ABC variants and state-of-art algorithms regarded with the performance of effectiveness and robustness.
Published Version
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