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An artificial bee colony algorithm for multi-objective optimisation
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DOI：10.1016/j.asoc.2016.11.014.png)
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Abstract
En 中文
In addition to dominance-based and decomposition-based algorithms, performance indicator-based algorithms have been widely used and investigated in the field of evolutionary multi-objective optimisation. This study proposes a multi-objective artificial bee colony optimisation method called epsilon-MOABC?-MOABC based on performance indicators to solve multi-objective and many-objective problems. The proposed algorithm develops an external archive on the basis of both Pareto dominance and preference indicators to save the non-dominated solutions produced in each generation. The population of the presented algorithm includes employed bees, onlooker bees, and scout bees. Employed bees adjust their trajectories according to the information provided by other employed bees. Motivated by employed bees, onlooker bees select food sources to update their positions according to a power law probability, with which the food sources with high quality have a high probability to be selected for exploration. The quality of food sources is calculated on the basis of the quality indicator I epsilon+. Scout bees dispose of food sources with poor quality. The proposed algorithm proves to be competitive in dealing with multi-objective and many-objective optimisation problems in comparison with other state-of-the-art algorithms for CEC09, LZ09, and DTLZ test instances. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Evolutionary computing
Intelligent computing
Swarm intelligence
Multi-objective optimisation
Diversity
Artificial bee colony algorithm
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