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An efficient multi-objective artificial raindrop algorithm and its application to dynamic optimization problems in chemical processes
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DOI：10.1016/j.asoc.2017.05.003.png)
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Abstract
En 中文
Recently, a new meta-heuristic approach, known as artificial raindrop algorithm (ARA), was proposed. This approach is inspired by the natural rainfall phenomenon, and has been observed to be a powerful tool in solving single-objective optimization problems. In this paper, a multi-objective variant of ARA (MOARA) is developed, which primarily combines the search mechanism of ARA and the non-dominated sorting technique, in an attempt to demonstrate the potential of ARA in tackling multi-objective optimization problems (MOPs). To improve the exploratory ability, the center point sampling strategy (CPSS) together with simulated binary crossover (SBX) is integrated into MOARA. The primary role of SBX is to accelerate the filling of the Pareto front (PF) by recombining diverse solutions, whereas CPSS serves as the domain knowledge of the MOP for guiding other points toward the target PF. For performance evaluation and comparison purposes, the proposed approach has been applied to two sets of benchmark MOPs, and compared with eight state-of-the-art multi-objective evolutionary algorithms based on the non-dominated sorting. The experimental results have indicated its improved efficiency over the other compared approaches. Furthermore, the contributions of SBX and CPSS to the entire algorithm have been experimentally studied. Finally, the proposed technique is applied to dynamic optimization problems in chemical processes, and promising results show the method's potential in real-world applications. (C) 2017 Published by Elsevier B.V All rights reserved.
Keywords:
Multi-objective optimization
Artificial raindrop algorithm
Nondominated sorting
Center point sampling strategy
Simulated binary crossover
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