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|Title:||Multi-objective particle swarm optimization control technology and its application in batch processes|
Particle swarm optimization
|Citation:||Jia, L., Cheng, D., Cao, L., Cai, Z., Chiu, M.-S. (2010). Multi-objective particle swarm optimization control technology and its application in batch processes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6328 LNCS (PART 1) : 36-44. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-15621-2_5|
|Abstract:||In this paper, considering the multi-objective problems in batch processes, an improved multi-objective particle swarm optimization based on pareto-optimal solutions is proposed. In this method, a novel diversity preservation strategy that combines the information on distance and angle into similarity judgment is employed to select global best and thus guarantees the convergence and the diversity characteristics of the pareto front. As a result, enough pareto solutions are distributed evenly in the pareto front. Lastly, the algorithm is applied to a classical batch process. The results show that the quality at the end of each batch can approximate the desire value sufficiently and the input trajectory converges; thus verify the efficiency and practicability of the algorithm. © 2010 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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