Hybrid evolutionary computation for the development of pollution prevention and control strategies
College
Gokongwei College of Engineering
Department/Unit
Chemical Engineering
Document Type
Article
Source Title
Journal of Cleaner Production
Volume
15
First Page
902
Last Page
906
Publication Date
2007
Abstract
Particle swarm optimization (PSO) is an evolutionary algorithm based on the behavior of social animals. Its key advantage is its computa- tional efficiency compared to related techniques such as genetic algorithm (GA). Use of a modified PSO algorithm in selecting an optimal array of pollution prevention techniques for clay brick production is described. The model is formulated as a multi-constraint knapsack optimization problem. The optimization technique used in the study is a binary PSO augmented with a GA-based mutation operator.
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Digitial Object Identifier (DOI)
10.1016/j.jclepro.2006.01.011
Recommended Citation
Tan, R. R. (2007). Hybrid evolutionary computation for the development of pollution prevention and control strategies. Journal of Cleaner Production, 15, 902-906. https://doi.org/10.1016/j.jclepro.2006.01.011
Disciplines
Chemical Engineering
Keywords
Swarm intelligence; Pollution prevention
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