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Hybrid gradient particle swarm optimization for dynamic optimization problems of chemical processes
Author(s) -
Chen Xu,
Du Wenli,
Qi Rongbin,
Qian Feng,
Tianfield Huaglory
Publication year - 2013
Publication title -
asia‐pacific journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.348
H-Index - 35
eISSN - 1932-2143
pISSN - 1932-2135
DOI - 10.1002/apj.1712
Subject(s) - particle swarm optimization , convergence (economics) , multi swarm optimization , mathematical optimization , computer science , nonlinear system , nonlinear programming , derivative free optimization , metaheuristic , optimization problem , algorithm , mathematics , physics , quantum mechanics , economics , economic growth
Dynamic optimization problems (DOP) in chemical processes are very challenging because of their highly nonlinear, multidimensional, multipeak and constrained nature. In this paper, we propose a novel algorithm named hybrid gradient particle swarm optimization (HGPSO) by hybridizing particle swarm optimization (PSO) with gradient‐based algorithms (GBA). HGSPO can improve the convergence rate and solution precision of pure PSO, and avoid getting trapped to local optimums with pure GBA search. We further incorporate HGPSO into control vector parameterization (CVP), a method converting DOP into nonlinear programming, to solve five complex DOPs. These DOPs include multimodal, multidimensional and constrained problems. The experiments demonstrate that HGPSO performs much better in terms of solution precision and computational cost when compared with other PSO variants. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd.