Hybrid gradient particle swarm optimization for dynamic optimization problems of chemical processes

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.

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