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Process Synthesis and Design Problems Based on a Global Particle Swarm Optimization Algorithm
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DOI：10.1109/ACCESS.2021.3049175.png)
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Abstract
En 中文
Many process synthesis and design problems in engineering are actually mixed integer nonlinear programming problems (MINLP), because they contain both continuous and integer variables. These problems are generally recognized to be complex and intractable by virtue of the combinatorial characteristic. In order to effectively solve process synthesis and design problems, a global particle swarm optimization (GPSO) algorithm is proposed in this paper. GPSO algorithm makes two improvements on original particle swarm optimization (PSO) algorithm: first, it introduces a global inertia weight, which is beneficial for improving its global searching capacity during the whole optimization process; second, it adopts a mutation operation with a small probability, which enables the GPSO algorithm to get rid of the local optimum easily. Simulation results show that the GPSO algorithm has high efficiency on finding the optimal solutions, and it has stronger convergence than the other four particle swarm optimization algorithms.
Keywords:
Optimization
Convergence
Particle swarm optimization
Perturbation methods
Sociology
Linear programming
Licenses
Process synthesis
global particle swarm optimization algorithm
global inertia weight
mutation
convergence
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