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Using Particle Swarm Optimization for Mixed Integer Non-linear Programming in Process Synthesis

Rongshan Bi (brs***at***ecss.com.cn)
Xia Yang (yangxia***at***ecss.com.cn)

Abstract: Process synthesis problems can be mathematically represented as mixed-integer nonlinear programming (MINLP) models, which are often irregular, large and non-convex and difficult to get the overall optimum by traditional method. In this paper, a new method named particle swarm optimization (PSO) is used to solve MINLP problems. By introduced penalty function and used sigmoid function, the PSO algorithm, which originally can only used in continues variables with max/min limitations, can solve the MINLP problems with equations and inequalities constraints. A few examples used to demonstrate the validity of the method and compared with the results of other methods, the results show that the PSO algorithm is a efficient method to deal with MINLP problems.

Keywords: process synthesis, mixed-integer nonlinear programming

Category 1: Applications -- Science and Engineering

Category 2: Applications -- Science and Engineering


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Entry Submitted: 01/02/2004
Entry Accepted: 01/02/2004
Entry Last Modified: 01/02/2004

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