A Mixed Coding Scheme of a Particle Swarm Optimization and a Hybrid Genetic Algorithm with Sequential Quadratic Programming for Mixed Integer Nonlinear Programming in Common Chemical Engineering Practice

In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA-SQP, the mixed coding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA-SQP). The population is separated into two groups: discrete and continuous variables...

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Vydáno v:Chemical engineering communications Ročník 204; číslo 8; s. 840 - 851
Hlavní autoři: Chanthasuwannasin, Manatsanan, Kottititum, Bundit, Srinophakun, Thongchai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 03.08.2017
Taylor & Francis Ltd
Témata:
Chemical engineering
Continuity (mathematics)
Fitness
Genetic algorithm
Genetic algorithms
Heat exchanger network
Mixed integer
Mixed integer nonlinear programming
Mixed-coding
Nonlinear programming
Optimization
Particle swarm
Particle swarm optimization
Quadratic programming
Sequential quadratic programming
Variables
ISSN:0098-6445, 1563-5201
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Shrnutí:In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA-SQP, the mixed coding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA-SQP). The population is separated into two groups: discrete and continuous variables. The discrete variables are optimized by the adapted PSO, while the continuous variables are optimized by the GA-SQP using the discrete variable information from the adapted PSO. Therefore, the population can be set to a smaller size than usual to obtain a global solution. The proposed PSO_GA-SQP algorithm is verified using various MINLP problems including the designing of retrofit heat exchanger networks. The fitness values of the tested problems are able to reach the global optimum.
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ISSN:0098-6445
1563-5201
DOI:10.1080/00986445.2017.1294583