Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems

Ponsich, Antonin; Azzaro‐Pantel, Catherine; Domenech, Serge; Pibouleau, Luc

doi:10.1021/ie060733d

Cited by 33 publications

(22 citation statements)

References 41 publications

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“…One of the most popular binary GAs is the bin-NSGA-II developed by Deb et al 11 Genetic algorithms have been successfully implemented on MINLP problems, such as batch plant design. 12,13 Another class of discrete optimization algorithms, which belong to Ant Colony Optimization (ACO), have also been reported in the literature. 14,15 Applications of ACO-based algorithms to discrete optimization problems include vehicle routing, sequential ordering, and graph coloring.…”

Section: B Existing Mixed-discrete Optimization Approachesmentioning

confidence: 99%

Avoiding Premature Convergence in a Mixed-Discrete Particle Swarm Optimization (MDPSO) Algorithm

Chowdhury¹,

Zhang²,

Messac³

2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

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Over the past decade or so, Particle Swarm Optimization (PSO) has emerged to be one of most useful methodologies to address complex high dimensional optimization problemsit's popularity can be attributed to its ease of implementation, and fast convergence property (compared to other population based algorithms). However, a premature stagnation of candidate solutions has been long standing in the way of its wider application, particularly to constrained single-objective problems. This issue becomes all the more pronounced in the case of optimization problems that involve a mixture of continuous and discrete design variables. In this paper, a modification of the standard Particle Swarm Optimization (PSO) algorithm is presented, which can adequately address system constraints and deal with mixed-discrete variables. Continuous optimization, as in conventional PSO, is implemented as the primary search strategy; subsequently, the discrete variables are updated using a deterministic nearest vertex approximation criterion. This approach is expected to avoid the undesirable discrepancy in the rate of evolution of discrete and continuous variables. To address the issue of premature convergence, a new adaptive diversity-preservation technique is developed. This technique characterizes the population diversity at each iteration. The estimated diversity measure is then used to apply (i) a dynamic repulsion towards the globally best solution in the case of continuous variables, and (ii) a stochastic update of the discrete variables. For performance validation, the Mixed-Discrete PSO algorithm is successfully applied to a wide variety of standard test problems: (i) a set of 9 unconstrained problems, and (ii) a comprehensive set of 98 Mixed-Integer Nonlinear Programming (MINLP) problems.

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Section: B Existing Mixed-discrete Optimization Approachesmentioning
confidence: 99%

Avoiding Premature Convergence in a Mixed-Discrete Particle Swarm Optimization (MDPSO) Algorithm
Chowdhury¹,
Zhang²,
Messac³
2012
53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
11
0
8
0
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“…In the literature dedicated to batch plant design (see Ponsich et al, 2007), the item sizes (volumes for batch stages and treatment capacity for semi-continuous stages) are continuous variables. Yet, it seems obvious that in the industrial practice, the design of operation unit equipment does not require such a level of accuracy, which seems not realistic.…”

Section: Discrete Size Of Equipmentmentioning
confidence: 99%

Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design
Azzaro‐Pantel¹,
Zaraté²
2009
Engineering Applications of Artificial Intelligence
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a b s t r a c tThis paper presents a MultiObjective Genetic Algorithm (MOGA) optimization framework for batch plant design. For this purpose, two approaches are implemented and compared with respect to three criteria, i.e., investment cost, equipment number and a flexibility indicator based on work in process (the so-called WIP) computed by use of a discrete-event simulation model. The first approach involves a genetic algorithm in order to generate acceptable solutions, from which the best ones are chosen by using a Pareto Sort algorithm. The second approach combines the previous Genetic Algorithm with a multicriteria analysis methodology, i.e., the Electre method in order to find the best solutions. The performances of the two procedures are studied for a large-size problem and a comparison between the procedures is then made.

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“…Ponsich et al [10]; Haupt [11]; Sekishiro and Todoroki [12]; Özcelik [13]; and the references therein). From the presented several numerical results of using GAbased approaches to solve the MDCNLP problems, it can be seen that (i) the cases studied by Rao and Xiong [2] are more complex, complete and detailed than those studied in the literatures, and (ii) the GA-based method proposed by Rao and Xiong [2] can provide a more efficient way than those GA-based methods presented in the literatures.…”

Section: Introductionmentioning
confidence: 99%

Design Variables Optimization of Mechanical Problems
Lee
¹
,
Tsai
²
,
Ho
³

et al. 2008
2008 3rd International Conference on Innovative Computing Information and Control
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An improved genetic algorithm (IGA) is presented to solve the mixed-discrete-continuous design optimization problems. The IGA approach combines the traditional genetic algorithm with the experimental design method. The experimental design method is incorporated in the crossover operations to systematically select the better genes to tailor the crossover operations in order to find the representative chromosomes to be the new potential offspring, so that the IGA approach possesses the merit of global exploration and obtains better solutions.

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Mixed-Integer Nonlinear Programming Optimization Strategies for Batch Plant Design Problems

Cited by 33 publications

References 41 publications

Avoiding Premature Convergence in a Mixed-Discrete Particle Swarm Optimization (MDPSO) Algorithm

Avoiding Premature Convergence in a Mixed-Discrete Particle Swarm Optimization (MDPSO) Algorithm

Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design

Design Variables Optimization of Mechanical Problems

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