Iterative ant-colony algorithm and its application to dynamic optimization of chemical process

Zhang, Bing; Chen, Dezhao; Zhao, Wenwei

doi:10.1016/j.compchemeng.2005.05.020

Cited by 58 publications

(53 citation statements)

References 22 publications

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“…Zhang et al [3] proposed IACA for dynamic optimization. In the first step of this method, both the control region and time period are discretized, while in IDP, the time interval, control…”

Section: Methods I: Region Reduction Strategy (Rrs)mentioning

confidence: 99%

“…Ray and Szekely [2] and Ramirez [4] proposed a gradient-based algorithm, which is applied to the Hamiltonian function to solve the optimization problem analytically. The main disadvantage of this method is that the algorithm may converge to local minima in many cases [3]. In addition, Lee and Ramirez [5] found an analytical solution for industrial foreign protein production by recombinant bacteria.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed a lower level of accuracy when compared with other methods, but the method is easier to implement than IDP. The Iterative Ant Colony Algorithm (IACA) was recently proposed by Zhang et al [3] and applied to some dynamic optimization problems. The first step of IACA is time span and control region discretization to change the continuous dynamic system to a discrete one.…”

Section: Introductionmentioning

confidence: 99%

“…A typical dynamic optimization problem for a continuous process is described as follows [2][3][4] with 0 ≤ t ≤ t f and u min < u < u max , where x and u are the state and control variables, respectively. Several methods have been utilized to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Optimization in Chemical Processes Using Region Reduction Strategy and Control Vector Parameterization with an Ant Colony Optimization Algorithm

Asgari

Pishvaie

2008

Chem Eng & Technol

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Two different approaches of the dynamic optimization for chemical process control engineering applications are presented. The first approach is based on discretizing both the control region and the time interval. This method, known as the Region Reduction Strategy (RRS), employs the previous solution in its next iteration to obtain more accurate results. Moreover, the procedure will continue unless the control region becomes smaller than a prescribed value. The second approach is called Control Vector Parameterization (CVP) and appears to have a large number of advantages. In this approach, control action is generated in feedback form, i.e., a set of trial functions of the state variables are expanded by multiplying by some unknown coefficients. By utilizing an optimization method, these coefficients are calculated. The Ant Colony Optimization (ACO) algorithm is employed as an optimization method in both approaches.

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“…Zhang et al [3] proposed IACA for dynamic optimization. In the first step of this method, both the control region and time period are discretized, while in IDP, the time interval, control…”

Section: Methods I: Region Reduction Strategy (Rrs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Optimization in Chemical Processes Using Region Reduction Strategy and Control Vector Parameterization with an Ant Colony Optimization Algorithm

Asgari

Pishvaie

2008

Chem Eng & Technol

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“…Comprehensive information on ACO can be found in Dorigo and Stuetzle [14] Several extensions of the ACO metaheuristic for continuous search domains can be found in the literature, among them Socha and Dorigo [32], Yu et al [36], Dreo and Siarry [15] or Kong and Tian [27]. Other applications of ACO frameworks for real-world problems, arising from engineering design applications, can be found in Jayaraman et al [25], Rajesh et al [29], Chunfeng and Xin [10] or Zhang et al [37]. In contrast extensions for mixed integer search domains are very rare in the literature.…”

Section: Introductionmentioning

confidence: 99%

Extended ant colony optimization for non-convex mixed integer nonlinear programming

Schlüter

Egea

Banga

2009

Computers & Operations Research

206

124

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Two novel extensions for the well known Ant Colony Optimization (ACO) framework are introduced here, which allow the solution of Mixed Integer Nonlinear Programs (MINLP). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results.These extensions on the ACO framework have been developed to serve the needs of practitioners who face highly non-convex and computationally costly MINLPs. The performance of this new method is evaluated considering several non-convex MINLP benchmark problems and one real-world application. The results obtained by our implementation substantiate the success of this new approach.

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

et al. 2013

Asia-Pacific J Chem Eng

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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.

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Iterative ant-colony algorithm and its application to dynamic optimization of chemical process

Cited by 58 publications

References 22 publications

Dynamic Optimization in Chemical Processes Using Region Reduction Strategy and Control Vector Parameterization with an Ant Colony Optimization Algorithm

Dynamic Optimization in Chemical Processes Using Region Reduction Strategy and Control Vector Parameterization with an Ant Colony Optimization Algorithm

Extended ant colony optimization for non-convex mixed integer nonlinear programming

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

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