Dynamic Optimization of a Continuous Polymer Reactor Using a Modified Differential Evolution Algorithm

Lee, Moo Ho; Han, Chong Hun; Chang, Kun Soo

doi:10.1021/ie980373x

Cited by 66 publications

(31 citation statements)

References 26 publications

(57 reference statements)

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“…The DE algorithm was applied by previous authors to solve parameter estimation and optimal control problems [25,[35][36][37] and has been found to be efficient and reliable. In this study, a DE was used to estimate model parameters and to find an optimal temperature profile.…”

Section: Discussionmentioning

confidence: 99%

“…Pontryagin's minimum principle, are efficient only for simple optimal control problems, whereas the parameterization methods, e.g. successive quadratic programming (SQP), when applied to nonlinear, multimodal, and discontinuous systems will often converge to a local optimum and not to the desired global optimum [14]. Differential Evolution algorithms is one of Evolutionary Algorithms (EA) belonging to a class of random search and global optimization methods.…”

Section: Optimal Control Problemmentioning

confidence: 99%

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Application of a mathematical model and Differential Evolution algorithm approach to optimization of bacteriocin production by Lactococcus lactis C7

Moonchai

Madlhoo

Jariyachavalit

et al. 2005

Bioprocess Biosyst Eng

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The effect of pH and temperature on cell growth and bacteriocin production in Lactococcus lactis C7 was investigated in order to optimize the production of bacteriocin. The study showed that the bacteriocin production was growth-associated, but declined after reaching the maximum titer. The decrease of bacteriocin was caused by a cell-bound protease. Maximum bacteriocin titer was obtained at pH 5.5 and at 22 degrees C. In order to obtain a global optimized solution for production of bacteriocin, the optimal temperature for bacteriocin production was further studied. Mathematical models were developed for cell growth, substrate consumption, lactic acid production and bacteriocin production. A Differential Evolution algorithm was used both to estimate the model parameters from the experimental data and to compute a temperature profile for maximizing the final bacteriocin titer and bacteriocin productivity. This simulation showed that maximum bacteriocin production was obtained at the optimal temperature profile, starting at 30 degrees C and terminating at 22 degrees C, which was validated by experiment. This temperature profile yielded 20% higher maximum bacteriocin productivity than that obtained at a constant temperature of 22 degrees C, although the total amount of bacteriocin obtained was slightly decreased.

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Section: Discussionmentioning

confidence: 99%

Section: Optimal Control Problemmentioning

confidence: 99%

Application of a mathematical model and Differential Evolution algorithm approach to optimization of bacteriocin production by Lactococcus lactis C7

Moonchai

Madlhoo

Jariyachavalit

et al. 2005

Bioprocess Biosyst Eng

View full text Add to dashboard Cite

show abstract

“…In this paper, two strategies are employed for mutation: DE/rand/1 and DE/ current-to-best/1 that use Eqs. 1 and 2, respectively (see Price 1999 andLee et al 1999). But for simplicity, the DE which uses Eq.…”

Section: The De Algorithmmentioning

confidence: 99%

Three modified versions of differential evolution algorithm for continuous optimization

2010

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Differential evolution (DE) is one simple and effective evolutionary algorithm (EA) for global optimization. In this paper, three modified versions of the DE to improve its performance, to repair its defect in accurate converging to individual optimal point and to compensate the limited amount of search moves of original DE are proposed. In the first modified version called bidirectional differential evolution (BDE), to generate a new trial point, is used from the bidirectional optimization concept, and in the second modified version called shuffled differential evolution (SDE), population such as shuffled frog leaping (SFL) algorithm is divided in to several memeplexes and each memeplex is improved by the DE algorithm. Finally, in the third modified version of DE called shuffled bidirectional differential evolution (SBDE) to improve each memeplex is used from the proposed BDE algorithm. Three proposed modified versions are applied on two types of DE and six obtained algorithms are compared with original DE and SFL algorithms. Experiments on continuous benchmark functions and non-parametric analysis of obtained results demonstrate that applying bidirectional concept only improves one type of the DE. But the SDE and the SBDE have a better success rate and higher solution precision than original DE and SFL, whereas those are more time consuming on some functions. In a later part of the comparative experiments, a comparison of the proposed algorithms with some modern DE and the other EAs reported in the literature confirms a better or at least comparable performance of our proposed algorithms.

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“…The critical value of F which gives a compromise between the intensification and diversification of the population is calculated analytically. Another suggestion is due to Lee et al [16], where a small initial value F ∈ (0, 1) is set at each iteration k. Then for each target vector x i,k , a trial vector y i,k is found as in the original DE. If the trial vector y i,k is better than the target vector…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

“…This process of creating a new trial vector and comparing it with the previous trial vector continues until there is no more improvement in the objective function. In [16], the value of C R is set to 0.5. In general, however, effective values of F and C R lie in the ranges [0.4, 1] and [0.4, 0.9], respectively [1,14].…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

Differential evolution algorithms using hybrid mutation

Kaelo

Ali

2007

Comput Optim Appl

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Differential evolution (DE) has gained a lot of attention from the global optimization research community. It has proved to be a very robust algorithm for solving non-differentiable and non-convex global optimization problems. In this paper, we propose some modifications to the original algorithm. Specifically, we use the attraction-repulsion concept of electromagnetism-like (EM) algorithm to boost the mutation operation of the original differential evolution. We carried out a numerical study using a set of 50 test problems, many of which are inspired by practical applications. Results presented show the potential of this new approach.

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Dynamic Optimization of a Continuous Polymer Reactor Using a Modified Differential Evolution Algorithm

Cited by 66 publications

References 26 publications

Application of a mathematical model and Differential Evolution algorithm approach to optimization of bacteriocin production by Lactococcus lactis C7

Application of a mathematical model and Differential Evolution algorithm approach to optimization of bacteriocin production by Lactococcus lactis C7

Three modified versions of differential evolution algorithm for continuous optimization

Differential evolution algorithms using hybrid mutation

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