A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier

Nobakhti, Amin; Wang, Hong

doi:10.1016/j.asoc.2006.12.005

Cited by 108 publications

(39 citation statements)

References 37 publications

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“…Herein, the total budget was set as 200 and 230. The compromise solutions calculated by NSDE and TOPSIS are shown in Figure 11, which are [1, 1, 1, 1, 1, 1, 1, 5, 1 , 5, 8, 7, 12, 6, 19, 5, 21, 30, 21, 32] and [1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 8, 7, 12, 6, 21,5,24,30,27,40]. Compared to the original settings, the maximum difference between these programs is related to the ninth and tenth weapons.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…A simple mutation operation and a one-on-one competitive survival strategy reduce the complexity of a genetic operation. DE is robust and has a strong global convergence ability, suitable for solving certain optimization problems that conventional mathematical programming methods cannot solve in complex environments [27]. Given the advantages of DE and the characteristics of this model, we designed the following solving algorithm.…”

Section: Algorithm Designmentioning

confidence: 99%

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Operation Loop-Based Optimization Model for Resource Allocation to Military Countermeasures versus Probabilistic Threat

et al. 2018

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Weapons development planning is an unstructured and complex multi-criteria decisionmaking problem, especially in antagonistic environments. In this paper, the defender's decision was modelled as a high complexity non-linear optimization problem with limited resources. An operation loop with realistic link rules was first proposed to model the cooperation relationships among weapons in the defense system. The system dynamics principle was used to characterize the dynamic behavior of the nodes in a complex weapons network. Then, we used cumulative threat and development risk to measure different planning solutions by considering the opponent and uncertainties in the development process. Next, an improved Differential Evolution (DE) and Non-Dominated Sorting Differential Evolution (NSDE) were designed to determine the optimal planning solutions for a single objective and multi-objective. The compromise solution, based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), was used to evaluate the Pareto solution set of the multi-objective. Finally, an illustrative case was studied to verify the feasibility and validity of the proposed model.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Algorithm Designmentioning

confidence: 99%

Operation Loop-Based Optimization Model for Resource Allocation to Military Countermeasures versus Probabilistic Threat

et al. 2018

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“…It was applied with great success for solving different types of problems from different areas. The problems tackled are both benchmarks [11][12][13][14][15][16][17] and real-life [18][19][20][21]. Some examples for chemical engineering applications include: oxidation processes [22][23][24], energy, fuels and petrol derivatives [25][26][27][28][29], fermentation [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

Curteanu

Drăgoi

Dafinescu

2015

Advances in Computational Intelligence

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Abstract.A modelling procedure based on hybrid configuration composed of artificial neural networks, differential evolution and clonal selection algorithms is developed and applied in this work. The neural network represents the model of the system, while the differential evolution and clonal selection algorithms perform a simultaneous topological and parametric optimization of the model. The results indicated that the combination of the two optimizers produces better results compared with each of them working separately. As case study, styrene polymerization, a complex process which is difficult to model when taking into consideration all the internal interactions, was chosen. Neural networks, designed in an optimal form, proved to be adequate tools for modelling this system.

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“…In SDE, the mutation weighting factor F is self-adapted by a mutation strategy similar to the mutation operator of DE. Nobakhti and Wang [23] proposed a Randomized Adaptive Differential Evolution (RADE) method, where a simple randomized self-adaptive scheme was proposed for the mutation weighting factor F . Das et al [24] [26] proposed an improved DE method, where the population size is gradually reduced.…”

Section: Related Work To Dementioning

confidence: 99%

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

2010

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A simple self-adaptive Differential Evolution algorithm with application on the ALSTOM gasifier

Cited by 108 publications

References 37 publications

Operation Loop-Based Optimization Model for Resource Allocation to Military Countermeasures versus Probabilistic Threat

Operation Loop-Based Optimization Model for Resource Allocation to Military Countermeasures versus Probabilistic Threat

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization

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