An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization

Bechikh, Slim; Chaabani, Abir; Saïd, Lamjed Ben

doi:10.1109/tcyb.2014.2363878

Cited by 74 publications

(20 citation statements)

References 37 publications

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“…In the same area, CRO algorithm which inspired from the chemical reactions of molecules is developed and becoming popular in researchers to tackle the real life NP-hard problems (Lam, Li, and Yu, 2012;Truong, Li, and Xu, 2013). Recently, CRO has modified by incorporating several features from other metaheuristics such as SA and PSO and it is one of the most powerful search algorithms in solving mono-objective optimization problems (Bechikh et al 2015;Lam and Li 2010). Therefore, we have employed the variant of CRO algorithm to solve our mathematical model.…”

Section: Assumptionsmentioning

confidence: 99%

Bulk wheat transportation and storage problem of public distribution system

Mogale

Kumar

Mrquez

et al. 2017

Computers & Industrial Engineering

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This research investigates the multi-period multi-modal bulk wheat transportation and storage problem in a two-stage supply chain network of Public Distribution System (PDS). The bulk transportation and storage can significantly curtail the transit and storage losses of food grains, which leads to substantial cost savings. A mixed integer non-linear programming model (MINLP) is developed after studying the Indian wheat supply chain scenario, where the objective is to minimize the transportation, storage and operational cost of the food grain incurred for efficient transfer of wheat from producing states to consuming states. The cost minimization of Indian food grain supply chain is a very complex and challenging problem because of the involvement of the many entities and their constraints such as seasonal procurement, limited scientific storages, varying demand, mode of transportation and vehicle capacity constraints. To address this complex and challenging problem of food grain supply chain, we have proposed the novel variant of Chemical Reaction Optimization (CRO) algorithm which combines the features of CRO and Tabu search (TS) and named it as a hybrid CROTS algorithm (Chemical reaction optimization combined with Tabu Search). The numerous problems with different sizes are solved using the proposed algorithm and obtained results have been compared with CRO. The comparative study reveals that the proposed CROTS algorithm offers a better solution in less computational time than CRO algorithm and the dominance of CROTS algorithm over the CRO algorithm is demonstrated through statistical analysis.

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Section: Assumptionsmentioning

confidence: 99%

Bulk wheat transportation and storage problem of public distribution system

Mogale

Kumar

Mrquez

et al. 2017

Computers & Industrial Engineering

View full text Add to dashboard Cite

show abstract

“…Chemical reaction optimization (CRO) is a meta-heuristic algorithm proposed by Lam in 2010. Inspired by the interaction between molecules in chemical reactions to find the lowest potential energy in potential energy surface, the algorithm adopts four primary reactions and follows the law of energy conservation [66]. From the perspective of microscopic analysis of chemical reactions, we can see that at the initial stage of a chemical reaction, the state of the molecules in the container is unstable due to excessive energy in the molecules.…”

Section: Chemical Reaction Optimizationmentioning

confidence: 99%

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

et al. 2020

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Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

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“…The ultimate outcome of a chemical reaction is the reaction products, which are formed in a process characterized by a diminishing reaction potential energy. It means that the CRO is an optimization process in which the potential energy of the search system is minimized [41][42][43][44][45][46].…”

Section: Chemical Reaction Optimization Algorithmmentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

et al. 2019

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The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

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An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization

Cited by 74 publications

References 37 publications

Bulk wheat transportation and storage problem of public distribution system

Bulk wheat transportation and storage problem of public distribution system

An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil

A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine

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