imperialistic competitive algorithm (ICA) is a recently developed meta-heuristic technique that has been successfully implemented for solving various complex optimization problems. Like other meta- heuristic technique...
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ISBN:
(纸本)9781509012770
imperialistic competitive algorithm (ICA) is a recently developed meta-heuristic technique that has been successfully implemented for solving various complex optimization problems. Like other meta- heuristic techniques, performance of ICA predominantly depends on the selection of control parameters. However, optimal setting of control parameter for ICA is problem specific. In this paper, a study of control parameter setting for an imperialistic competitive algorithm based economic emission dispatch problem with valve point loading and transmission loss is carried out. Economic emission dispatch problem was formulated by taking equality constraint on power balance and inequality constraint on generator capacity limit and losses into consideration. Proposed approach was examined on a ten generating unit system and effects of parametric variation on results are presented. Analysis of obtained results reveals that an improper selection of algorithm control parameter might lead to premature convergence and poor computation efficiency.
In order to accurately predict the dissolved gas content for the transformer oil in a period of time in the future, imperialist competitivealgorithm (ICA) and the least squares support vector machine regression (LSSV...
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ISBN:
(纸本)9781538621561
In order to accurately predict the dissolved gas content for the transformer oil in a period of time in the future, imperialist competitivealgorithm (ICA) and the least squares support vector machine regression (LSSVR) were introduced to the prediction model. ICA is employed to optimize the hyper-parameters of constructed SVM regression, and the parameters of the radial basis function (RBF) kernel function are optimized and the prediction model is established. The results show that the proposed model has obvious advantages and the prediction effect is generally higher than BPNN, which verifies the correctness of the proposed method and the feasibility of the scheme. The MAPE in training of RBF - LSSVR less than 1.3% while BPNN's higher than 8.8%, at the same time, the MAPE in testing of RBF -LSSVR less than 1.8% while BPNN's higher than 9.0%.
Reservoir operation plays an important role in economic development of a region. Hedging operations were used for municipal, industrial, and irrigation water supplies from reservoirs in the past. However, hedging oper...
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Reservoir operation plays an important role in economic development of a region. Hedging operations were used for municipal, industrial, and irrigation water supplies from reservoirs in the past. However, hedging operation for hydropower reservoir operation is very rare. A practically simple and useful new form of Standard Operation Policy and a new form of hedging rules for hydropower production are introduced in this paper and demonstrated with a case study for hydropower reservoir operation of Indirasagar reservoir system in India. The performance of optimal hedging rules is compared with that of a new standard operation policies and the superiority (reliability increases by about 10%) of the hedging rules is demonstrated. When the number of decision variables is increased from 5 to 15, energy production increases by 0.7%, the spill is reduced by 16.8%, and reliability slightly decreases by 2.1%. A bi-level simulation-optimization algorithm is used for optimizing the hedging rules. For optimization, Genetic algorithm, artificial bee colony algorithm, and imperialistic competitive algorithms are utilized. The results indicate that all the three algorithms are competitive and artificial bee colony algorithm is marginally better than the other two. (C) 2017 Sharif University of Technology. All rights reserved.
Tunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent bac...
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Tunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. (c) 2014 American Society of Civil Engineers.
In this paper, an efficient hybrid approach to deal with multi-period heat exchanger networks (HENs) synthesis, which is inherently formulated as a mixed-integer non-linear programming (MINLP) model, is proposed. In t...
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In this paper, an efficient hybrid approach to deal with multi-period heat exchanger networks (HENs) synthesis, which is inherently formulated as a mixed-integer non-linear programming (MINLP) model, is proposed. In this novel two surfaces approach, the creation and determination of optimal HENs structures are accomplished by an imperialist competitivealgorithm (ICA) on the first surface. In each HEN structure, there is a sequence of stages containing the addresses of the exchanger(s). The HENs generated by the ICA are sent to the second surface, where their minimum total annual cost (TAC) is calculated as an overall objective function. This surface works on two levels. For all periods of each HEN, the local optimal solutions are determined based on the maximum energy recovery at the outer level, including an external search loop and a linear programming (LP) model. Based on the outer level results, the ICA is re-used, at the inner level, to find the final minimum TAC of the network. As a result, the MINLP model is transformed into a relatively linear LP + ICA hybrid model, which is easily solvable. The results demonstrate that this approach can sometimes reduce the network TAC even by over 7.2% compared to the literature. (c) 2022 Elsevier Ltd. All rights reserved.
In this paper an imperialistic competition algorithm (ICA) based optimization technique is presented for solving the short-term economic emission dispatch (EED) problem in a hydrothermal system with cascaded reservoir...
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ISBN:
(纸本)9781479974566
In this paper an imperialistic competition algorithm (ICA) based optimization technique is presented for solving the short-term economic emission dispatch (EED) problem in a hydrothermal system with cascaded reservoirs. Various operating constraints of the hydro system such as flow rate limit, reservoir storage volume limit, dynamic water balance, power generation limit and transportation delay between cascaded reservoirs are taken into account while for thermal units valve point effects and generation limits are considered in problem formulation. The bi-objective EED problem is converted to a single objective problem by price penalty factor approach. The proposed approach is successfully implemented on a test system comprising of three thermal units and four cascaded hydro units. A comparison of the result obtained by the proposed approach with those provided by other population based meta-heuristic techniques established proposed technique's superiority both in terms of result quality and computation time.
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