This study aims to establish equations for energy consumptions of Taiwan Railways Administration (TRA) trains in operation considering the variations of slope to determine the traction energy cost (TEC). Where railway...
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ISBN:
(纸本)9781467365963
This study aims to establish equations for energy consumptions of Taiwan Railways Administration (TRA) trains in operation considering the variations of slope to determine the traction energy cost (TEC). Where railway transportation has been one of the important means of transport for mankind, since TRA became electrified the parameters for train operations have been able to be accurately recorded. The equations for overall energy consumption and resistance parameters are completed for the overall energy consumption of TRA trains and resistance to them to simulate the operation of the whole train model. The inputs of different conditions of trains and track sections are then analyzed for energy use, as the energy use by train travel is obvious. That by the behavior of energy use, can be divided in three conditions for analysis which is acceleration, coasting and deceleration. In the analysis of the energy in the journey, the variation of its consumption is subject to multiple factors because of the difference in operation model and the variation in the track slope data. In view of decreasing unnecessary energy consumption, the equations for train energy consumption and the track parameters are substituted in the teaching-learning-based optimization (TLBO) technique, which can compute the optimal results fast. The optimal results being incorporated in the trains can help reduce unnecessary energy consumption and errors by human operations and arrive at the destination on schedule. By the equations for energy consumption, train parameters, and TLBO, our study proved to be able to reduce energy use as well as ensure of Top Transport Time Expenditure (TTE) of the trains.
Real world problems are also classified to multi-objective optimization problems since they are tailored with more than one objective functions for which the optimization is advantageous simultaneously. The best possi...
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ISBN:
(纸本)9781467369114
Real world problems are also classified to multi-objective optimization problems since they are tailored with more than one objective functions for which the optimization is advantageous simultaneously. The best possible outcome among these objective function is being optimized from the set of solutions rather than finding a single solution. One of the most recently originated teaching-learning-based optimization (TLBO) algorithm in Evolutionary Approaches also addresses such issues. This paper aims to formulate the three transcripts basic, elitist and improved TLBO masterminded by R.V. Rao at a single congregation and investigate their competence and vitality in solving multiple objective functions of clustering techniques when used over real-time datasets. Also, this study annals the contributions made by novel researches in synergizing clustering applications with TLBO.
This paper proposes a Hybrid Cuckoo search algorithm to solve Multi-area economic dispatch problem ( MAED). Hybrid Cuckoo search algorithm is a combination of the Cuckoo search algorithm and teaching-learning-based op...
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ISBN:
(纸本)9781467396820
This paper proposes a Hybrid Cuckoo search algorithm to solve Multi-area economic dispatch problem ( MAED). Hybrid Cuckoo search algorithm is a combination of the Cuckoo search algorithm and teaching-learning-based optimization, where the learner phase of TLBO is added to improve performance of Cuckoo eggs. The proposed method has been applied for solving three tested cases of Multi-area economic dispatch problem. The objective of this problem is to minimize a total generation cost while satisfying generator operational constraints and tie-line constraints. The proposed method has been compared with the conventional Cuckoo search algorithm and teaching-learning-based optimization to obtain its effectiveness. Numerical results show that the proposed method gives better solutions than two compared methods with high performance.
In this paper,a hybrid differential evolution and teaching-learning-based optimization(hDE-TLBO) algorithm is proposed to solve the multi-objective short-term optimal hydro-thermal scheduling(MOSOHTS).In the MOSOHTS m...
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ISBN:
(纸本)9781479900305
In this paper,a hybrid differential evolution and teaching-learning-based optimization(hDE-TLBO) algorithm is proposed to solve the multi-objective short-term optimal hydro-thermal scheduling(MOSOHTS).In the MOSOHTS model, the total fuel cost and emission effects are optimized as two conflict objectives,the water transport delay between reservoirs and the valve-point effect of thermal units are also taken into *** the proposed method,Differential Evolution (DE) and teaching-learning-based optimization(TLBO) algorithm are combined together;DE plays the role of "Teachers Refresher" in TLBO and enhances the overall efficiency of the *** addition,an effective constraint handling approach is presented to deal with the complex *** effectiveness of the proposed method is tested on three scenarios of a hydro-thermal power system with four cascaded hydro plants and three thermal *** results show that hDE-TLBO is a viable alternative way to generate optimal non-dominated scheduling schemes for the MOSOHTS.
This paper intends to incorporate a brain storming mechanism into the existing teaching-learning-based optimization (TLBO) algorithm. The potential solutions of TLBO evolve using the primitive steps that are maintaine...
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ISBN:
(纸本)9783642353796;9783642353802
This paper intends to incorporate a brain storming mechanism into the existing teaching-learning-based optimization (TLBO) algorithm. The potential solutions of TLBO evolve using the primitive steps that are maintained between the acts of teaching and learning. Another novel algorithm, Brain Storm optimization (BSO) sticks to the philosophy of interchange of ideas by a team to develop as a whole. The brain storming methods from BSO are introduced into the working of TLBO and applied to a well-studied electric power dispatch problem of high intricacy. The results are compared to best of the existing solutions to demonstrate the efficacy of the proposed hybrid algorithm.
In this paper, a multiobjective teaching-learning-based optimization algorithm with non-domination based sorting is applied to solve the environmental/economic dispatch (EED) problem containing the incommensurable obj...
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ISBN:
(纸本)9783642271717
In this paper, a multiobjective teaching-learning-based optimization algorithm with non-domination based sorting is applied to solve the environmental/economic dispatch (EED) problem containing the incommensurable objectives of best economic dispatch and least emission dispatch. The address of the environmental concerns that arise in the present day due to the operation of fossil fuel fired electric generators and global warming requires the transformation of the classical single objective economic load dispatch problem into multiobjective environmental/economic dispatch problem. In the work presented a test system of forty units is taken with fuel cost and emission as two conflicting objectives to be optimized simultaneously. The mathematical model used considers practical upper and lower bounds applicable to the generators. The valve point loading of the generator is mimicked in the modeling to accommodate a more realistic system. The simulation result reveals that the proposed approach is a competitive one to the current existing methods for finding the best optimal pareto front of two conflicting objectives and has the better robustness.
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