The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO-GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-c...
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The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO-GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the REF by GA operation and the parameters of the basis and weights by a PSO-GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO-GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models;and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7-11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85 billion kW h. The lowest demand is estimated for the business-as-usual, scenario, and will be 88.45 billion kW h. (C) 2014 Elsevier Ltd. All rights reserved.
Firing dispersion of multi-launch rocket system is affected by launch sequence and firing interval significantly. Firing order and firing interval of the existing multi launch rocket system (MLRS) are optimized to imp...
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ISBN:
(纸本)9783038352884
Firing dispersion of multi-launch rocket system is affected by launch sequence and firing interval significantly. Firing order and firing interval of the existing multi launch rocket system (MLRS) are optimized to improve the firing performance of the existing weapon system without changing the overall design of the weapon system. On one hand, based on optimization problem, the firing dispersion optimal model is established and the genetic algorithm is improved therefore, a sequence of mixed coding genetic algorithm is designed. On the other hand, simulation optimization of firing dispersion has been finished by the aid of fitness function which is based on the optimal model. Meanwhile, it testifies this algorithm's validity and the simulation results can provide a certain reference value for engineering experiment.
Optimising pump scheduling is a complex problem, which involves a large space search, continuous and discrete variables, physical and operational constraints and also multi-objectives. In this paper, multi-objective e...
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Optimising pump scheduling is a complex problem, which involves a large space search, continuous and discrete variables, physical and operational constraints and also multi-objectives. In this paper, multi-objective evolutionary algorithms (MOEAs) combined with a repair mechanism are used to solve the optimal operation problem within water supply system. In this work two objectives are minimised: operation cost (energy cost + treatment cost) and maintenance cost, while one objective is maximised: service level of hydraulic. Decision variables are the settings of the pumps and speed ratio of variable-speed pumps at a time step of the total operational time horizon. A mixed coding methodology and a new crossover operator are developed according to the characteristics of decision variables. Three well-known MOEAS (NSGA-II, epsilon-MOEA and SPEA2) are implemented and compared. Practical application of this method shows that it can make efficient decision to support the operators.
This paper presents an improved genetic algorithm (GA) to minimize weight of truss with sizing, shape and topology variables. Because of the nature of discrete and continuous variables, mixed coding schemes are propos...
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This paper presents an improved genetic algorithm (GA) to minimize weight of truss with sizing, shape and topology variables. Because of the nature of discrete and continuous variables, mixed coding schemes are proposed, including binary and float coding, integer and float coding. Surrogate function is applied to unify the constraints into single one;moreover Surrogate reproduction is developed to select good individuals to mating pool oil the basis of constraint and fitness values, which completely considers the character of constrained optimization. This paper proposes a new strategy of creating next Population by competing between parent and offspring Population based on constraint and fitness values: so that lifetime of excellent gene is prolonged. Because the initial population is created randomly and three operators of GA are also indeterminable, it is necessary to check whether the structural topology is desirable. An improved restart operator is proposed to introduce new gene and explore new space. so that the reliability of GA is enhanced. Selected examples are solved;the improved numerical results demonstrate that the enhanced GA scheme is feasible and effective. Copyright (c) 2005 John Wiley & Sons, Ltd.
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