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This paper presents a novel approach to solve multistage pumping station optimization operation (MPSOO) problem. The problem is analyzed by two-tier mathematical model using decomposition-coordination method of large-scale system theory, and solved by hierarchical clonal selection algorithm (HCSA). The time priority based mutation operation is employed for considering the impact of peak and valley...
The method to handle the constraints is the key factor to success when we are trying to solve constrained optimization problems by quantum-behaved particle swarm optimization. In this paper, a modified quantum-behaved particle swarm optimization is proposed for constrained optimization. Double fitness values are defined for every particle. Whether the particle is better or not will be decided by its...
To achieve braking control of locomotive brake control system (LBCS) accurately and steadily under high nonlinearity various time delay condition, a locomotive brake control method based on T-S fuzzy modeling predictive control (MPC) is proposed. Firstly, the paper uses fuzzy clustering method (FCM) to initial parameters, and uses back-propagation algorithm to rectify rectified its premise parameters...
The partner selection and optimization problem is an important area of virtual enterprise. The model of partner selection is analyzed in this paper. In a virtual enterprise, the whole task can be accomplished by the cooperation among those candidate partners. The optimal objective is to minimize the total cost and completing time. To improve the searching performance for particle swarm optimization...
Biological conclusions reached during microarray experiments can be greatly affected by human intervention, which is currently required in microarray image analysis. Therefore, accurate and automatic analysis of cDNA microarray images becomes crucial. In this paper, an automatic approach to microarray image analysis is presented. The proposed approach is based on the concept of evolution in order...
In this paper we propose an evolutionary computation (genetic algorithm) based approach to economic dispatch problem. In case of thermal power plants fuel cost is an important element. Fuel price tend to rise from time to time. Therefore effort should be given on minimizing the fuel cost. In the proposed approach elitism operation in genetic algorithm is exploited very well to improve the quality...
With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. GA and chaos optimizing was integrated, by use of the chaos serial's property of "ergodicity,...
An ultrasonic piezoelectric transducer to cut the human tissue or to remove the spinal disc is envisioned to improve efficiency and facilitate the surgeons work. Three genetic algorithms have been developed. The first one is based on conditional genetic operators, the second one is based on a specific crossover operator definition with a local search method, and the third is a combination of both...
There are several factors that influence the design of optimal IEEE 802.16e networks. In the first stage of the network development, the most important issue is to find an appropriate solution for the base station location from a given set of candidate sites. The network can be considerably improved if the base station location solution found during the planning phase is designed to achieve optimal...
The work described in his paper aims at exploring the use of soft computing techniques for designing a controller to perform control of level in a spherical tank. First, system identification of this nonlinear process is done using black box model, which is identified to be non linear and approximated to be a first order plus dead time (FOPDT) model. Then the controller tuning strategy has been applied...
Back Propagation (BP) Neural Network has the ability of self-studying, self-adapting, fault tolerance and generalization. But there are some defaults in its basic application. Such as low convergence speed, local extremes and so on. So there are some limitations in practice. A quantitative forecast method based on the BP Neural Network improved by genetic algorithm (GA) is proposed in the paper. And...
The principle and the main characteristics of genetic algorithm (GA) were stated. Crossover was a main process producing new generation through genetic genes recombination at the whole exploration space in GA. But some traditional crossover mechanism made the extent and profundity of cross among operators limited. There were some thorny issues such as crossover efficiency slowly, some operator perdition...
In this paper, a mathematical model of synthetically optimizing mechanics properties for ships is constructed. Basing on delamination, parallel algorithm, genetic algorithm (GA) and chaos algorithm, then a delamination-parallel genetic chaos algorithm basing on delicate variables' segments is advanced. This complex algorithm is programmed by VC++ into software which has a user-friendly graphical interface...
The dominant models for inventory control of repairable items, both in the literature and in practical applications, are based on the assumption of infinite or ample repair capacity. However this assumption is not accurate in practice. In this paper, a two-echelon inventory system of repairable items is studied, which has finite repair capacity. Regarding the number of repair bench at depot as one...
A two-echelon inventory system of repairable items is considered. For the repairable items with high-value and low- demand rate that include condemnation, continuous (S-1,S) ordering strategy is adopted. Neighbor support strategy for inventory pooling is presented to obtain high service levels at a low cost. The premise of neighbor support is that it is more efficient to obtain needed stock from the...
Ant colony system (ACS) and a few improved ant colony optimization (ACO) methods for solving optimization problems with continuous domain were studied. Based on these work, a new ACO method for solving nonlinear geophysical multi-parameter inversion was presented. Several techniques are employed, such as decimal coding, mapping mechanism, roulette wheel selection of genetic algorithm (GA), renormalization,...
An agent-oriented approach for optimization of internal consumption of gas transmission network (GTN) is presented in this paper. In the proposed approach, a number of software agents have been developed to minimize the internal consumption of GTN. The main agents are coordinator and search agents. The structure of the agents is logical and layered, respectively. The first agent is developed in JACK...
Optimization problems in the steady state analysis of power systems aim at minimizing or maximizing some objective function. Traditional methods use mathematical programming techniques to obtain the optimum solution. Artificial intelligence (AI) methods have been shown to exhibit greater flexibility in solving the optimization problem. Genetic algorithm (GA) technique is used in the paper with a new...
This paper presents a new method to find minimum number of Phasor Measurement Units (PMUs), to determine the fault location of all the transmission network lines. Considering the installation cost of PMUs, it is important to investigate the placement scheme of the PMUs at minimal locations on the network in the sense that the fault location observability can be achieved over the entire network. In...
In this paper we develop a variation of the particle swarm optimization (PSO) algorithm that is tailored to discrete optimization problems. We focus on solving Sudoku puzzles but the ideas can be extended to other problems with discrete solutions. We compare our PSO-based algorithm to the classic PSO and to a (mu+lambda) evolutionary strategy (ES) for 50 puzzles and find that the PSO algorithms do...
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