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This paper extends the unlimited capacity location problem with fixed cost in a competitive environment where a firm tends to enter the market competing for exotic products. The demands will increase if a new facility sets into the market. And the demands will be served by both the nearest facilities of the two competitors. A revenue maximization model is presented and a greedy adding and substitution...
Energy efficiency is an important issue for cognitive radio networks, in which each cognitive user must acknowledge experienced environment, besides maintaining communication. Existing works on energy efficiency have been focused on power control in traditional networks (e.g. CDMA network), supposing that the network environment is static. However, there has not been works on energy efficiency under...
An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that...
To solve the premature problem of particle swarm optimization, firstly, the dynamic nonlinear inertia weights are designed which can make particles retain the favorable conditions and converge to the global optima continually; secondly, two kinds of anti-mistake equations are introduced which can make the stagnated particles break away from the local optima and dynamically search the global optima;...
How to improve the efficiency and performance of job scheduling in grid computing is one of the most important and challenging techniques. This paper tries to give out a novel grid job scheduling model based on agent technology. To make full use of intelligence and adaptability of the agents, dynamic fuzzy knowledge-base and corresponding fuzzy reinforcement learning algorithm are proposed for the...
To solve the premature convergence problem of particle swarm optimization, two novel methods are introduced to adjust the inertia weight in parallel according to different fitness values of two dynamic sub-swarms. When the fitness values of the particles are worse than the average, the inertia weight is adjusted by the introduced dynamic piecewise linear chaotic map which can make the local-optima...
Two new methods are introduced to modify the velocity in particle swarm optimization cooperatively: when the fitness values of some particles are worse than the average, the dynamic Zaslavskii chaotic map is devised to modify the velocity, which can make particles break away from the local optima and search global optima dynamically in very complex environments. On the contrary, when the fitness values...
To solve the premature convergence problem of particle swarm optimization, two novel methods are introduced to adjust the inertia weight in parallel according to different fitness values of two dynamic sub-swarms. When fitness values are better than or equal to the average, two types of dynamic nonlinear equations are proposed to adjust the inertia weight in a continuous convex area which can retain...
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