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We consider the energy efficiency-aware user association problem in heterogeneous networks (HetNets). Since there is a large disparity between the transmit power levels of the macro and small cell base stations, the traditional downlink received signal strength indicator (RSSI) based user association results in extreme load imbalance and high uplink interference, which degrades the spectrum efficiency...
This paper focuses on the problem of fair resource allocation in wireless multi-user multi-relay networks where both constant-rate users and variable-rate users exist. The problem is formulated as an optimization problem targeting at finding the Nash bargaining solution for the variable-rate users subject to a set of users' rate constraints and a set of relay power constraints. The formulated problem...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. However, straightforward application of PSO suffers from premature convergence and lacks of intensification around the local best locations. In this paper, we propose a new particle swarm optimization strategy, namely, particle swarm optimization...
Due to the fast convergence, Particle swarm optimization (PSO) has been advocated to be especially suitable for multiobjective optimization. However, there is no information-sharing of with other particles in the population, except that each particle can access the global best. Thus, the premature convergence and lacks of intensification around the local best locations are inevitable during extending...
In this paper, the nonlinear constrained multi-objective environmental economic dispatch (EED) problem is solved using fast multi-objective evolutionary programming (FMOEP). Due to the global warming by fossil fuel, environmental concern becomes more and more important in recent years. The purpose of multi-objective optimization algorithm is minimizing all the different objectives simultaneously and...
Constrained multi-objective differential evolution (CMODE) is a population-based stochastic search technique for solving constrained multi-objective optimization problems. Although CMODE is a powerful and efficient search algorithm, it frequently suffers from pre-mature convergence, especially when there are numerous local Pareto optimal solutions. In this paper, a diversity enhanced constrained multi-objective...
The most issue is designing the fitness function of the chromosome when Generic algorithm is been used for gcalculating the minimal attribute reduction in rough set theory. But with the existed fitness function of the chromosome, the one that the value of the fitness function is larger might not be an attribute reduction. So the optimization candidate attribute reduction might not be the minimal attribute...
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