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In this paper, a novel improved multiobjective particle swarm optimization (IMOPSO) is proposed for solving the optimal reactive power dispatch (ORPD) problem with multiple and competing objectives. In order to improve the global search capability and the nondominated solutions diversity, time variant parameters, mutation operator, and dynamic crowding distance are incorporated into the MOPSO algorithm...
This paper aims at the characteristics of reactive power optimization of the electric power system with the wind farm; proposing a new method for reactive power optimization on the entire power grid which uses the Parallel Immune Particle Swarm Optimization. It uses integer and real number hybrid encoding, improves the efficiency of compiling. And it combines the continuous and discrete particle swarm...
One of the most important features of the PSO algorithm is its fast convergence. This is a positive feature as long as there's no premature convergence. Inspired by the phenomenon of quorum sensing behavior in the bacteria, we incorporate this bio-behavior into PSO and MOPSO to maintain the swarm diversity and promote global exploration when the velocity of each particle in the swarm is rather small...
In order to improve convergence speed and precision of optimization in quantum particle swarm optimization (QPSO), an improved quantum particle swarm optimization (IQPSO) algorithm was presented. Chaotic sequences were used to initialize the origin angle position of particle, mutation operation algorithm was used to increase diversity of population and avoid premature convergence. The proposed algorithm...
In order to overcome the disadvantages of premature and local convergence in the traditional particle swarm optimization (PSO), an improved chaotic PSO algorithm based on adaptive inertia weight (AIWCPSO) is proposed. The initial population is generated by using chaotic mapping appropriately, in order to improve both the diversity of population and the periodicity of particles. The value of the new...
In this paper, a fast-sorting method called summation of normalized objectives and diversified selection (SNOV-DS) is embedded in Comprehensive Learning Particle Swarm Optimization (CLPSO) to solve multi-objective problems. Due to this method, the simulation time will be decreased. The convergence to true Pareto front and the spread of solutions can also be improved. The algorithm is tested on a set...
In wireless sensor networks, the energy supply is limited and the node will be dead while the energy is out of use. In order to solve the energy consumption problem about the sizes of clusters, a novel grid clustering algorithm based on location information is proposed in the paper: the node is planned to the corresponding grid according to the location information.While we can get the sizes of clusters...
In this paper, The improved Particle Swarm Optimization in dynamic objective function environment (DOFPSO) is purposed. The dynamic environment will change with the time t. The DOFPSO algorithm discuss that how to determine changes of the time (environment) and how to keep population diversity. The improved algorithm has the ability to fast response the change of environment and could find the best...
In the traditional improved Particle Swarm Optimization algorithms, the search spaces of the particles are always fixed. In this paper, based on the standard particle swarm optimization (PSO) algorithm, a dynamic search space particle swarm optimization algorithm (DSPPSO) based on population entropy is proposed. The population entropy is introduced to describe the particles' location confusion degree,...
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