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This paper presents an improved particle swarm optimization (PSO) based maximum power point tracking (MPPT) strategy. Modeling of the PV cell and effect of the partially shaded condition (PSC) on the P_V characteristics are investigated. Typical PSO algorithm is modified to provide a better convergence and faster performance. A novel strategy is also proposed for reinitialization to reduce tracking...
Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from early attempts to simulate the behavior of birds looking for food. Estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms that build and maintain a probabilistic model capturing the search space characteristics and continuously use this model to generate new individuals. In this...
Because the variable inertia weight particle swarm optimization algorithm is easy to fall into the local optimum, this paper introduces the improved simulated annealing operator, chaotic disturbance operator and Cauchy mutation operator to the former and proposes an improved particle swarm optimization algorithm; Then, two typical Benchmark functions are used to test the performance of basic the proposed...
In this paper, first of all, we introduce the normal particle swarm optimization algorithms (PSO), for this kind of algorithms, there are problems like it easily stuck at a local minimum point and its convergence speed is slow. To overcome this, an improved particle swarm optimization algorithms is presented for improving global and local search ability of PSO. That is, the rate of particle convergence...
In order to prevent premature convergence of constriction factor particle swarm optimization (cfPSO), an improved version of cfPSO is presented in this paper. Firstly, a new standard of premature convergence was set up to determine the algorithm relapsing into local extremum. Then an improved re-initialization method was design to insure the uniform distribution of swarm in search space. In this method,...
Inspired by the diffusion movement phenomenon of the molecules, this paper presents a diffusion-repulsion particle swarm optimization (DRPSO). The proposed new algorithm (DRPSO) includes attraction and repulsion (or migration) phases. Once the diversity of population becomes too low, the individuals will be dispersed and carry out diffusion movement, while if the diversity of population becomes too...
The difficulty for accurate determination of the angles of arrival (AOA) of signals arises from the optimization of likelihood functions of high dimension. Usually, a gradient-based technique is employed to find the optimum of the function. However, this method requires heavy computational work and the differentiability of the likelihood function. This paper presents two gradient-free methods: One...
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