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Reservoir optimal operation is a nonlinear, multi-stage and strong constraint combinatorial optimization problem. As the standard particle swarm optimization (SPSO) easily trapped into local optima, this paper proposes a hybrid algorithm combining particle swarm optimization algorithm with chaotic search algorithm, also referred to as CPSO algorithm. Making use of the stochastic property and ergodicity...
When a local optimal solution is reached with classical Particle Swarm Optimization (PSO), all particles in the swarm gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present in this paper a novel variant of PSO algorithm, called MPSOM, that uses Metropolis equation to update local best solutions (lbest) of each particle and uses mutation...
Motivated by the growing demand of accuracy and low computational time in optimizing functions in various fields of engineering, an approach has been presented using the technique of parallel computing. The parallelization has been carried out on one of the simplest and flexible optimization algorithms, namely the particle swarm optimization (PSO) algorithm. PSO is a stochastic population global optimizer...
Swarm Intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. Although the swarm algorithms have exhibited good performance across a wide range of application problems, it is difficult to analyze the convergence. We discuss the...
Several strategies have been proposed to provide quality solutions to the unit commitment problem and increase the potential saving in the power system operation. These include deterministic and stochastic search algorithms. One of the limitations of deterministic approaches is, they suffer from the curse of dimensionality when dealing with the modern power system with large number of generators....
A novel dynamic particle swarm optimization algorithm based on chaotic mutation (DCPSO) is proposed to solve the problem of the premature and low precision of the common PSO. Combined with linear decreasing inertia weight, a kind of convergence factor is proposed based on the variance of the populationpsilas fitness in order to adjust ability of the local search and global search; The chaotic mutation...
In this paper, the particle swarm optimizer is modified to create the multi-swarm accelerating PSO which is applied to dynamic continuous functions. Different from the existing multi-swarm PSOs and local versions of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these swarms are regrouped frequently by using various regrouping schedules...
A recurrent wavelet neural network (RWNN) controller is proposed in this study to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive to track periodic reference trajectories. First, the dynamic model of the PMLSM drive system is derived. Next, an RWNN controller is proposed to control the PMLSM. Moreover, the connective weights, translations and dilations of the RWNN...
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