Showing papers on "Particle swarm optimization published in 2000"

Comparing inertia weights and constriction factors in particle swarm optimization

Abstract: The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.

A particle swarm optimization for reactive power and voltage control considering voltage security assessment

Abstract: Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

Cooperative learning in neural networks using particle swarm optimizers

Abstract: This paper presents a method to employ particle swarms optimizers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each which is optimized cooperatively in its own swarm. the application of this technique to neural network training is investigated, with promising results.

Particle swarm optimisation for evolving artificial neural network

Abstract: The information processing capability of artificial neural networks (ANNs) is closely related to its architecture and weights. The paper describes a new evolutionary system for evolving artificial feedforward neural networks, which is based on the particle swarm optimisation (PSO) algorithm. Both the architecture and the weights of ANNs are adaptively adjusted according to the quality of the neural network. This process is repeated until the best ANN is accepted or the maximum number of generations has been reached. A strategy of evolving added nodes and a partial training algorithm are used to maintain a close behavioural link between the parents and their offspring. This system has been tested on two real problems in the medical domain. The results show that ANNs evolved by PSONN have good accuracy and generalisation ability.

A particle swarm optimization for reactive power and voltage control considering voltage stability

Abstract: This paper presents a particle swarm optimization for reactive power and voltage control considering voltage stability. The proposed method determines a control strategy with continuous and discrete control variables such as AVR operating values, OLTC tap positions, and the amount of reactive power compensation equipment. The method also considers voltage stability using a continuation power flow technique. The feasibility of the proposed method is demonstrated on model power systems with promising results.

Global optimization algorithms for training product unit neural networks

Abstract: Product units in the hidden layer of multilayer neural networks provide a powerful mechanism for neural networks to efficiently learn higher-order combinations of inputs. Training product unit networks using local optimization algorithms is difficult due to an increased number of local minima and increased chances of network paralysis. The paper discusses the problems with using gradient descent to train product unit neural networks, and shows that particle swarm optimization, genetic algorithms and LeapFrog are efficient alternatives to successfully train product unit neural networks.

Battery pack state of charge estimator design using computational intelligence approaches

Abstract: This paper presents a novel design of a battery pack state of charge (SOC) estimator for electric vehicles using computational intelligence techniques. The main framework of the estimator is a three-layer feedforward neural network with four inputs and one output (estimated SOC). The inputs are the battery pack current, accumulated ampere hours, average pack temperature and minimum voltage of the battery modules. A strategy is developed to select the training data set from a large amount of the original testing data sets under different drive cycles and operating conditions. A modified particle swarm optimization (PSO) algorithm is used to train the proposed neural network. The designed SOC estimator is validated and evaluated using the testing data under different drive profiles and temperatures. The errors of the SOC estimates are well within the acceptable range compared to that obtained by using traditional mathematical models. The resulting SOC estimator is computationally efficient and can be easily implemented using low-cost microprocessors.

Multiobjective particle swarm optimization

Abstract: Evolutionary algorithms (EAs) are search procedures based on natural selection [2]. They have been successfully applied to a wide variety of optimization problems [4]. Particle Swarm Optimization (PSO) [1,7] is a new type of evolutionary paradigm that has been successfully used to solve a number of single objective optimization problems (SOPs). However, to date, no one has applied PSO in an effort to solve multiobjective optimization problems (MOPs). The purpose of our research is to demonstrate how PSO can be modified to solve MOPs. In addition to showing how this can be done, we demonstrate its effectiveness on two MOPs.

Distribution state estimation considering nonlinear characteristics of practical equipment using hybrid particle swarm optimization

Abstract: This paper proposes a distribution state estimation method using a hybrid particle swarm optimization (HPSO). The proposed method considers practical measurements in distribution systems and assumes that absolute values of voltage and current can be measured at the secondary side buses of substations (S/Ss) and RTUs (remote terminal units) in distribution systems. The method can estimate load and distributed generation output values at each node considering nonlinear characteristics of the practical equipment in distribution systems. The feasibility of the proposed method is demonstrated and compared with the original PSO on practical distribution system models. The results indicate the applicability of the proposed state estimation method to the practical distribution systems.

A new hybrid algorithm based on collaborative line search and Particle Swarm Optimization

Abstract: Recently Particle Swarm Optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improved by using hybrid techniques. There are various hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristics algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and basic PSO (B-PSO). The performance of the proposed hybrid algorithm, examined through four typically nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms basic PSO.

Particle Swarm Optimisation in Feedforward Neural Network

Abstract: The paper describes a new evolutionary system for evolving artificial neural networks (ANN’s) called PSONN, which is based on the particle swarm optimisation (PSO) algorithm. The PSO algorithm is used to evolve both the architecture and weights of ANN’s, this means that an ANN’s architecture is adaptively adjusted by PSO algorithm, then the nodes of this ANN’s are also evolved by PSO algorithm to evaluate the quality of this network architecture. This process is repeated until the best ANN’s is accepted or the maximum number of generations has been reached. In PSONN, a strategy of evolving added nodes and a partial training algorithm are used to maintain a close behavioural link between the parents and their offspring, which improves the efficiency of evolving ANN’s. PSONN has been tested on two real problems in the medical domain. The results show that ANN’s evolved by PSONN have good accuracy and generalisation ability.

Human and Computer Learning Together In the Exteriorized Particle Swarm

Abstract: The particle swarm is a computer algorithm for function optimization based on simulation of principles of human social behavior. Individual problem-solution vectors inte ract in a population until a criterion is met. The present e xperiment replaces one of the computational agents with a human being. It is shown that the human performs as well as the computational beings on an extremely simple pro blem, and worse than them on a harder one.