Showing papers on "Particle swarm optimization published in 1999"

Empirical study of particle swarm optimization

Abstract: We empirically study the performance of the particle swarm optimizer (PSO). Four different benchmark functions with asymmetric initial range settings are selected as testing functions. The experimental results illustrate the advantages and disadvantages of the PSO. Under all the testing cases, the PSO always converges very quickly towards the optimal positions but may slow its convergence speed when it is near a minimum. Nevertheless, the experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.

The swarm and the queen: towards a deterministic and adaptive particle swarm optimization

Abstract: A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter. We define a "no-hope" convergence criterion and a "rehope" method so that, from time to time, the swarm re-initializes its position, according to some gradient estimations of the objective function and to the previous re-initialization (it means it has a kind of very rudimentary memory). We then study two different cases, a quite "easy" one (the Alpine function) and a "difficult" one (the Banana function), but both just in dimension two. The process is improved by taking into account the swarm gravity center (the "queen") and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.

Particle swarm optimiser with neighbourhood operator

Abstract: In recent years population based methods such as genetic algorithms, evolutionary programming, evolution strategies and genetic programming have been increasingly employed to solve a variety of optimisation problems. Recently, another novel population based optimisation algorithm - namely the particle swarm optimisation (PSO) algorithm, was introduced by R. Eberhart and J. Kennedy (1995). Although the PSO algorithm possesses some attractive properties, its solution quality has been somewhat inferior to other evolutionary optimisation algorithms (P. Angeline, 1998). We propose a number of techniques to improve the standard PSO algorithm. Similar techniques have been employed in the context of self organising maps and neural-gas networks (T. Kohonen, 1990; T.M. Martinez et al., 1994).

Particle swarm optimization: surfing the waves

Abstract: A new optimization method has been proposed by J. Kennedy and R.C. Eberhart (1997; 1995), called Particle Swarm Optimization (PSO). This approach combines social psychology principles and evolutionary computation. It has been applied successfully to nonlinear function optimization and neural network training. Preliminary formal analyses showed that a particle in a simple one-dimensional PSO system follows a path defined by a sinusoidal wave, randomly deciding on both its amplitude and frequency (Y. Shi and R. Eberhart, 1998). The paper takes the next step, generalizing to obtain closed form equations for trajectories of particles in a multi-dimensional search space.

Human tremor analysis using particle swarm optimization

Abstract: The paper presents methods for the analysis of human tremor using particle swarm optimization. Two forms of human tremor are addressed: essential tremor and Parkinson's disease. Particle swarm optimization is used to evolve a neural network that distinguishes between normal subjects and those with tremor. Inputs to the neural network are normalized movement amplitudes obtained from an actigraph system. The results from this preliminary investigation are quite promising, and work is continuing.

The particle swarm: social adaptation in information-processing systems

Abstract: A clutch mechanism of the rocker-shift type has a carrier member interposed between and affixed to one of a rotatable driving and a rotatable driven member. The carrier member has circumferentially spaced rocker arms, having normally self-disengaging clutch portions, that are adapted to be rocked radially, on at least one end thereof, by means of a shift yoke and collar, for selective rocking engagement into coupling engagement with adjacent corresponding normally self-disengaging clutch portions on the other of the driving and driven members. The collar has locked neutral and locked engaged positions on the carrier, with the improvement comprising means for axially shifting the collar to an unlocked engaged position intermediate the locked neutral and locked engaged positions and yieldingly maintaining the collar in this unlocked engaged position at a predetermined torque level, with the collar being axially shifted back into locked neutral position as result of the separation of the self-disengaging clutch portions when the predetermined torque level is exceeded by the torque transfer between the driving and driven members.

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

Abstract: 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 on-line 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 pieces of reactive power compensation equipment. 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.

A particle swarm optimization for reactive power and voltage control in electric power systems

Abstract: This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control: VVC) in electric power systems considering voltage security 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 on-line VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of onload tap changer (OLTC) of transformers, and the number of reactive power compensation equipment The method considers voltage security using a continuation power flow (CPFLOW) and contingency analysis technique The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical electric power system models with promising results

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.