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The simplicity of bare bone particle swarm optimization (BPSO) is attractive since no parameters tuning is required. Nevertheless, it also encounters the issue of premature convergence. To remedy this problem, by integrated global model and local model search strategies, a unified bare bone particle swarm optimization (UBPSO) is appeared in recently where the weightings of global and local search...
Bare bone particle swarm optimization (BPSO), derived from particle swarm optimization, is a simple optimization technique with the advantage of without using parameters, except the number of particles and generations. Inspect the model of BPSO carefully, one can found that if a particle is restricted to move to a new position only when the new position is better than its original position, the particle...
Teaching-learning based optimization (TLBO), inspired from the teaching-learning process in a classroom, is a newly developed population based algorithm. Except population size and maximum number of iteration, it does not require any specific parameters. TLBO consists of two modes of searching phase, teacher and learner phase. In this paper, every learner is assigned to at least one groups and, instead...
In this paper, a strategy to increase the performance of particle swarm optimization is proposed. The idea is to altering the content of the worst particle of the personal best particles after each iteration. The behavior of the worst personal best particle is then forced to move out its regular path and then affects other particles' behavior. This approach prevents the particles getting stuck on...
Although bare bone particle swarm optimization (BPSO) is a promising algorithm without employing accelerating coefficients compared with traditional particle swarm optimization (PSO), it also inevitably tends to converges prematurely, especially for problems with multiple extremes. In this paper a cooperative learning strategy is applied to enhance the performance of BPSO. The proposed method uses...
This paper investigates a novel varying-frequency isolated drive power supply method using an isolation method to drive a primary side high-voltage circuit of a power supply by a secondary side PWM, while using a varying-frequency method as a control method to enhance effectiveness and efficiency. The control method mainly focuses on the importance of present power management and energy saving, so...
Economic generating electric power is a very important issue for power utilities, especially in current state of fuel cost booming. In this paper, the unified bare bone particle swarm algorithm (UBPSO), which integrates local and global learning strategies, is proposed to solve economic dispatch problems with multiple fuel options. Tested on three systems with different number of units has verified...
It is well known that the dynamic properties of the particles in the particle swarm optimization (PSO) can be described by a second-order difference equation. The convergent properties of the particle are then governed by the roots of the characteristic equation. The roots, or referred to eigenvalues, are functions of the coefficients, which are determined by the inertia weight and acceleration constants...
Economic power dispatch problem plays an important role in the operation of the power systems. The objective of economic dispatch problem is to schedule output of the committed units such that the total fuel cost is minimized while meeting a set of operating constraints. In this paper, two modified particle swarm optimization algorithms with one of the accelerating coefficients being constant are...
Personal best oriented particle swarm optimization (PPSO) is a promising optimizer. The present paper studies a simple form of PPSO which, instead of using three terms in the original velocity formula of PSO and PPSO, only two terms are used in determining the velocity of next step. Investigation shows that this simplified form of PPSO works well for a suite of low-dimensional benchmark functions...
In this paper, a new search strategy for constriction type particle swarm optimization is presented. The modification is based on the observation that personal past best experience is helpful for searching optimal result. As a result, instead of moving particle to the vicinity of current position, by letting the particle to explore the proximity of personal best position, a great improvement in computation...
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