원문정보
초록
영어
This paper aims to present a self-adaptive global particle swarm optimization (SGPSO) algorithm for solving unconstrained optimization problems. In the new algorithm, the inertia weights are generated based on Gaussian distribution, which is helpful to improve the diversity of the population. In addition, the worst particle is updated by averaging the other particles, which is beneficial to improving the quality of the population. Finally, a global disturbance is adopted to increase the convergence rate of SGPSO. In the disturbance process, a disturbance factor is utilized to control the searching ranges of the population, which can effectively keep a balance between the global exploration and local exploitation. Twenty well-known benchmark functions are considered to evaluate the performance of SGPSO, and 50 runs are implemented in each case. Numerical experiments and comparisons demonstrate that SGPSO is superior to the other three algorithms according to means, standard deviations and convergence rate.
목차
1. Introduction
2. Four Particle Swarm Optimization Algorithms
2.1. The Original Particle Swarm Optimization Algorithm
2.2. The Particle Swarm Optimization Algorithm based on Linearly Decreased Inertia Weight
2.3. Bare Bones Particle Swarm Optimization (BBPSO)
2.4. Center Particle Swarm Optimization Algorithm
3. Self-adaptive Global Particle Swarm Optimization Algorithm
3.1. Adjust Inertia Weight by using a Self-adaptive Strategy
3.2. Update the Worst Particle
3.3. Disturb the Global Best Particle
4. Experimental Results and Analysis
5. Conclusion and Discussion
Acknowledgements
References