원문정보
초록
영어
Due to the slow convergent speed of particle and easily get trapped in the local optima, a novel simple PSO algorithm with opposition-based learning average elite strategy is proposed. In this algorithm, a particle updating formula of the simplified swarm optimization (sPSO) algorithm is adopted. Moreover, the opposition-based learning elite strategy and Gaussian disturbance are exerted on the personal best particles and then replace personal best particle of sPSO with the average of elite opposite solutions with Gaussian disturbance of personal best particles. The adjustment of inertia weight is based on setting a threshold and then the inertia weight selects each mode adaptively according to its current state. A set of experimental results on benchmark functions demonstrate that the proposed PSO algorithm is an effective and efficient approach for optimization problems. Furthermore, the T-test analysis shows that the proposed algorithm is able to achieve better results.
목차
1. Introduction
2. Basic Description of Simple PSO
3. Design of OLAE-SPSO
3.1. The Introduction of Elite Opposition-Based Learning Strategy
3.2. The Addition of Improved Gauss Disturbance
3.3. The Simplification of The Particle Updating Formula
3.4. Decreasing Inertia Weight Based On Cosine Function
3.5. Procedure of the OLAE-SPSO
3.6. Runtime Complexity of the OLAE-SPSO
4. Algorithm Simulation and Analysis
4.1. Test Functions
4.2. Experimental Parameters Settings
4.3. Experimental Results and Discussion
4.4. T-Test Analysis
5. Conclusion
Acknowledgements
References