Modern Control Engineering
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Information Processing Letters
The generalized PSO: a new door to PSO evolution
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A novel particle swarm optimizer hybridized with extremal optimization
Applied Soft Computing
Simplifying Particle Swarm Optimization
Applied Soft Computing
Analysis of particle interaction in particle swarm optimization
Theoretical Computer Science
Expert Systems with Applications: An International Journal
Implementation of hybrid ANN-PSO algorithm on FPGA for harmonic estimation
Engineering Applications of Artificial Intelligence
Convergence analysis and improvements of quantum-behaved particle swarm optimization
Information Sciences: an International Journal
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Randomization in particle swarm optimization for global search ability
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this study, we found that engineering experience can be used to determine the parameters of an optimization algorithm. We came to this conclusion by analyzing the dynamic characteristics of PSO through a large number of experiments. We constructed a relationship between the dynamic process of particle swarm optimization and the transition process of a control system. A novel parameter strategy for PSO was proven in this paper using the overshoot and the peak time of a transition process. This strategy not only provides a series of flexible parameters for PSO but it also provides a new way to analyze particle trajectories that incorporates engineering practices. In order to validate the new strategy, we compared it with published results from three previous reports, which are consistent or approximately consistent with our new strategy, using a suite of well-known benchmark optimization functions. The experimental results show that the proposed strategy is effective and easy to implement. Moreover, the new strategy was applied to equally spaced linear array synthesis examples and compared with other optimization methods. Experimental results show that it performed well in pattern synthesis.