The particle swarm optimization (PSO) algorithm is a swarm intelligence (SI) algorithm used to solve optimization problems. Owing to its advantages in simplicity, using only a few parameters, PSO has ...

This hybrid technique combines PSO and GA concepts to create new candidate by crossover and mutations while applying PSO operators to increase the diversity of solutions. Kao, Yi-Tung, and Erwie ...

This study presents the application of a new hybrid particle swarm optimization and ant lion algorithm (Hybrid PSO-ALO) to resolve the economic dispatch problem (ED) considering the limits on ...

Algorithm Overview. The MFO-PSO-SSA hybrid algorithm is designed to address the challenge of finding the optimal set of thresholds for multilevel image segmentation. It combines the exploration ...

Researchers have developed a quantum particle swarm optimization algorithm for maximum power point tracking that reportedly generates 3.33% more power in higher temperature tests and 0.89% more ...

Flowchart of the SR PSO . ... “The 25-iteration limit in the symbolic regression algorithm based on PSO allows for a brief and controlled execution time (0.175 hours), ...

For the PSO, the initial parameters of the particle swarm are set as follows: the population size n is 50, the maximum number of iterations k max is 1,000 and 600, the speed limit v lim is [−2, 2], ...

As PSO is a naturalistic exploration method as opposed to gradient-based optimisation, it has the drawback of being a sluggish procedure (Roy et al., 2021b). On the other hand, complicated non-linear ...