Abstract
Objective: In view of the issue that particle swarm optimization algorithm is easy to fall into local optimal solution and low convergence precision when searching for the feasible space solution of complex multimodal functions, the paper proposes an adaptive particle swarm optimization algorithm (PSO) based on aggregation degree.
Methods: The algorithm firstly makes a random disturbance to search global optimal solution through differential evolution method according to the size of population aggregation to increase the diversity of particle swarm and improve its global searching ability. Then the adaptive adjustment is made to the inertia weight parameter of particles in accordance with the aggregation degree of particles.
Conclusion: The simulation example of typical optimization problems suggests that this algorithm can improve the population diversity, particle activity, convergence performance and searching ability of PSO.
Keywords: Particle swarm optimization, aggregation degree, differential evolution, inertia weight, adaptive adjustment, PSO algorithm.
Graphical Abstract
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