Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

Abstract

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

“…The inertia weight parameter is important for optimization. Some of them are inertia weights that have been introduced in research [16][17][18][19][20][21]. In this study, the logarithm decreasing inertia weight (LogDIW) of PSO [22] was used to optimize the CNN hyperparameter.…”