A hybrid optimization method by incorporating adaptive response strategy for Feedforward neural network

Abstract

Particle swarm optimisation algorithm (PSO) possesses a strong exploitation capability due to its fast search speed. It, however, suffers from an early convergence leading to its inability to preserve diversity. An improved particle swarm optimiser is proposed based on a constriction factor and Gravitational Search Algorithm to overcome premature convergence. The constriction factor ensures an appropriately controlled transition from exploration into exploitation, leading to an enhanced diversity and appropriate learning rate adjustment throughout the search process. We introduce Gravitational Search Algorithm to enhance the exploratory ability of PSO. An adaptive response strategy is incorporated to activate stagnated particles to curtail the high tendency to get trapped in a local optimum. To verify the efficacy of the improvement strategies, we employ the proposed algorithm in training a Single Layer Feedforward neural network to classify real-world data ranging from binary to multi-class datasets of which our proposed algorithm outperforms the others.