Hybrid teaching-learning-based PSO for trajectory optimisation

A hybrid modified teaching-learning-based particle swarm optimisation (HMTL-PSO) initialised by the normalised step cost (NSC), named HMTL-NSCPSO, is proposed for solving trajectory optimisation with complex constraint problems. Specially, the new HMTL-NSCPSO combines the canonical PSO basic policy, the teaching-learning-based optimisation (TLBO) algorithm and the normalised step cost (NSC) function in order to promote diversity, obtain well-speed convergence and to improve search ability. The algorithm is tested on an UAV trajectory optimization problems. Experimental results validate the effectiveness of the HMTL-NSCPSO.