Hybrid teaching–learning‐based PSO for trajectory optimisation

Abstract

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.