Open AccessHybrid teaching–learning‐based PSO for trajectory optimisation
Author(s) -
Wang Hongfei,
Li Yongwei
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.0729
Subject(s) - particle swarm optimization , trajectory , convergence (economics) , mathematical optimization , computer science , trajectory optimization , constraint (computer aided design) , function (biology) , artificial intelligence , mathematics , optimal control , physics , astronomy , geometry , evolutionary biology , economics , biology , economic growth
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.