Teaching-learning based optimization with global crossover for global optimization problems

• Published in: Applied mathematics and computation Vol. 265; pp. 533 - 556
• Main Authors: Ouyang, Hai-bin, Gao, Li-qun, Kong, Xiang-yong, Zou, De-xuan, Li, Steven
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.08.2015
• Subjects: Crossover; Crossovers; Education; Global optimization; Heuristic methods; Learning; Mathematical models; Optimization; Searching; Teaching; Teaching learning based optimization; Crossover; Global optimization; Teaching learning based optimization
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2015.05.012

Teaching learning based optimization (TLBO) is a newly developed population-based meta-heuristic algorithm. It has better global searching capability but it also easily got stuck on local optima when solving global optimization problems. This paper develops a new variant of TLBO, called teaching learning based optimization with global crossover (TLBO-GC), for improving the performance of TLBO. In teaching phase, a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima. And a new global crossover strategy is incorporated into the learning phase, which aims at balancing local and global searching effectively. The performance of TLBO-GC is assessed by solving global optimization functions with different characteristics. Compared to the TLBO, several modified TLBOs and other promising heuristic methods, numerical results reveal that the TLBO-GC has better optimization performance.