An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry

摘要: Display Omitted We propose a novel improved teaching-learning-based optimization algorithm with the concept of historical population.Two new operators are designed in proposed to achieve balance exploration and exploitation ability.24 benchmark functions tested other algorithms verify good ability algorithm.The is applied address combinatorial problem foundry industry design coding decoding mechanism. Teaching-learning-based (TLBO) nature-inspired that mimics teaching learning process. In this paper, an version TLBO (I-TLBO) investigated enhance performance original by achieving between ability. Inspired population, two phases, namely self-feedback phase as well mutation crossover phase, introduced I-TLBO algorithm. learner can improve his result based on experience if present state better than state. learners update their positions probability population obtained operations population. The seeks maintaining while introduction aims at improvement TLBO. effectiveness some heat treating industry. comparative results classic show has significant advantages due