Zhenlei Wang

TL;DR: A self-adaptive teaching-learning-based optimization (SATLBO) that improves the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase.

TL;DR: An improved TLBO (ITLBO) is proposed, in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm.

TL;DR: Experimental results indicate that ICTLBO can obtain a highly competitive performance compared with other state-of-the-art algorithms.

TL;DR: A multiobjective operational model for an industrial cracking furnace system that describes the operation of each furnace based on current feedstock allocations is developed, and this model is used to optimize two important and conflicting objectives: maximization of key products yield and minimization of the fuel consumed per unit ethylene.

TL;DR: The computational results of SA-MTLBO indicate that the operation of Ethylene cracking furnace can be improved by increasing the yields of ethylene, propylene, and butadiene and can generate Pareto optimal fronts with good convergence and distribution.