Abstract
Teaching-Learning-Based Optimization (TLBO) algorithm was developed to solve single-objective optimization problems. TLBO is inspired by the theory of teaching-learning mechanism. Basic TLBO works better for unimodal problems but poorly for multi-model problems because of its poor exploration. To provide a fair exploration for solving complex optimization problems, we redefined the learning strategy to the basic TLBO. This newly redefined variant is called Multi-Teacher Teaching-Learning Based Optimization (MT-TLBO). The performance of MT-TLBO was tested on the latest optimization benchmark functions, CEC-C06, 2019. It is observed that the performance of MT-TLBO is superb comparatively. After that, it is simulated to solve workflow scheduling problems by minimizing the execution cost of the standard workflow and maximizing the workload on computing resources in a cloud environment. Finally, it is found that MT-TLBO produces minimal execution cost and a fair workload distribution on resources on standard benchmark workflow, comparatively.
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