A Hybrid Teaching-Learning-Based Optimization Algorithm for the Travel Route Optimization Problem alongside the Urban Railway Line

Fu-ying, Liu; Liu, Chen; Zhao, Qi; He, Chenhao

doi:10.3390/su13031408

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…Xu et al [57] proposed a novel TLBO variant named dynamic-opposite learning TLBO (DOLTLBO), which employs a new Dynamic-Opposite Learning (DOL) strategy to overcome premature convergence. Liu et al [58] proposed a hybrid metaheuristic teaching-learning-based optimization (HTLBO) for the travel route optimization problem alongside the urban railway line, where OBL is embedded for enhancing HTLBO's exploration ability.…”

Section: B Teaching-learning-based Optimization and Opposition-based ...mentioning

confidence: 99%

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

He,

Liu

2023

IEEE Access

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At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), Teaching-Learning-Based Optimization (TLBO), Opposition-Based Learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO's performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency.

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Section: B Teaching-learning-based Optimization and Opposition-based ...mentioning

confidence: 99%

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

He,

Liu

2023

IEEE Access

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“…However, the quality of a teacher will impact the learner's outcome. With time, TLBO has been accepted widely for addressing different optimization problems such as numerical and engineering [196], knapsack optimization [197], route optimization [198], and so on. Similarly, TLBO method was adopted in t-way testing strategy to solve combinatorial optimization problem in the works of [199] for mixed strength t-way test suites, and [200] for pairwise testing.…”

Section: Human-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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“…Placement of distributed STATCOM (D-STATCOM) is developed as a solution to the multiobjective problem. The TLBO method is a new efficient optimization technique that was inspired by a student learning process [27], [28]. This paper uses the superior qualities of TLBO and PSO, presenting a novel algorithm that incorporates the evolutionary natures of both algorithms (denoted as TLBO-PSO).…”

Section: Introductionmentioning

confidence: 99%

Application of hybrid TLBO-PSO algorithm for allocation of distributed generation and STATCOM

Ansari

Byalihal

2022

IJEECS

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This paper proposes a hybrid teaching learning-based optimization- particle swarm optimization (TLBO-PSO) Algorithm for optimal distributed generation (DG) and STATCOM placement. A multi-objective formulation is developed that optimizes the DG and STATCOM placement in IEEE 33 and real-time 52 bus distribution system. The objective function formulated involves maximizing cost-benefit and voltage stability factors while minimizing network security index and power losses. Simulation is carried out in MATLAB/Simulink for different systems and different cases. The results for single and multiple STATCOM and DG are compared with TLBO and PSO algorithm and the results exhibit better convergence performance with the hybrid optimization algorithm.

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A Hybrid Teaching-Learning-Based Optimization Algorithm for the Travel Route Optimization Problem alongside the Urban Railway Line

Cited by 9 publications

References 35 publications

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Application of hybrid TLBO-PSO algorithm for allocation of distributed generation and STATCOM

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