An Improved Teaching-Learning Based Optimization Algorithm and Its Application to Aero-Engine Start Model Adaptation

Feng, Hailin; Li, Qiuhong

doi:10.1109/access.2019.2939653

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…These algorithms have been recently introduced as powerful stochastic optimization algorithms [14]− [20]. Most nature-inspired metaheuristic algorithms rely on random variables and have some parameters to be fitted to handle the problem under study [16]- [18]. In contrast, TLBO and SOS have no tuned parameters.…”

Section: The Employment Of the Candidate Optimization Algorithms mentioning

confidence: 99%

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

et al. 2020

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The complexity of achieving optimal power flow in the presence of renewable resources decreases the accuracy and optimality levels of the power system due to the associated intermittency and uncertainty. The increased challenge of large-scale deployment of wind energy necessitates the proper modeling of wind impact on power system security and reliability levels. This article discusses a new reliable power flow optimization tool that accounts for wind power availability and uncertainty. An accurate wind forecast model is created to maintain power system security considering wind power variability. The error of the forecasting phase is included in the proposed model to accurately predict the available wind power. In this work, the scattered wind data is converted into informative frequency distribution considering the effect of averaging around integers, halves, and quarters. The proposed method maximizes the utilization level of wind energy without deteriorating the system security. The accuracy of the new proposed work is presented by comparing its results with other models discussed in the literature. A complete and integrated formulation of the objective function has been accomplished. The cost function includes transmission losses, generation operating costs, generation gas emissions, and valve-point effects. Reliable and efficient optimization algorithms are adopted to minimize the established cost function of the system ⎯namely, teaching-learningbased optimization and symbiotic organisms search algorithms. The effectiveness of the proposed approach is validated using the IEEE 39-bus system. INDEX TERMS Generation gas emission, generation operating cost, optimal power flow, symbiotic organisms search algorithm, valve-point effects, wind speed forecast.

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Section: The Employment Of the Candidate Optimization Algorithms mentioning

confidence: 99%

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

et al. 2020

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“…Wang et al [69], improved the exploitation capability of the sine cosine algorithm using an adaptive probability selection technique. In [70], an improved version of the TLBO algorithm has been proposed to enhance the searching ability and accuracy of the basic TLBO [37] algorithm. This has been achieved by introducing an Sshaped group learning phase instead of the random learning phase.…”

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confidence: 99%

Border Collie Optimization

Dutta

Bhattacharyya

Dey³

et al. 2020

IEEE Access

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In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

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An Improved Teaching-Learning Optimization Algorithm based on Morlet Wavelet Variation

Wei

Huang

2021

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)

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An Improved Teaching-Learning Based Optimization Algorithm and Its Application to Aero-Engine Start Model Adaptation

Cited by 3 publications

References 31 publications

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

Border Collie Optimization

An Improved Teaching-Learning Optimization Algorithm based on Morlet Wavelet Variation

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