Improving teaching–learning-based-optimization algorithm by a distance-fitness learning strategy

Xu, Yujie; Peng, Yi; Su, Xinyi; Yang, Zhile; Ding, Chao; Yang, Xiaofeng

doi:10.1016/j.knosys.2022.108271

Cited by 35 publications

(19 citation statements)

References 45 publications

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“…Conversely, a class of learners can be defined as P , standing for Population, and following P G = [X 1,G , X 2,G , ..., X Np,G ] where G refers to the generation in question, X i,G the vector of the individual number i, and N p , the population size [58]. Knowing the dimension of subjects as D, X i,G = [x 1i,G , x 2i,G , ..., x Di,G ] T defines each vector X i,G (i = 1, 2, ..., N p ) as an individual of generation G [58].…”

Section: Teaching-learning-based-optimization Algorithmmentioning

confidence: 99%

“…Consequently, the teacher, designated as X t,G refers to the learner with the best fitness among a population of learners, in a specific generation G [58]. A teacher phase vector…”

Section: Tlbo Algorithm: Teacher Phasementioning

confidence: 99%

“…where a random value r i ∈ (0, 1), i = 1, 2, ..., N p , a mean vector of all the individuals in the population of generation G, M G , and a learning weight, valued at either 1 or 2, T F = round[1 + rand(0, 1){2 − 1}], are included [58]. This equation allows for a new solution to be generated according to the mean of the population and the previous best solution.…”

Section: Tlbo Algorithm: Teacher Phasementioning

confidence: 99%

“…where the fitness function is identified by f (.) [58]. As such, greedy selection refers to the process of the above-mentioned equation.…”

Section: Tlbo Algorithm: Teacher Phasementioning

confidence: 99%

“…where m = n, a random value r m ∈ (0, 1), and two individuals obtained from a random selection in the population of generation G, X m,G and X n,G , are observed [58]. This equation allows for the generation of a new solution via the assistance of other solutions considered as partners.…”

Section: Tlbo Algorithm: Learner Phasementioning

confidence: 99%

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Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Berrou¹,

Kalbani²,

Antonijevic³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

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Spam and emails have always been intrinsically linked since the creation of the Advanced Research Projects Agency Network, otherwise known as (ARPANET). The latter witnessed, on May 3rd, 1978, the first known spam email to date. Today, spam emails negatively affect the users' productivity and private lives. A significant number of approaches emerged in the past two decades that deal with the spam detection problem, with limited success. Therefore, the current paper presents an intelligent and automated solution to spam email detection using a logistic regression model trained by a teaching-learning-based optimization algorithm. The proposed solution has been tested on two benchmark spam email datasets (CSDMC2010 and TurkishEmail), and evaluated against seven other contending cutting-edge metaheuristics utilized in the same experimental setup. The simulation outcomes without a doubt indicate the superior level of accuracy achieved by the proposed solution.

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Section: Teaching-learning-based-optimization Algorithmmentioning

confidence: 99%

“…Consequently, the teacher, designated as X t,G refers to the learner with the best fitness among a population of learners, in a specific generation G [58]. A teacher phase vector…”

Section: Tlbo Algorithm: Teacher Phasementioning

confidence: 99%

Section: Tlbo Algorithm: Teacher Phasementioning

confidence: 99%

“…where the fitness function is identified by f (.) [58]. As such, greedy selection refers to the process of the above-mentioned equation.…”

Section: Tlbo Algorithm: Teacher Phasementioning

confidence: 99%

Section: Tlbo Algorithm: Learner Phasementioning

confidence: 99%

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Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Berrou¹,

Kalbani²,

Antonijevic³

et al. 2023

Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)

View full text Add to dashboard Cite

show abstract

Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

Hachemi,

Sadaoui,

Arif

et al. 2023

Energy Science & Engineering

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This paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

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An Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature Selection

Bao

2023

Lecture Notes in Computer Science

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Improving teaching–learning-based-optimization algorithm by a distance-fitness learning strategy

Cited by 35 publications

References 45 publications

Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Training a Logistic Regression Machine Learning Model for Spam Email Detection Using the Teaching-Learning-Based-Optimization Algorithm

Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

An Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature Selection

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