Optimal Power Flow via Teaching-Learning-Studying-Based Optimization Algorithm

Akbari, Ebrahim; Ghasemi, Maryam; Gil, Milad; Rahimnejad, Abolfazl; Gadsden, S. Andrew

doi:10.1080/15325008.2021.1971331

Cited by 36 publications

(31 citation statements)

References 98 publications

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“…One such algorithm is Teaching-Learning Based Optimization (TLBO), which simulates educational interactions between teachers and students in the classroom 65 . Teaching-learning-studying-based optimizer (TLSBO) 66 is a method that enhanced TLBO by adding a new strategy called "studying strategy", in which each member uses the information from another randomly selected individual to improve its position. Dynamic Group Strategy TLBO (DGSTLBO) 67 is an improved TLBO algorithm that enables each learner to learn from the mean of his corresponding group.…”

Section: Literature Reviewmentioning

confidence: 99%

Mother Optimization Algorithm: A New Human-Based Metaheuristic Approach for Solving Engineering Optimization

Matoušová

Trojovský

Dehghani

et al. 2023

Preprint

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This paper presents a new metaheuristic method called Mother Optimization Algorithm (MOA) that mimics the human interaction between a mother and her children. The fundamental inspiration of MOA is to simulate the mother's care of children in three phases education, advice, and upbringing. The mathematical model of MOA used in the search process and exploration is presented. The performance of MOA is assessed on a set of 52 benchmark functions, including unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, and the CEC 2017 test suite. The results indicate that MOA's proposed approach, which balances exploration and exploitation, effectively supports the search process and can generate appropriate solutions for optimization problems. The outcomes quality obtained from MOA has been compared with the performance of twelve often-used metaheuristic algorithms. Upon analysis and comparison of the simulation results, it was found that the proposed MOA outperforms competing algorithms with superior and significantly more competitive performance. Specifically, the proposed MOA delivers better results in most objective functions. Furthermore, the application of MOA on four engineering design problems demonstrates the efficacy of the proposed approach in solving real-world optimization problems.

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Section: Literature Reviewmentioning

confidence: 99%

Mother Optimization Algorithm: A New Human-Based Metaheuristic Approach for Solving Engineering Optimization

Matoušová

Trojovský

Dehghani

et al. 2023

Preprint

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“…This paper proposes a multi-objective teaching-learning studying-based algorithm (MTLSBA), an improved version of TLBA that improves the TLBA's entire searchability and handling of multi-objective problems. The proposed update focuses on incorporating a strategic adjustment to the TLBA, which is characterized as a study approach wherein every individual obtains knowledge from a randomly chosen participant to improve their position [41]. Additionally, the proposed MTLSBA is updated to incorporate an extra Pareto archive to preserve the non-dominated solutions.…”

Section: An Adaptive Algorithm With Quadratic and Polyhedral Relaxationsmentioning

confidence: 99%

“…The presented strategy helps to avoid the local optima and increases the algorithm's strength. Throughout this stage, the jth component attempts to adapt and improve its position by precisely adjusting each portion of its position [41] as:…”

Section: Proposed Studying Strategymentioning

confidence: 99%

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

et al. 2022

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This paper proposes a multi-objective teaching–learning studying-based algorithm (MTLSBA) to handle different objective frameworks for solving the large-scale Combined Heat and Power Economic Environmental Dispatch (CHPEED) problem. It aims at minimizing the fuel costs and emissions by managing the power-only, CHP and heat-only units. TLSBA is a modified version of TLBA to increase its global optimization performance by merging a new studying strategy. Based on this integrated tactic, every participant gathers knowledge from someone else randomly to improve his position. The position is specified as the vector of the design variables, which are the power and heat outputs from the power-only, CHP and heat-only units. TLSBA has been upgraded to include an extra Pareto archiving to capture and sustain the non-dominated responses. The objective characteristic is dynamically adapted by systematically modifying the shape of the applicable objective model. Likewise, a decision-making approach based on the fuzzy concept is used to select the most suitable CHPEED solution for large-scale dispatching of combined electrical power and heat energies. The proposed MTLSBA is assigned to multiple testing of 5-unit, 7-unit and 96-unit systems. It is contrasted with other reported techniques in the literature. According to numerical data, the suggested MTLSBA outperforms the others in terms of effectiveness and robustness indices. For the 5-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 0.6625% and 0.3677% and reduction in the emissions of 2.723% and 7.4669% compared to non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA 2), respectively. For the 7-unit system, the proposed MTLSBA achieves improvement in the fuel costs of 2.927% and 3.041% and reduction in the emissions of 40.156% and 40.050% compared to NSGA-II and SPEA 2, respectively.

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“…In this phase learners interact with one another randomly to enhance their knowledge (Akbari et al, 2021). This phase involves following steps: Choose two learners randomly X i and X j where i 6 ¼ j.…”

Section: Teaching Phasementioning

confidence: 99%

Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis

2022

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Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The Teaching Learning Based Optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.

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Optimal Power Flow via Teaching-Learning-Studying-Based Optimization Algorithm

Cited by 36 publications

References 98 publications

Mother Optimization Algorithm: A New Human-Based Metaheuristic Approach for Solving Engineering Optimization

Mother Optimization Algorithm: A New Human-Based Metaheuristic Approach for Solving Engineering Optimization

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis

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