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Multifactorial teaching-learning-based optimization with the diversity and triangle cooperation mechanism

APPLIED INTELLIGENCE 2022年第14期52卷 16512-16531页

作者： Li, Wei Fan, Yaochi Wang, Lei Jiang, Qiaoyong Xu, Qingzheng Xian Univ Technol Sch Comp Sci & Engn Xian Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian Peoples R China Natl Univ Def Technol Coll Informat & Commun Xian Peoples R China

Multifactorial optimization (MFO) is a newly developed optimization framework that can be embedded with an evolutionary algorithm to solve multiple optimization tasks simultaneously. To further explore the generality of the MFO framework, this paper first attempts to use teaching-learning-based optimization as a base optimizer for multiple optimization tasks (MFTLBO). However, the quality of the solution obtained by MFTLBO is not satisfactory because of premature convergence. Therefore, an MFTLBO variant named multifactorial teaching-learning-based optimization with the diversity and triangle cooperation mechanism (DTMFTLBO) is proposed to reduce premature convergence. The diversity indicator is used to dynamically adjust the exploration and exploitation. The cooperative teaching strategy based on weight is designed to acquire evolution direction information, which can guide the population toward more promising solution regions. The triangle cooperation strategy is employed to promote the knowledge transfer between different tasks in the learner phase, which can overcome premature convergence. To verify the efficiency of the proposed DTMFTLBO, numerical studies have been conducted with the 9 commonly used single objective benchmark problems of MTO. Experimental results show that the average excellent rate of the DTMFTLBO for 18 tasks is 85.80%, which confirms the competitiveness of our proposed algorithm compared with eight state-of-the-art multitask optimization algorithms.

关键词： Multifactorial optimization teaching-learning-based optimization Diversity indicator Triangle cooperation mechanism

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Hybrid teaching-learning-based optimization for solving engineering and mathematical problems

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第9期44卷 1-31页

作者： Dastan, Mohammadhossein Shojaee, Saeed Hamzehei-Javaran, Saleh Goodarzimehr, Vahid Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

In this work, a new and effective algorithm called hybrid teaching-learning-based optimization (TLBO) and charged system search (CSS) algorithms (HTC) are proposed to solve engineering and mathematical problems. The CSS is inspired by Coulomb and Gauss's electrostatic laws of physics as well as the Newtonian mechanic laws of motion. The TLBO is inspired by the interaction between teacher and student in a classroom. Usually, the TLBO gets trapped in the local optimal due to the lack of a system for measuring the distance between the student and the optimal point. In order to solve this problem, the CSS algorithm, which is based on the electrical physics laws, is utilized. In the CSS algorithm, each factor is stored under the influence of the best local and global positions, and it is used in subsequent iterations as the possible optimal answers. In fact, this leads to a better balance between exploration and exploitation. In order to validate the proposed method, the CEC2021 and CEC2005 mathematical functions are optimized. Additionally, to show the applicability of the proposed algorithm and to evaluate its performance and convergence rate, several benchmark truss structures are optimized. The weight of the structural elements is taken into account as the objective function, which is optimized under displacement and stress constraints. The results of the proposed algorithm are compared with some other well-known meta-heuristic methods. The results show that the hybrid HTC algorithm improved the convergence rate and quickly obtained the optimal and desired design. The hybrid HTC algorithm can be adapted to solve other complex mathematical and optimization problems.

关键词： Meta-heuristic algorithms optimization of structures Charged system search algorithm teaching-learning-based optimization Hybrid charged system search based on TLBO algorithm

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teaching-learning-based optimization (TLBO) in Hybridized with Fuzzy Inference System Estimating Heating Loads

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ENERGIES 2022年第21期15卷 8289页

作者： Foong, Loke Kok Binh Nguyen Le Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Sch Engn & Technol Da Nang 550000 Vietnam

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads' analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching-learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro-fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. based on the value of R-2 calculated for both testing and training (0.98933, 0.98931), teaching-learning-based optimization can help the adaptive neuro-fuzzy interface system to enhance the results' correlation. Also, the high R-2 value means that the model has high accuracy in the HL prediction. In addition, according to the estimated RMSE, the training error of TLBO-ANFIS in the testing and training stages was 0.07794 and 0.07984, respectively. The low value of root-mean-square error (RMSE) indicates that the TLBO-ANFIS method acts favorably in the estimation of the heating load for residential buildings.

关键词： adaptive neuro-fuzzy interface system residential buildings metaheuristic heating-load teaching-learning-based optimization

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A fast and efficient teaching-learning-based optimization algorithm for large-scale multi-objective optimization problems

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS- COMPUTER SYSTEMS THEORY 2023年第3期8卷 160-177页

作者： Aouadj, Wafa Seghir, Rachid Batna 2 Univ LaST Lab Fesdis Algeria Batna 2 Univ LaST Lab Fesdis Batna Algeria

Multi-objective optimization problems with large-scale decision variables, known as LSMOPs, are characterized by their large-scale search space and multiple conflicting objectives to be optimized. They have been involved in many emergent real-world applications, such as feature and instance selection, data clustering, adversarial attack, vehicle routing problem, and others. In this work, we propose a new teaching-learning-based optimization algorithm to effectively tackle large-scale multi-objective optimization problems. The proposed approach ensures both the diversity of solutions, requested for high dimensionality of LSMOPs, and the balance between the exploitation and exploration during the optimization process. The experimental studies conducted on 54 instances of a large-scale multi-objective optimization problems test suite and the comparisons against well-known and state-of-the-art algorithms have shown the superiority of the proposed algorithm in terms of Pareto front approximation and computation time by using limited evaluation budget.

关键词： teaching-learning-based optimization multi-objective optimization large-scale optimization >

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Competitive teaching-learning-based optimization for multimodal optimization problems

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SOFT COMPUTING 2022年第19期26卷 10163-10186页

作者： Chi, Aining Ma, Maode Zhang, Yiying Jin, Zhigang Taizhou Univ Coll Informat Engn Taizhou 225300 Peoples R China Qatar Univ Coll Engn Doha 2713 Qatar Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

teaching-learning-based optimization is one of the latest metaheuristic algorithms. TLBO has a simple framework and good global search ability. In addition, TLBO only needs population size and terminal condition for performing search tasks. Given these advantages, TLBO has been used widely since it was proposed. However, TLBO may fall into local optimal solutions in solving complex multimodal optimization problems. This paper reports an improved TLBO, namely competitive teaching-learning-based optimization, for solving multimodal optimization problems. In CTLBO, population is first divided into outstanding group and common group by the designed competitive mechanism. Then outstanding group is updated by the learning strategies of TLBO and common group is guided by outstanding group. In addition, a mutation operator for the optimal individual is introduced to increase the ability of CTLBO to escape from the local optima. The performance of CTLBO is investigated by 45 benchmark test functions from CEC 2014 and CEC 2015 test suites and three challenging real-world engineering problems. Experimental results show that CTLBO is more reliable and efficient on most test cases than TLBO and the other compared algorithms. This supports the effectiveness of the improved strategies and the superiority of CTLBO in solving multimodal optimization problems.

关键词： teaching-learning-based optimization Engineering optimization Multimodal problems Competitive learning mechanism

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teaching-learning-based optimization Enhanced With Multiobjective Sorting based and Cooperative learning

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IEEE ACCESS 2020年 8卷 65923-65937页

作者： Li, Wei Fan, Yaochi Xu, Qingzheng Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China Natl Univ Def Technol Coll Informat & Commun Xian 710106 Peoples R China

teaching-learning-based optimization (TLBO) algorithm has been shown to be an effective optimization algorithm. However, it is easily trapped into local optima when the global optimal solution of the function to be optimized is at the original dot or around the original dot. This paper presents a novel TLBO variant by incorporating multiobjective sorting-based mechanism and cooperative learning strategy to alleviate this problem. Taking advantages of multiobjective optimization in maintaining good population diversity, several teachers are selected based on non-dominated sorting, so as to guide learners to learn more effectively. In addition, the proposed algorithm adopts cooperative learning, including learning within and between groups, to improve the search ability of the algorithm. Experimental and statistical analyses are performed on CEC2014 benchmark functions. The experimental results demonstrate the effectiveness of the proposed algorithm in comparison with other variants of TLBO and other state-of-the-art optimization algorithms.

关键词： teaching-learning-based optimization non-dominated sorting cooperative learning optimization algorithm learning strategy

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Optimal equivalent circuit models for photovoltaic cells and modules using multi-source guided teaching-learning-based optimization

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AIN SHAMS ENGINEERING JOURNAL 2024年第11期15卷

作者： Li, Yasha Xiong, Guojiang Mirjalili, Seyedali Mohamed, Ali Wagdy Guizhou Univ Coll Elect Engn Room 402Jiaxiu South Rd Guiyang 550025 Guizhou Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Univ Sci & Technol Sch Business Zewail City Sci & Technol Giza 12588 Egypt

The complexity of equivalent circuit models of photovoltaic cells and modules poses a difficult task to the parameter extraction methods. teaching-learning-based optimization (TLBO) is a potent metaheuristic-based parameter extraction method, but it suffers from insufficient precision and low dependability. This study presented a multi-source guided TLBO through improving its two optimization phases. A multi-source guided approach with one-to-one and step-by-step teaching strategies was designed to guide different learners in the teacher phase. Besides, different strategies based on multiple learners were introduced for learners with different knowledge reserves to strengthen information exchanging. With the improvements, it is advantageous to lessen the likelihood of hitting a local optimum and thereby the global convergence can be accelerated. The resultant method was verified on single diode model, double diode model, and three additional modules. The findings demonstrate that it obtained better solutions in precision and dependability, and stood out from the crowd of algorithms.

关键词： Metaheuristic algorithm Parameter extraction Photovoltaic cell teaching-learning-based optimization

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teaching-learning-based optimization of Neural Networks for Water Supply Pipe Condition Prediction

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WATER 2021年第24期13卷 3546页

作者： Elshaboury, Nehal Abdelkader, Eslam Mohammed Al-Sakkaf, Abobakr Alfalah, Ghasan Housing & Bldg Natl Res Ctr Construct & Project Management Res Inst Giza 12311 Egypt Cairo Univ Fac Engn Struct Engn Dept Giza 12613 Egypt Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada Hadhramout Univ Coll Engn & Petr Dept Architecture & Environm Planning Mukalla 50512 Yemen King Saud Univ Coll Architecture & Planning Dept Architecture & Bldg Sci Riyadh 11421 Saudi Arabia

The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery's water distribution networks, in Egypt. The developed models involve training the Elman neural network (ENN) and feed-forward neural network (FFNN) coupled with particle swarm optimization (PSO), genetic algorithms (GA), the sine cosine algorithm (SCA), and the teaching-learning-based optimization (TLBO) algorithm. For the Shattora network, the inputs to these models are pipe characteristics such as length, wall thickness, diameter, material, lining and coating, surface type, traffic distribution, cathodic protection, flow velocity, and c-factor. For the Shaker Al-Bahery network, the data gathered include length, material, age, diameter, depth, and wall thickness. Three assessment criteria are used to evaluate the suggested machine learning models, namely index of agreement (IOA), correlation coefficient (R), and root mean squared error (RMSE). The results reveal that coupling FFNN with the TLBO algorithm outperforms other prediction models. Therefore, the FFNN-TLBO model can be a valuable tool for simulating the water network pipe condition. This study could help the water municipality allocate the available budget effectively and plan the required maintenance and rehabilitation actions.

关键词： teaching-learning-based optimization optimized neural network machine learning optimization algorithm condition prediction

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A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive problems

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Zhang, Jian Li, Muxi Yue, Xinxin Wang, Xiaojuan Shi, Maolin Northwestern Polytech Univ Ocean Inst Taicang 215400 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China Anhui Sci & Technol Univ Coll Architecture Bengbu 233030 Peoples R China Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China Jiangsu Univ Sch Agr Engn Zhenjiang 212013 Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving computationally expensive optimization problems. However, when tackling high-dimensional expensive problems, a large number of exact function evaluations (FEs) need to be consumed for existing SAEAs to achieve an acceptable solution. In this paper, a hierarchical surrogate assisted optimization algorithm (HSAOA) using teaching-learning-based optimization and differential evolution is proposed for solving high-dimensional expensive problems with a relatively small number of exact FEs. To keep a balance between global exploration and local exploitation, a hierarchical surrogate framework with hybrid evolutionary algorithms is devised. In the global search phase, a radial basis function surrogate is utilized to assist the teaching-learning-based optimization in locating the promising sub-regions. In the local search phase, a novel dynamic ensemble of surrogates is proposed to assist the differential evolution in speeding up the convergence process. Eight test functions with 10 to 100 dimensions and a spatial truss design problem are employed to compare the proposed method with several state-of-the-art SAEAs. The results show that the proposed HSAOA is superior to the comparison algorithms for solving expensive optimization problems, and needs a much smaller number of exact FEs than other competing SAEAs to produce competitive or even better results for high-dimensional expensive problems.

关键词： Hybrid optimization algorithm Differential evolution teaching-learning-based optimization Surrogate model High -dimensional expensive problems

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A teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Yu, Xiaobing Zhang, Wen Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteorol Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Res Inst Risk Governance & Emergency Decis Making Sch Management Sci & Engn Nanjing 210044 Peoples R China

As the global demand for renewable energy continues to rise, wind energy has received widespread attention as an eco-friendly energy source. Wind power generation is regarded as one of the key means to reduce carbon emissions and achieve sustainable development. Usually, a mass of turbines works together to produce electricity in a wind farm. However, downstream turbines will inevitably be influenced by the wake generated by upstream turbines, resulting in unused wind energy being lost. To reduce the negative effects of the wake, maximization of wind farm output power, and minimization of wind farm cost, a teaching-learning-based optimization algorithm with reinforcement learning is proposed in this paper. The improvements of the proposed algorithm mainly include the following three points: i) the original serial structure of the algorithm is changed to a parallel structure to accelerate the convergence and improve the efficiency of the algorithm. ii) the parameter F, which is adjusted by RL, is proposed to adjust the selection of the updating phase due to the design of a parallel structure. iii) in the modified learner phase, an individual is added to participate in the update, and a selection probability is proposed to improve the ability of the algorithm to retain the information of superior individuals. To study the performance of the modified algorithm, it was first tested against 10 other advanced algorithms on a benchmark testing suite. They then ran numerical experiments on four hypothetical wind farm cases under two simulated wind conditions. Finally, the superiority of improved algorithm over others and the effectiveness of addressing wind farm layout problem are demonstrated by experimental results.

关键词： teaching-learning-based optimization Reinforcement learning Wind farm layout optimization Q-learning algorithm Meta-heuristic algorithm

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