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36th Chinese Control and Decision Conference (CCDC)

作者： Zheng, Liuke Wang, Weihong Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Due to the complex principles and design rules of robust controllers, their design has always been a rather difficult problem. To address the difficulty in selecting weight matrix parameters for the reference output in the design of a hybrid H-2/H-infinity state feedback controller in robust control problems, a parameter optimization scheme based on teaching-learning-based optimization algorithm (TLBO) is raised in this paper. Firstly, the convex optimization solution problem equivalent to the mixed H-2/H-infinity state feedback controller with closed-loop pole region constraint is described, which leads to the research problem of this paper, that is, the selection of weight matrix parameters. Then, the weight matrix is parameterized, and the parameters are optimized and solved based on TLBO to optimize the performance of the controller. Finally, simulation experiments are designed, and the results show that compared with the H-2 state feedback control and the H-infinity state feedback control, the hybrid H-2/H-infinity state feedback controller designed in this paper behaves better in terms of suppressing external interference and initial disturbance. Moreover, the controller raised in this paper has good robustness to the perturbation of system parameters.

关键词： Hybrid H-2/H-infinity state feedback control pole constraints convex optimization teaching-learning-based optimization

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A teaching-learning-based optimization Algorithm for the Resource-Constrained Project Scheduling Problem 4th

A Teaching-Learning-Based Optimization Algorithm for the Res...

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Joshi, Dheeraj Mittal, M. L. Kumar, Manish Swami Keshvanand Inst Technol Management & Gramot Dept Mech Engn Jaipur Rajasthan India Malaviya Natl Inst Technol Dept Mech Engn Jaipur 302017 Rajasthan India

ISBN: (纸本)9789811307614;9789811307607

In this paper, a recently introduced population-based metaheuristic known as teaching-learning-based optimization algorithm (TLBO) is used to find solution for the resource-constrained project scheduling problem (RCPSP). The RCPSP is considered in its basic form, wherein activities are non-preemptive in nature and their execution is possible in a single mode only. The scheduling objective chosen is minimizing the makespan or total project duration. TLBO algorithm in its original form employs two phases, namely the teacher phase and the learner phase to reach a global optimum solution. In order to increase the exploitation and exploration capabilities of the basic TLBO the concepts of elitism and mutation as used in genetic algorithm (GA) have been introduced. An activity list representation is used to represent a learner (solution) and to derive the schedule from this activity list a serial schedule generation scheme is used as a decoding procedure. It has been found after the computational experiment on a test problem from the literature that proposed TLBO gives results competitive to the other metaheuristics like GA and particle swarm optimization (PSO). In addition, it offers the inherent advantage of less parameter to tune and can, therefore, be used as an effective method to solve RCPSP and its other variants as well.

关键词： Resource-constrained project scheduling Metaheuristic teaching-learning-based optimization

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Power System Stability Enhancement with teaching-learning-based optimization 26

Power System Stability Enhancement with Teaching-Learning-Ba...

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26th Iranian Conference on Electrical Engineering (ICEE)

作者： Naghizadeh, Ramezan Ali Azimi, Seyed Mohammad Hamedan Univ Technol Elect Engn Dept Hamadan *** Iran

ISBN: (纸本)9781538649169

Modern electric power systems are complex, interconnected and susceptible to low frequency oscillations. Power system stabilizers (PSSs) are used in synchronous generators to damp out these oscillations and prevent instability in the case of different possible disturbances. This paper proposes application of an optimal approach for two different controller types to improve PSS performance and enhance power system stability. Three types of controllers including Lead-Lag, fuzzy, and sliding mode are considered for PSS of a single machine infinite bus power system. The parameters of fuzzy and sliding mode controllers are optimized using teaching-learning-based optimization (TLBO) algorithm to minimize oscillations. Simulation results show that TLBO algorithm is capable of proper tuning of different PSS controller strategies to not only maintain stability, but also to reduce oscillations. In addition, performance of different controllers is evaluated for the same specific disturbance. Significant improvement is observed in fuzzy logic controller. In addition, it has been found that SMC optimized by TLBO exhibit best results compared to other controllers.

关键词： component Power System Stabilizer Fuzzy Logic Control Sliding Mode Control teaching-learning-based optimization

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A teaching-learning-based optimization with Uniform Design for Solving Constrained optimization Problems 13

A Teaching-Learning-based Optimization with Uniform Design f...

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Jia, Liping Li, Zhonghua Leshan Normal Univ Coll Math & Informat Sci Leshan 614000 Peoples R China Leshan Normal Univ Coll Comp Sci Leshan 614000 Peoples R China

ISBN: (纸本)9781538648223

As a newly developed population-based metaheuristic algorithm, teaching-learning-based optimization (TBLO) has been gained extensively attention since it was proposed in 2011. It has been applied to many optimal problems and a lot of algorithms have also been designed to solve these real-world problems. In this paper, TBLO with uniform design is proposed for solving constrained optimization problems. The performance of the proposed algorithm is checked by experiments with two type of different benchmark problems under the criteria of best, mean, worst, function evaluations and ratio of feasible search space. Compared results are given to illustrate the efficiency of the proposed algorithm.

关键词： teaching-learning-based optimization constrained optimization problems uniform design

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Optimized Path Planning for Three-Wheeled Autonomous Robot Using teaching-learning-based optimization Technique 1

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International Conference on Advances in Materials and Manufacturing Engineering (ICAMME)

作者： Kashyap, Abhishek K. Pandey, Anish KIIT Deemed Be Univ Sch Mech Engn Bhubaneswar 751024 India

ISBN: (数字)9789811513077

ISBN: (纸本)9789811513077;9789811513060

Path planning is a leading topic in the field of the wheeled robot (WR). Three basic characteristic path planning should have when the WR is traveling toward the goal: (1) obtain information about the given working space conditions, (2) location of itself, and (3) optimize the decision to reach the target. The current research paper focuses on obtaining an efficient and robust technique to guide the WR. teaching-learning-based optimization technique is the centerpiece of the present research work. Fitness function has been presented to optimize the path planning and reaching target. Parameters selected for the proposed technique are (1) distance between robot, start point, goal, and obstacles and (2) turning angle while avoiding obstacles. The technique is examined in various environments with the different level of difficulties. The WR efficiently reaches the target by avoiding collision with the obstacles. In addition, the proposed technique is compared with the previously used technique. The obtained simulated results justified that the teaching-learning-based optimization technique selects better travel path and have shorter travel length.

关键词： Wheeled robot Path planning Obstacle avoidance teaching-learning-based optimization Static obstacles

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optimization of job shop scheduling problems using teaching-learning-based optimization algorithm

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OPSEARCH 2014年第4期51卷 545-561页

作者： Keesari, H. S. Rao, R. V. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

A job shop scheduling problem is one of the most difficult NP hard combinatorial optimization problems. In a job shop scheduling problem (JSSP), there are n jobs that should be processed on m machines. Each job consists of a predetermined sequence of task operations, each of which needs to be processed without interruption for a given period of time on a given machine. Tasks of the same job cannot be processed concurrently. In recent years, optimization algorithms such as simulated annealing (SA), genetic algorithm (GA), tabu search (TS), ant colony optimization (ACO) particle swarm optimization (PSO) and artificial bee colony (ABC) have played a significant role in solving small-scale job shop scheduling problems. However, when the problem size grows, metaheuristic algorithms usually take excessive time to converge. In this study, a recently developed teaching-learning- based optimization (TLBO) method is proposed to solve the job shop scheduling problems to minimize the makespan. The proposed algorithm is tested on 58 job shop scheduling bench mark problems from OR Library and results are compared with the results obtained by using the other algorithms. It is concluded that the TLBO algorithm can be effectively used for job shop scheduling problems.

关键词： Job shop scheduling Benchmark problems teaching-learning-based optimization

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An improved teaching-learning-based optimization 37

An improved teaching-learning-based optimization

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37th Chinese Control Conference (CCC)

作者： Hou, Jie Ren, Ziwu Lu, Pan Zhang, Kunting Soochow Univ Robot & Microsyst Ctr Jiangsu Prov Key Lab Adv Robot Suzhou 215021 Peoples R China Soochow Univ Collaborat Innovat Ctr Suzhou Nano Sci & Technol Suzhou 215021 Peoples R China

ISBN: (纸本)9789881563958

teaching-learning-based optimization(TLBO) is a new proposed heuristic algorithm for optimization applications in recent years. In this paper, an improved TLBO algorithm (ITLBO) is presented. In the teacher phase, the second-teaching strategy and self-exploration study of teacher are introduced to improve the convergence speed. And the improved learner phase can ensure the diversity of the population to avoid the possibility of falling into a local optimum. Meanwhile, second-teaching strategy and the improved learner phase enable the algorithm to use fine local search and improve the precision. To assess the performance of ITLBO algorithm, experiments are implemented on 8 classical benchmark functions. The result show that ITLBO algorithm is an effective approach.

关键词： teaching-learning-based optimization Second-teaching strategy Teacher's self-exploration study

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ABC-TLBO: A Hybrid Algorithm based on Artificial Bee Colony and teaching-learning-based optimization 37

ABC-TLBO: A Hybrid Algorithm Based on Artificial Bee Colony ...

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37th Chinese Control Conference (CCC)

作者： Zhang, Mei Pan, Yuchong Zhu, Jinhui Chen, Guangsen South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9789881563958

Artificial bee colony (ABC) is shown to be effective in solving optimization problems because of its good exploration capability. However it suffers from poor exploitation because onlooker bees use the same searching method as employed bees. In order to remedy the limited exploitation capability of ABC and achieve a better balance between exploration and exploitation, searching strategies for employed bees and onlooker bees are redesigned respectively and a novel algorithm called ABC-TLBO is proposed. It inherits the framework of basic ABC, and meanwhile an individualized TLBO based on teaching-learning-based optimization (TLBO) is embedded in onlooker bee phase to improve the exploitation capability of the presented algorithm. Moreover, a new searching strategy for employed bee phase which combines direction guiding searching and differential evolution crossover searching is applied to increase the population diversity and accelerate the convergence. The proposed ABC-TLBO algorithm is validated on benchmark functions and it is compared with ABC-based, TLBO-based and nature-inspired algorithms. Experimental results reveal that ABC-TLBO algorithm is an efficient, fast converging optimization method for solving global optimization problems.

关键词： Artificial bee colony teaching-learning-based optimization global numerical optimization

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Surrogate-assisted teaching-learning-based optimization for high-dimensional and computationally expensive problems

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APPLIED SOFT COMPUTING 2021年 99卷 106934-106934页

作者： Dong, Huachao Wang, Peng Yu, Xinkai Song, Baowei Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

In this work, a surrogate-assisted teaching-learning-based optimization algorithm is presented for high-dimensional and computationally expensive black-box optimization problems. In the presented method, a two-phase searching framework is proposed to realize the exploitation of surrogates and the metaheuristic exploration. Specifically, radial basis functions are used to build the dynamically updated surrogate models. Moreover, a surrogate-assisted knowledge mining strategy is proposed to sufficiently collect valuable information in each cycle. In this strategy, multiple groups of promising points are selected from the expensive sample set to construct the subspaces and local surrogate models, from which the most potential points are captured for the subsequent updates and optimization. At the same time, a population composed of the present best expensive samples carries out the teaching/learning-based search, accelerating local convergence and promoting global exploration. In the surrogate-assisted teaching phase, both of individual and mean behaviors are considered to make learners go close to the teacher efficiently. In the surrogate-assisted learning phase, a more extensive search range is defined to improve sampling diversity. The cooperation of the surrogate-assisted knowledge mining and the modified teaching-learning-based exploration makes the new method have excellent performance on 21 benchmark cases with 30-100 design variables. Furthermore, this method is used for the shape optimization of a blended-wing-body underwater glider, and gets impressive results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Radial Basis Function Computationally expensive High-dimensional teaching-learning-based optimization

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Hybrid teaching-learning-based optimization and neural network algorithm for engineering design optimization problems

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KNOWLEDGE-based SYSTEMS 2020年 187卷 104836-000页

作者： Zhang, Yiying Jin, Zhigang Chen, Ye Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Hainan Univ Coll Appl Sci & Technol Haikou 570228 Hainan Peoples R China

Neural network algorithm (NNA) is one of the newest meta-heuristic algorithms, which is inspired by biological nervous systems and artificial neural networks. Benefiting from the unique structure of artificial neural networks, NNA has good global search ability. However, slow convergence is its drawback that restricts its practical application. teaching-learning-based optimization (TLBO) is an algorithm without any effort for fine tuning initial parameters, which has fast convergence speed while it is easy to fall into local optimum in solving complex global optimization problems. Considering the features of NNA and TLBO, an effective hybrid method based on TLBO and NNA, named TLNNA, is proposed for solving engineering optimization problems. The performance of TLNNA for 30 well-known unconstrained benchmark functions and 4 challenging engineering optimization problems is examined and the optimization results are compared with other competitive meta-heuristic algorithms. Such comparisons suggest that TLNNA has not only good global search ability of NNA but also fast convergence speed of TLBO and is more successful for most test problems in terms of solution quality and computational efficiency. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Neural network algorithm Artificial neural networks teaching-learning-based optimization Engineering optimization

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