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A Hybrid Two-Stage teaching-learning-based optimization Algorithm for Feature Selection in Bioinformatics

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 1746-1760页

作者： Kang, Yan Wang, Haining Pu, Bin Tao, Liu Chen, Jianguo Yu, Philip S. Yunnan Univ Natl Pilot Sch Software Kunming 650106 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou 510275 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA Tsinghua Univ Inst Data Sci Beijing 100084 Peoples R China

The "curse of dimensionality" brings new challenges to the feature selection (FS) problem, especially in bioinformatics filed. In this paper, we propose a hybrid Two-Stage teaching-learning-based optimization (TS-TLBO) algorithm to improve the performance of bioinformatics data classification. In the selection reduction stage, potentially informative features, as well as noisy features, are selected to effectively reduce the search space. In the following comparative self-learning stage, the teacher and the worst student with self-learning evolve together based on the duality of the FS problems to enhance the exploitation capabilities. In addition, an opposition-based learning strategy is utilized to generate initial solutions to rapidly improve the quality of the solutions. We further develop a self-adaptive mutation mechanism to improve the search performance by dynamically adjusting the mutation rate according to the teacher's convergence ability. Moreover, we integrate a differential evolutionary method with TLBO to boost the exploration ability of our algorithm. We conduct comparative experiments on 31 public data sets with different data dimensions, including 7 bioinformatics datasets, and evaluate our TS-TLBO algorithm compared with 11 related methods. The experimental results show that the TS-TLBO algorithm obtains a good feature subset with better classification performance, and indicates its generality to the FS problems.

关键词： Feature selection teaching-learning-based optimization data classification hybrid search

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Improved teaching-learning-based optimization algorithm based on fusion difference mutation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 4643-4651页

作者： Liang, Shaohui Wei, Botao Xian Univ Sci & Technol Sch Sci Dept Math Xian 710054 Peoples R China

teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm.

关键词： teaching-learning-based optimization adaptive teaching factors the improved teaching stage learning stages differential mutation

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Minimizing cycle time and energy consumption for a multi-degree serial manipulator using teaching-learning-based optimization

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第5期45卷 1-12页

作者： Shinde, V. B. Pawar, P. J. Savitribai Phule Pune Univ KK Wagh Inst Engn Educ & Res Dept Prod Engn Pune 422003 Maharashtra India Savitribai Phule Pune Univ Amrutvahini Coll Engn Dept Prod Engn Pune 422608 Maharashtra India

Multiple inverse kinematic solutions are obtained for a multi-degree serial manipulator. Each solution provides a different cycle time and energy consumed to perform a given task in a particular sequence. Improper selection of inverse kinematic solution in a robotic assembly line may lead to an increase in not only overall cycle time but also energy consumption. Therefore, it is necessary to optimize the robot's performance with respect to cycle time and energy consumption simultaneously. These two objectives usually conflict due to joint motors' different operational speeds and energy consumption rates. Hence, in this work, an attempt is made to simultaneously optimize both cycle time and energy consumption by minimizing the differences in joint angles of the robot manipulator while moving from one position to another in applications involving multi-point movement of the robot end effector. The proposed approach is demonstrated by considering an example of a commonly used six-degree-of-freedom Kuka KR5-A robot manipulator. It can be extended efficiently for any other robot manipulator. A relatively new algorithm known as teaching-learning-based optimization (TLBO) algorithm is employed to solve this problem. It is observed that the results obtained by TLBO show a significant reduction of about 20% and 10% for cycle time, while 3% and 15% for energy consumption over the results obtained by genetic algorithm (GA) and sequential quadratic programming (SQP), respectively.

关键词： teaching-learning-based optimization Cycle time Energy consumption Multi-degree serial manipulator Inverse robot kinematics

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Effective hybridization of JAYA and teaching-learning-based optimization algorithms for numerical function optimization

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SOFT COMPUTING 2023年第14期27卷 9673-9691页

作者： Gholami, Jafar Nia, Fariba Abbasi Sanatifar, Maryam Zawbaa, Hossam M. Islamic Azad Univ Dept Comp Engn Kermanshah Sci & Res Branch Kermanshah Iran Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan

The JAYA is classified as the state-of-the-art population-oriented algorithm for the optimization of diverse problems, both discrete and continuous. The concept behind this algorithm is to present a solution by means of the best and worst individuals in the population. On the other hand, teaching-learning-based optimization algorithm cooperation of a teacher on students' learning process. Due to each one having some benefits and drawbacks, combining those leads to better exploring the problem. Consequently, this investigation exploits the hybridization of both mentioned algorithms, and a novel algorithm is made named H-JTLBO (hybridization of JAYA and teaching learning-based optimization). The proposed approach is then evaluated using different test functions used frequently in the literate. Finally, the results of such functions are compared with other optimization algorithms which have recently been introduced in the literature, such as Sine Cosine Algorithm (SCA), Grasshopper optimization Algorithm (GOA), Moth-flame optimization (MFO), and JAYA algorithm. In addition, the statistical test is used to evaluate the proposed method. Through the results, H-JTLBO outperforms all mentioned algorithms in terms of convergence and solution quality.

关键词： JAYA teaching-learning-based optimization Hybridization of JAYA and teaching-learning-based optimization algorithms Convergence

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Hybrid teaching-learning-based optimization for Workflow Scheduling in Cloud Environment

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IEEE ACCESS 2023年 11卷 100755-100768页

作者： He, Jieguang Liu, Xiaoli Guangdong Univ Petrochem Technol Coll Comp Sci Maoming 525011 Peoples R China

At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), teaching-learning-based optimization (TLBO), Opposition-based learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO's performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency.

关键词： Workflow scheduling cloud computing teaching-learning-based optimization opposition-based learning search boundary population information

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Group-individual multi-mode cooperative teaching-learning-based optimization algorithm and an industrial engineering application

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 5437-5465页

作者： Chen, Zhixiang Sun Yat Sen Univ Sch Business Dept Management Sci 135 West Xingang Rd Guangzhou 510275 Peoples R China

This paper modifies the original teaching-learning-based optimization (TLBO) algorithm to present a novel Group-Individual Multi-Mode Cooperative teaching-learning-based optimization (CTLBO) algorithm. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher-learner cooperation strategies in teaching and learning processes. In the preparation phase, teacher-learner interaction and teacher self-learning mechanism are applied. In the teaching phase, class-teaching and performance-based group-teaching operators are applied. In the learning phase, neighbor learning, student self-learning and team-learning strategies are mixed together to form three operators. Experiments indicate that CTLBO has significant improvement in accuracy and convergence ability compared with original TLBO in solving large scale problems and outperforms other compared variants of TLBO in literature and other 9 meta-heuristic algorithms. A large-scale industrial engineering problem-warehouse materials inventory optimization problem is taken as application case, comparison results show that CTLBO can effectively solve the large-scale real problem with 1000 decision variables, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO, revealing that CTLBO can far outperform other algorithms. CTLBO is an excellent algorithm for solving large scale complex optimization issues.

关键词： teaching-learning-based optimization group-individual multi-mode cooperation performance-based group teaching teacher self-learning team learning

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Multi-population cooperative teaching-learning-based optimization for nonlinear equation systems

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COMPLEX & INTELLIGENT SYSTEMS 2023年第6期9卷 6593-6609页

作者： Liao, Zuowen Li, Shuijia Gong, Wenyin Gu, Qiong Beibu Gulf Univ Beibu Gulf Ocean Dev Res Ctr Qinzhou 535000 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Peoples R China Beibu Gulf Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Beibu Gulf Offshore Engn Equipment & Techn Qinzhou 535011 Peoples R China

Solving nonlinear equation systems (NESs) requires locating different roots in one run. To effectively deal with NESs, a multi-population cooperative teaching-learning-based optimization, named MCTLBO, is presented. The innovations of MCTLBO are as follows: (i) two niching technique (crowding and improved speciation) are integrated into the algorithm to enhance population diversity;(ii) an adaptive selection scheme is proposed to select the learning rules in the teaching phase;(iii) the new learning rules based on experience learning are developed to promote the search efficiency in the teaching and learning phases. MCTLBO was tested on 30 classical problems and the experimental results show that MCTLBO has better root finding performance than other algorithms. In addition, MCTLBO achieves competitive results in eighteen new test sets.

关键词： Nonlinear equation systems multi-population cooperation teaching-learning-based optimization niching technique adaptive selection scheme

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Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching-learning-based optimization

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第SUPPL 1期14卷 353-373页

作者： Pinninti, Swarupa Sura, Srinivasa Rao GITAM Deemed be Univ Dept Elect Elect & Commun Engn Visakhapatnam India

Distributed generators (DGs) which may be both renewable energy sources (RES) or conventional fossil fueled generators must be optimally scheduled so as to reduce the generation cost of a power network at the end of the day. Adequate attention must also be given to expand the utilization of RES not only because they are abundant in nature but also because they are clean sources of energy supply. This paper utilizes a robust and efficient teaching-learning-based optimization (TLBO) to optimally schedule the DGs of four dynamic systems so as to reduce their active power generation cost. The cost effective fitness functions of the subject test systems are both linear and non-linear in nature. RES like photovoltaic (PV) systems and wind were prioritized to share the load demand and load profiles of various system were studied. By instigating a savings of 9.36% with 99.9% efficiency, TLBO clearly outperformed a long list of algorithms available in literatures in minimizing the generation cost of the test systems. Various statistical parameters, non-parametric statistical analysis and computational time also points towards the robustness of TLBO in handling any dimensional test system.

关键词： Dynamic economic dispatch VPE or valve point effect Wind power optimization algorithms teaching-learning-based optimization

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Research on the Mixed H2/H∞ State Feedback Control Parameter Problem based on teaching-learning-based optimization Algorithm 36

Research on the Mixed H2/H∞ State Feedback Control Paramete...

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

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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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