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MTLBO: A Multi-objective Multi-course teaching-learning-based optimization Algorithm

JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2018年第3期21卷 331-342页

作者： Lin, Wenwen Wang, Lei Tian, Guangdong Zhang, Yuejun Ningbo Univ Fac Mech Engn & Mech Ningbo 315211 Zhejiang Peoples R China Jilin Univ Transportat Coll Changchun 130022 Jilin Peoples R China

This paper presents a multi-objective evolutionary algorithm, which is called multi-course teaching-learning-based optimization algorithm. It consists of four phases: teacher, leaner, teacher training, and exchange student phases. Three novel modifications are introduced into the basic teaching-learning-based optimization algorithm. It helps diversify evolutionary population, enhance stochastic search ability, and avoid premature convergence. Eight widely used benchmark problems are employed in order to investigate the performance of the proposed algorithm. The experimental results indicate that it finds better Pareto optimal solutions than some other state-of-the-art algorithms for the majority of problems.

关键词： teaching-learning-based optimization Multi-objective optimization Evolutionary Algorithm Heuristic Algorithm

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Modified teaching-learning-based optimization for Combined Heat and Power Economic Dispatch

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2017年第5期18卷 1-22页

作者： Pattanaik, Jagat Kishore Basu, Mousumi Dash, Deba Prasad Jadavpur Univ Power Engn Dept Sect 3 Kolkata 700098 W Bengal India Orissa Engn Coll Elect Engn Dept Bhubaneswar Orissa India

This paper develops and suggests modified teaching-learning-based optimization to solve non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. The valve-point loading and forbidden working regions of conventional thermal generator are taken into account. Modified teaching-learning-based optimization (MTLBO) introduces Gaussian random variables in the 'Teacher phase' and 'Learner phase' which perks up search efficiency and assurances a high probability of acquiring the global optimum without considerably impairing the convergence speed and the simplicity of the structure of teaching-learning-based optimization (TLBO). The efficacy of the suggested MTLBO technique has been confirmed on three test systems and 15 benchmark functions. Test results of the suggested technique have been matched up to those achieved by other evolutionary techniques. It has been observed from the comparison that the suggested MTLBO technique has the ability to endow with superior solution.

关键词： modified teaching-learning-based optimization teaching-learning-based optimization combined heat and power economic dispatch cogeneration forbidden working region valve-point loading

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Quantum teaching-learning-based optimization algorithm for sizing optimization of skeletal structures with discrete variables

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STRUCTURES 2021年 32卷 1798-1819页

作者： Kaveh, A. Kamalinejad, M. Hamedani, K. Biabani Arzani, H. Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran Shahid Rajaee Teacher Training Univ Dept Civil Engn Tehran Lavizan Iran

In terms of optimization algorithm improvement, researchers utilize some strategies to establish the right balance between local and global search phases to escape from a local minimum in some NP-hard problems. The approach of quantum-behaved particles, as a novel strategy, uses uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates' positions. New formulation defines the local attractors as probable solutions between the best solution and the others to explore all solution space domain. Also, mentioned formulation employs the difference between the average solution and other ones as a new step size. Both local attractors and new step sizes guarantee diversification besides the inherent intensification. In the present paper, the teacher phase of the teaching-learning-based optimization algorithm (TLBO), which has been introduced recently, is improved using the formulation obtained from solving the time-independent Schrodinger equation to predict the probable positions of optimal solutions. The results show that QTLBO, an acronym for the Quantum teaching- learning- based optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two planner rigid frames and two trusses with strength and serviceability constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.

关键词： Quantum-inspired evolutionary algorithm teaching-learning-based optimization Population-based algorithm Rigid frames Quantum behaved particles Quantum teacher

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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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Fuzzy adaptive teaching-learning-based optimization for global numerical optimization

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NEURAL COMPUTING & APPLICATIONS 2018年第2期29卷 309-327页

作者： Cheng, Min-Yuan Prayogo, Doddy Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43Sec 4Keelung Rd Taipei 106 Taiwan Petra Christian Univ Dept Civil Engn Jalan Siwalankerto 121-131 Surabaya 60236 Indonesia

teaching-learning-based optimization (TLBO) is one of the latest metaheuristic algorithms being used to solve global optimization problems over continuous search space. Researchers have proposed few variants of TLBO to improve the performance of the basic TLBO algorithm. This paper presents a new variant of TLBO called fuzzy adaptive teaching-learning-based optimization (FATLBO) for numerical global optimization. We propose three new modifications to the basic scheme of TLBO in order to improve its searching capability. These modifications consist, namely of a status monitor, fuzzy adaptive teaching-learning strategies, and a remedial operator. The performance of FATLBO is investigated on four experimental sets comprising complex benchmark functions in various dimensions and compared with well-known optimization methods. based on the results, we conclude that FATLBO is able to deliver excellence and competitive performance for global optimization.

关键词： Metaheuristic Global optimization Fuzzy logic teaching-learning-based optimization Parameter tuning

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teaching-learning-based optimization algorithm for hybrid flow shop scheduling problem with assembly operations

Teaching-learning-based optimization algorithm for hybrid fl...

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第30届中国控制与决策会议

作者： Zhiwei Xu Deming Lei School of Automation Wuhan University of Technology

This paper addresses assembly-type hybrid flow shop scheduling problem(AHFSP) with the minimi-zation of total tardiness and makespan. The products have parts which are first processed in hybrid flow shop and then are assembled at the assembly stage. To solve this problem, a part-based solution representation is adopted and a teachers' teaching-learning-based optimization(TTLBO) is proposed. Teachers chosen from a non-dominated set undergo a self-learning phase and a teaching phase and these phases are implemented by multiple neighborhood search and global search, respectively. More-over, the learning phase of learners is removed and the learning of teachers is intensified to avoid the low-efficiency computation. Extensive experiments are conducted to test the performance of TTLBO. The computational results demonstrate that TTLBO is an effective and competitive method for multi-objective AHFSP.

关键词： Hybrid flow shop scheduling multi-objective optimization assembly teaching-learning-based optimization

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Disassembly sequence planning using a Simplified teaching-learning-based optimization algorithm

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ADVANCED ENGINEERING INFORMATICS 2014年第4期28卷 518-527页

作者： Xia, Kai Gao, Liang Li, Weidong Chao, Kuo-Ming Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Coventry Univ Fac Engn & Comp Coventry CV1 5FB W Midlands England

Disassembly Sequence Planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, the teaching-learning-based optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This paper presents a Simplified teaching-learning-based optimization (STLBO) algorithm for solving DSP problems effectively. The STLBO algorithm inherits the main idea of the teaching-leaming-based evolutionary mechanism from the TLBO algorithm, while the realization method for the evolutionary mechanism and the adaptation methods for the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a Feasible Solution Generator (FSG) used to generate a feasible disassembly sequence, a teaching Phase Operator (TPO) and a learning Phase Operator (LPO) used to learn and evolve the solutions towards better ones by applying the method of precedence preservation crossover operation. Numerical experiments with case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Disassembly Disassembly sequence planning Meta-heuristics teaching-learning-based optimization Simplified teaching-learning-based optimization

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A comparative study of a teaching-learning-based optimization algorithm on multi-objective unconstrained and constrained functions

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2014年第3期26卷 332-346页

作者： Rao, R. Venkata Waghmare, G. G. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

Multi-objective optimization is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints. Real-life engineering designs often contain more than one conflicting objective function, which requires a multi-objective approach. In a single-objective optimization problem, the optimal solution is clearly defined, while a set of trade-offs that gives rise to numerous solutions exists in multi-objective optimization problems. Each solution represents a particular performance trade-off between the objectives and can be considered optimal. In this paper, the performance of a recently developed teaching-learning-based optimization (TLBO) algorithm is evaluated against the other optimization algorithms over a set of multi-objective unconstrained and constrained test functions and the results are compared. The TLBO algorithm was observed to outperform the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. (C) 2013 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Multi-objective optimization Unconstrained and constrained benchmark functions

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Minimax Design of Linear Phase FIR Hilbert Transformer using teaching-learning-based optimization 8

Minimax Design of Linear Phase FIR Hilbert Transformer using...

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8th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Kwan, Hon Keung Zhang, Miao Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781509028603

In this paper, teaching-learning-based optimization (TLBO) is used for minimax design of linear phase finite impulse response (FIR) digital Hilbert transformers. TLBO is a population-based and heuristic search algorithm which is parameter-free and exhibits a strong convergence ability. The results obtained from using TLBO to design Type 4 linear phase FIR highpass digital Hilbert transformers indicate that it is an effective method to reach smaller equiripple peak errors as compared to those obtained by the least-squares error minimization method and the optimal Parks-McClellan algorithm.

关键词： Digital Hilbert transformer Highpass digital Hilbert transformer design Type 4 linear phase FIR digital filters Minimax design teaching-learning-based optimization Evolutionary optimization Global optimization

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2-Dimensional FIR Digital Filter Design using teaching-learning-based optimization 61

2-Dimensional FIR Digital Filter Design using Teaching-Learn...

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61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Kwan, Hon Keung Zhang, Miao Univ Windsor Dept Elect & Comp Engn 401 Sunset Ave Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781538673928

In this paper, weighted least-squares (WLS) design of 2-dimensional nonlinear phase circularly symmetric finite impulse digital filters using teaching-learning-based optimization is presented. To improve the performance of the teaching-learning-based optimization algorithm, an elitist replacement with mutation is applied to the worst solution in a population. Three 2D nonlinear phase FIR lowpass digital filters are designed and the results indicate that lower mean squared complex errors can be obtained as compared to those obtained by another design method.

关键词： 2-D nonlinear phase FIR digital filter design Complex frequency response approximation WLS design teaching-learning-based optimization

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