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Multi-objective teaching-learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

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ENGINEERING optimization 2015年第7期47卷 994-1007页

作者： Lin, Wenwen Yu, D. Y. Wang, S. Zhang, Chaoyong Zhang, Sanqiang Tian, Huiyu Luo, Min Liu, Shengqiang Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Southeast Missouri State Univ Dept Ind & Engn Technol Cape Girardeau MO USA Shan Dong Hoteam Software Co Ltd Jinan Peoples R China Hubei Univ Automat Technol Sch Elect & Informat Engn Shiyan Peoples R China China Natl Heavy Duty Truck Grp Co Ltd Jinan Peoples R China

In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly;therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching-learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.

关键词： sustainable manufacturing orthogonal arrays teaching-learning-based optimization multi-objective optimization metal-cutting parameter optimization

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teaching-learning-based optimization algorithm for multi-area economic dispatch

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ENERGY 2014年 68卷 21-28页

作者： Basu, M. Jadavpur Univ Dept Power Engn Kolkata 700098 India

This paper presents teaching learning-based optimization algorithm for solving MAED (multi-area economic dispatch) problem with tie line constraints considering transmission losses, multiple fuels, valve-point loading and prohibited operating zones. TLBO (teaching learning-based optimization) is one of the recently proposed population based algorithms which simulates the teaching learning process of the class room. It is a very simple and robust global optimization technique. The effectiveness of the proposed algorithm has been verified on three different test systems, both small and large, involving varying degree of complexity. Compared with differential evolution, evolutionary programming and real coded genetic algorithm, considering the quality of the solution obtained, the proposed algorithm seems to be a promising alternative approach for solving the MAED problems in practical power system. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Multi-area economic dispatch Tie line constraints teaching-learning-based optimization

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teaching-learning-based optimization with dynamic group strategy for global optimization

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INFORMATION SCIENCES 2014年 273卷 112-131页

作者： Zou, Feng Wang, Lei Hei, Xinhong Chen, Debao Yang, Dongdong Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

Global optimization remains one of the most challenging tasks for evolutionary computation and swarm intelligence. In recent years, there have been some significant developments in these areas regarding the solution of global optimization problems. In this paper, we propose an improved teaching-learning-based optimization (TLBO) algorithm with dynamic group strategy (DGS) for global optimization problems. Different to the original TLBO algorithm, DGSTLBO enables each learner to learn from the mean of his corresponding group, rather than the mean of the class, in the teacher phase. Furthermore, each learner employs the random learning strategy or the quantum-behaved learning strategy in his corresponding group in the learner phase. Regrouping occurs dynamically after a certain number of generations, helping to maintain the diversity of the population and discourage premature convergence. To verify the feasibility and effectiveness of the proposed algorithm, experiments are conducted on 18 numerical benchmark functions in 10, 30, and 50 dimensions. The results show that the proposed DGSTLBO algorithm is an effective method for global optimization problems. (c) 2014 Elsevier Inc. All rights reserved.

关键词： teaching-learning-based optimization Global optimization Dynamic group strategy learning strategy Quantum-behaved

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An optimization algorithm for the multi-objective flexible fuzzy job shop environment with partial flexibility based on adaptive teaching-learning considering fuzzy processing times

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SOFT COMPUTING 2024年第2期28卷 1459-1489页

作者： Tovar, Mary Jimenez Acevedo-Chedid, Jaime Ospina-Mateus, Holman Salas-Navarro, Katherinne Sana, Shib Sankar Univ Tecnol Bolivar Dept Ind Engn Cartagena Colombia Univ Costa Dept Prod & Innovat Barranquilla Colombia Kishore Bharati Bhagini Nivedita Coll Dept Math Kolkata 700060 India

Production scheduling is a critical factor to enhancing productivity in manufacturing engineering and combinatorial optimization research. The complexity and dynamic nature of production systems necessitates innovative solutions. The Job Shop Flexible Programming Problem (FJSP) provides a realistic environment for production, where processing times are variable and uncertain, and multiple objectives need optimization. To solve the Multi-Objective Flexible Fuzzy Job Shop problem with partial flexibility (P-MOFfJSP), this paper proposes a hybrid metaheuristic approach that combines the teaching-learning-based optimization (TLBO) algorithm with a Genetic Algorithm. The proposed algorithm of Adaptive TLBO (TLBO-A) uses two genetic operators (mutation and crossover) with an adaptive population reconfiguration strategy, ensuring solution space exploration and preventing premature convergence. We have evaluated the TLBO-A algorithm's performance on benchmark instances commonly used in programming problems with fuzzy variables. The experimental analysis indicates significant results, demonstrating that the adaptive strategy improves the search for suitable solutions. The proposed algorithm (TLBO-A) exhibits low variations (around 11%) compared to the best mono-objective heuristic for the fuzzy makespan problem, indicating its robustness. Moreover, compared with other heuristics like traditional TLBO, the variations decrease to around 1%. However, TLBO-A stands out as it aims to solve a multi-objective problem, improving the fuzzy makespan, and identifying good results on the Pareto frontier for the fuzzy average flow time, all within this low variation margin. Our contribution addresses the challenges of production scheduling in fuzzy time environments and proposes a practical hybrid metaheuristic approach. The TLBO-A algorithm shows promising results in solving the P-MOFfJSP, highlighting the potential of our proposed methodology for solving real-world production schedulin

关键词： Flexible job-shop teaching-learning-based optimization Fuzzy processing times Hybrid algorithm

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An improved teaching-learning-based optimization with neighborhood search for applications of ANN

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NEUROCOMPUTING 2014年 143卷 231-247页

作者： Wang, Lei Zou, Feng Hei, Xinhong Yang, Dongdong Chen, Debao Jiang, Qiaoyong Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

teaching-learning-based optimization (TLBO) algorithm, which simulates the teaching-learning process of the class room, is one of the recently proposed swarm intelligent (SI) algorithms. The performance of TLBO is maintained by the teaching and learning process, but when the learners cannot found a better position than the old one at some successive iteration, the population might be trapped into local optima. In this paper, an improved teaching-learning-based optimization algorithm with neighborhood search (NSTLBO) is presented. In the proposed method, a ring neighborhood topology is introduced into the original TLBO algorithm to maintain the exploration ability of the population. Different than the traditional method to utilize the global information, the mutation of each learner is now restricted within a certain neighboring area so as to fully utilize the whole space and avoid over-congestion around local optima. Moreover, a mutation operation is presented to NSTLBO during the duplicate eliminations in order to maintain the diversity of population. To verify the performance of the proposed algorithm, thirty-two benchmark functions are utilized. Finally, three application problems of artificial neural network are examined. The results in thirty-two benchmark functions and three applications of ANN indicate that the proposed algorithm has shown interesting outcomes. (C) 2014 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Neighborhood Artificial neural network

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Prediction of berm geometry using a set of laboratory tests combined with teaching-learning-based optimization and artificial bee colony algorithms

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APPLIED OCEAN RESEARCH 2014年 48卷 103-113页

作者： Uzlu, Ergun Komurcu, Murat Ihsan Kankal, Murat Dede, Tayfun Ozturk, Hasan Tahsin Karadeniz Tech Univ Fac Engn Dept Civil Engn TR-61080 Trabzon Turkey

Understanding sediment movement in coastal areas is crucial in planning the stability of coastal structures, the recovery of coastal areas, and the formation of new coast. Accretion or erosion profiles form as a result of sediment movement. The characteristics of these profiles depend on the bed slope, wave conditions, and sediment properties. Here, experimental studies were performed in a wave flume with regular waves, considering different values for the wave height (H-0), wave period (T), bed slope (m), and mean sediment diameter (d(50)). Accretion profiles developed in these experiments, and the geometric parameters of the resulting berms were determined. teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms were applied to regression functions of the data from the physical model. Dimensional and dimensionless equations were found for each parameter. These equations were compared to data from the physical model, to determine the best equation for each parameter and to evaluate the performances of the TLBO and ABC algorithms in the estimation of the berm parameters. Compared to the ABC algorithm, the TLBO algorithm provided better accuracy in estimating the berm parameters. Overall, the equations successfully determined the berm parameters. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Coastal profiles Cross-shore sediment transport Berm Accretion teaching-learning-based optimization Artificial bee colony algorithm

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An effective hybrid teaching-learning-based optimization algorithm for permutation flow shop scheduling problem

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ADVANCES IN ENGINEERING SOFTWARE 2014年第0期77卷 35-47页

作者： Xie, Zhanpeng Zhang, Chaoyong Shao, Xiniyu Lin, Wenwen Zhu, Haiping Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hunan Peoples R China

Permutation flow shop scheduling (PFSP) is among the most studied scheduling settings. In this paper, a hybrid teaching-learning-based optimization algorithm (HTLBO), which combines a novel teaching-learning-based optimization algorithm for solution evolution and a variable neighborhood search (VNS) for fast solution improvement, is proposed for PFSP to determine the job sequence with minimization of makespan criterion and minimization of maximum lateness criterion, respectively. To convert the individual to the job permutation, a largest order value (LOV) rule is utilized. Furthermore, a simulated annealing (SA) is adopted as the local search method of VNS after the shaking procedure. Experimental comparisons over public PFSP test instances with other competitive algorithms show the effectiveness of the proposed algorithm. For the DMU problems, 19 new upper bounds are obtained for the instances with makespan criterion and 88 new upper bounds are obtained for the instances with maximum lateness criterion. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Permutation flow shop scheduling problem teaching-learning-based optimization algorithm Variable neighborhood search Simulated annealing Makespan Maximum lateness

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Optimal power flow using teaching-learning-based optimization technique

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ELECTRIC POWER SYSTEMS RESEARCH 2014年 114卷 49-59页

作者： Bouchekara, H. R. E. H. Abido, M. A. Boucherma, M. Univ Constantine 1 Dept Elect Engn LEC Constantine Elect Engn Lab Constantine 25000 Algeria King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

teaching-learning-based optimization is a rising star among metaheuristic techniques with highly competitive performances. This technique is based on the influence of a teacher on learners. In this paper, the teaching-learning-based optimization technique is used to solve the optimal power flow problem. In order to show the effectiveness of the proposed method, it has been applied to the standard IEEE 30-bus and IEEE 118-bus test systems for different objectives that reflect the performances of the power system. Furthermore, the obtained results using the proposed technique have been compared to those obtained using other techniques reported in the literature. The obtained results and the comparison with other techniques indicate that the teaching-learning-based optimization technique provides effective and robust high-quality solution when solving the optimal power flow problem with different complexities. (C) 2014 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Metaheuristic Optimal power flow Power system optimization Voltage profile Voltage stability

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A hybridization of teaching-learning-based optimization and differential evolution for chaotic time series prediction

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NEURAL COMPUTING & APPLICATIONS 2014年第6期25卷 1407-1422页

作者： Wang, Lei Zou, Feng Hei, Xinhong Yang, Dongdong Chen, Debao Jiang, Qiaoyong Cao, Zijian Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching-learning-based optimization (TLBO)-differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm's performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO-DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.

关键词： Chaotic time series Hybrid optimization teaching-learning-based optimization Differential evolution

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Synthesis of Thinned Concentric Circular Antenna Arrays Using teaching-learning-based optimization

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2014年第4期24卷 443-450页

作者： Dib, Nihad Sharaqa, Ashraf Jordan Univ Sci & Technol Dept Elect Engn Irbid 22110 Jordan WorleyParsons Arabia Ltd Commun & Secur Projects Div Al Khobar 31952 Saudi Arabia

In this article, the design of thinned concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum side lobe level (SLL) reduction is studied. The newly proposed global evolutionary optimization method;namely, the teaching-learning-based optimization (TLBO) is used to determine an optimum set of turned ON elements of thinned CCAAs that provides a radiation pattern with optimum SLL reduction. The TLBO represents a new algorithm for optimization problems in electromagnetics and antennas. It is shown that the TLBO provides results that are somewhat better than those obtained using other evolutionary algorithms, like the firefly algorithm and biogeography based optimization. (C) 2013 Wiley Periodicals, Inc.

关键词： antenna arrays circular arrays optimization techniques teaching-learning-based optimization

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