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A neutrosophic set-based TLBO algorithm for the flexible job-shop scheduling problem with routing flexibility and uncertain processing times

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COMPLEX & INTELLIGENT SYSTEMS 2021年第6期7卷 2833-2853页

作者： Jin, Liangliang Zhang, Chaoyong Wen, Xiaoyu Sun, Chengda Fei, Xinjiang Shaoxing Univ Dept Mech Engn Shaoxing Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China Zhengzhou Univ Light Ind Henan Key Lab Intelligent Mfg Mech Equipment Zhengzhou Peoples R China

Different with the plain flexible job-shop scheduling problem (FJSP), the FJSP with routing flexibility is more complex and it can be deemed as the integrated process planning and (job shop) scheduling (IPPS) problem, where the process planning and the job shop scheduling two important functions are considered as a whole and optimized simultaneously to utilize the flexibility in a flexible manufacturing system. Although, many novel meta-heuristics have been introduced to address this problem and corresponding fruitful results have been observed;the dilemma in real-life applications of resultant scheduling schemes stems from the uncertainty or the nondeterminacy in processing times, since the uncertainty in processing times will disturb the predefined scheduling scheme by influencing unfinished operations. As a result, the performance of the manufacturing system will also be deteriorated. Nevertheless, research on such issue has seldom been considered before. This research focuses on the modeling and optimization method of the IPPS problem with uncertain processing times. The neutrosophic set is first introduced to model uncertain processing times. Due to the complexity in the math model, we developed an improved teaching-learning-based optimization(TLBO) algorithm to capture more robust scheduling schemes. In the proposed optimization method, the score values of the uncertain completion times on each machine are compared and optimized to obtain the most promising solution. Distinct levels of fluctuations or uncertainties on processing times are defined in testing the well-known Kim's benchmark instances. The performance of computational results is analyzed and competitive solutions with smaller score values are obtained. Computational results show that more robust scheduling schemes with corresponding neutrosophic Gantt charts can be obtained;in general, the results of the improved TLBO algorithm suggested in this research are better than those of other algorithms w

关键词： Neutrosophic sets Flexible job-shop scheduling Processing planning teaching-learning-based optimization TLBO Integrated process planning and scheduling IPPS Uncertain processing times

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A modified teaching learning metaheuristic algorithm with opposite-based learning for permutation flow-shop scheduling problem

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 57-79页

作者： Balande, Umesh Shrimankar, Deepti Visvesvaraya Natl Inst Technol Nagpur 440010 Maharashtra India

teaching-learning-based optimization is one of the well-known metaheuristic algorithm in the research industry. Recently, various population-based algorithms have been developed for solving optimization problems. In this paper, a random scale factor approach is proposed to modify the simple TLBO algorithm. Modified teaching-learning-based optimization with Opposite-based-learning algorithm is applied to solve the Permutation Flow-Shop-Scheduling Problem with the purpose of minimizing the makespan. The OBL approach is used to enhance the quality of the initial population and convergence speed. PFSSP is used extensively for solving scheduling problem, which belongs to the category of NP-hard optimization problems. First, MTLBO is developed to effectively determine the PFSSP using the Largest Order Value rule-based random key, so that individual job schedules are converted into discrete schedules. Second, new initial populations are generated in MTLBO using the Nawaz-Enscore-Ham heuristic mechanism. Finally, the local exploitation ability is enhanced in the MTLBO using effective swap, insert and inverse structures. The performance of proposed algorithm is validated using ten benchmark functions and the Wilcoxon rank test. The computational results and comparisons indicate that the proposed algorithm outperformed over five well-known datasets such as Carlier, Reeves, Heller, Taillards and VRF benchmark test functions, compared to other metaheuristic algorithms. Thep-value indicated the significance and superiority of the proposed algorithm over other metaheuristic algorithms.

关键词： Evolutionary algorithms Opposite-based learning Permutation flow-shop scheduling problem teaching-learning-based optimization

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Artificial intelligence driven advanced optimization strategy vis-a-vis response surface optimization of production medium: Bacterial exopolysaccharide production as a case- study

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BIOCHEMICAL ENGINEERING JOURNAL 2022年第0期178卷 108271-108271页

作者： Rabiya, Rabiya Sen, Ramkrishna Indian Inst Technol Kharagpur Dept Biotechnol Kharagpur 721302 W Bengal India

In the present study, Artificial Intelligence (AI) driven multivariate-optimization strategy, hybrid-support vector machine (h-SVM) was employed and compared with conventional response surface methodology (RSM) in terms of its performance efficiency to optimize the medium composition for improved exopolysaccharide (EPS) production by a thermophilic isolate of Bacillus licheniformis. Firstly, the application of Plackett-Burman design and analysis indicated that sucrose, KH2PO4 and NaCl have significant positive effects, whereas NaNO3 showed a significant negative effect on EPS production. Then, experimental data of a central-composite design was leveraged by RSM and the h-SVM to optimize these significant variables. SVM showed better performance in data modeling (R-2 = 0.9874, Root-mean-square error = 0.693) compared to conventional RSM. Subsequently, the SVM model was coupled with optimization algorithms, teaching-learning-based optimization (TLBO) and Particle swarm optimization (PSO). As a result, a maximum EPS concentration of 64 +/- 1.1 g L. was obtained from h-SVM predicted model (SVM-PSO, SVM-TLBO), which is about a 5-fold increase in EPS production to that obtained with the un-optimized medium. Within our knowledge, this is the first report on the implementation of SVM-TLBO to improve the production of bioproducts like bacterial EPS while comparing it with other techniques like SVM-PSO and RSM.

关键词： Exopolysaccharides Plackett-Burman Design Response surface methodology Support vector machine Particle swarm optimization teaching-learning-based optimization

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Artificial neural network based multivariable optimization of a hybrid system integrated with phase change materials, active cooling and hybrid ventilations

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ENERGY CONVERSION AND MANAGEMENT 2019年 197卷 111859-000页

作者： Zhou, Yuekuan Zheng, Siqian Zhang, Guoqiang Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg Serv Engn Hong Kong Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Hunan Univ Natl Ctr Int Res Collaborat Bldg Safety & Environ Changsha 410082 Hunan Peoples R China Hunan Univ Coll Civil Engn Changsha 410082 Hunan Peoples R China

Utilising diversified forms of energy in combination with advanced energy conversions and thermal energy storages is an effective way of developing high energy-efficient renewable systems for green buildings. In this study, a novel hybrid system for the energy cascade utilisation has been proposed, integrating the hybrid ventilations, the active photovoltaic cooling, the radiative cooling and the phase change materials' storages. An enthalpy-based numerical modelling using the finite-difference method, which has been developed earlier, was used to characterize the sophisticated heat transfer process. A generic optimization methodology with competitive computational efficiency was applied by implementing the supervised machine learning and the advanced optimization algorithm. Multivariable optimizations for geometrical and operating parameters have been conducted and contrasted between the teaching-learning-based optimization and the particle swarm optimization. The results illustrate that the developed artificial neural network-based data-driven learning algorithm is more accurate and more computational-efficient than the traditional 'lsqcurvefit' fitting methodology for the characterization of the optimization function. In addition, the optimal case through the teaching-learning-based optimization is more robust than the optimal case through the particle swarm optimization in terms of the equivalent overall energy generation. This study presents a novel hybrid system for the energy cascade utilisation and a new generic optimization methodology, which are important for the promotion of green buildings with high efficiency of renewable energy utilisation.

关键词： Phase Change Materials (PCMs) Hybrid ventilations Active photovoltaic cooling Machine learning Particle swarm optimization teaching-learning-based optimization

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A surrogate-assisted hybrid swarm optimization algorithm for high-dimensional computationally expensive problems

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 72卷

作者： Li, Fan Li, Yingli Cai, Xiwen Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

In this paper, a surrogate-assisted hybrid swarm optimization algorithm is proposed to solve high-dimensional computationally expensive problems. Two swarms are, respectively, used in different optimization states. The first swarm uses the teaching-learning-based optimization in the early stage to enhance the exploration. The second swarm uses the particle swarm optimization in the later stage to accelerate convergence. Two different pre-screening criteria based on the corresponding evolutionary rules are proposed to select promising individuals for exact function evaluations. Several commonly used benchmark functions with their dimensions varying from 30 to 200 and an engineering optimization problem are used to validate the efficiency of the proposed algorithm. In addition, a comprehensive analysis is conducted to demonstrate the effectiveness of each main component of the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm over other compared algorithms.

关键词： Computationally expensive problems Hybrid swarm Particle swarm optimization teaching-learning-based optimization Surrogate

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Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis

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WIND ENGINEERING 2023年第2期47卷 464-490页

作者： Maheshwari, Ankur Sood, Yog Raj Jaiswal, Supriya Natl Inst Technol Hamirpur Elect Engn Dept Hamirpur 177005 Himachal Prades India

Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The teaching learning based optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.

关键词： Optimal power flow teaching-learning-based optimization renewable energy sources renewable uncertainty cost and emission voltage profile enhancement transmission losses

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Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

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SN APPLIED SCIENCES 2020年第2期2卷 273页

作者： Ezugwu, Absalom E. Univ KwaZulu Natal Sch Comp Sci King Edward RdPietermaritzburg Campus ZA-3201 Pietermaritzburg Kwazulu Natal South Africa

The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be

关键词： Automatic clustering Clustering analysis Differential evolution Particle swarm optimization Firefly algorithm Invasive weed optimization Artificial bee colony Bees algorithm Biogeography-based optimization Harmony search Symbiotic organisms search teaching-learning-based optimization DB validity index CS validity index

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Numerical Study on the Thermal and Optical Performances of an Aerogel Glazing System with the Multivariable optimization Using an Advanced Machine learning Algorithm

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ADVANCED THEORY AND SIMULATIONS 2019年第9期2卷

作者： Zheng, Siqian Zhou, Yuekuan City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong 999077 Peoples R China Hunan Univ Coll Civil Engn Key Lab Bldg Safety & Energy Efficiency Minist Educ Changsha 410082 Hunan Peoples R China Hong Kong Polytech Univ Fac Construct & Environm Dept Bldg Serv Engn Hong Kong 999077 Peoples R China

The implementation of advanced materials in high-efficient glazing system is important for green buildings. In this study, aerogel granules are implemented in the glazing system to form a translucent window with super-insulating performance. An experimentally validated numerical modeling integrating both heat transfer model and optical model is developed to characterize the sophisticated heat transfer and solar radiation transmission mechanisms. Sensitivity analysis is presented with quantifiable contribution ratio of each parameter to the total heat gain. Instead of returning back to numerical modeling repeatedly, an advanced optimization engine implemented with a generic optimization methodology with competitive computational efficiency and accuracy is proposed by implementing the supervised machine learning and advanced optimization algorithms. The research results show that the developed artificial neural network modeling is more accurate and computational-efficient than the traditional lsqcurvefit fitting methodology. In addition, the optimal case through the teaching-learning-based optimization is more robust and competitive than the optimal case through the particle swarm optimization in terms of the total heat gain. This study presents an in-depth understanding of heat transfer and solar radiation transmission of nanoporous aerogel granules together with a robust optimal design, which is important for the promotion of green buildings with high-energy performance.

关键词： aerogel machine learning optimization function particle swarm optimization teaching-learning-based optimization transmittance

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An optimization method of active distribution network considering uncertainties of renewable DGs

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Energy Procedia 2019年 158卷 934-939页

作者： Chengsi Yong Xiangyu Kong Ying Chen Quan Xu Li Yu Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China Electric Power Research Institute of China Southern Power Grid Guangzhou 510080 China

With the access of the renewable DGs such as wind turbines and photovoltaic generations, network operation state is uncertain due to the randomness of these renewable DGs. This paper proposes a novel optimal method of the active distribution network (ADN) considering the uncertain conditions and the coordination control of source-network-load. By optimizing the controllable distributed power output, controlling the network switches, and managing the demand-side load synchronously, the impact of distributed renewable energies can be reduced and the reliable operation of ADN can be ensured. The chance constrained programming is used to deal with the uncertainties. The proposed model is settled by the improved teaching-learning-based optimization algorithm (ITLBO) and the performance of the algorithm is verified by the comparison with the TLBO in the modified IEEE 33-bus distribution system.

关键词： Active distribution network distributed renewable energy teaching-learning-based optimization chance-constrained programming

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Design and optimization for a New XYZ Micropositioner with Embedded Displacement Sensor for Biomaterial Sample Probing Application

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SENSORS 2022年第21期22卷 8204页

作者： Dang, Minh Phung Le, Hieu Giang Phan, Thu Thi Dang Le Chau, Ngoc Dao, Thanh-Phong Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam Thu Duc Coll Technol Fac Mech Engn Ho Chi Minh City Vietnam Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

An XYZ compliant micropositioner has been widely mentioned in precision engineering, but the displacements in the X, Y, and Z directions are often not the same. In this study, a design and optimization for a new XYZ micropositioner are developed to obtain three same displacements in three axes. The proposed micropositioner is a planar mechanism whose advantage is a generation of three motions with only two actuators. In the design strategy, the proposed micropositioner is designed by a combination of a symmetrical four-lever displacement amplifier, a symmetrical parallel guiding mechanism, and a symmetrical parallel redirection mechanism. The Z-shaped hinges are used to gain motion in the Z-axis displacement. Four flexure right-circular hinges are combined with two rigid joints and two flexure leaf hinges to permit two large X-and-Y displacements. The symmetrical four-lever displacement amplifier is designed to increase the micropositioner's travel. The displacement sensor is built by embedding the strain gauges on the hinges of the micropositioner, which is developed to measure the travel of the micropositioner. The behaviors and performances of the micropositioner are modeled by using the Taguchi-based response surface methodology. Additionally, the geometrical factors of the XYZ micropositioner are optimized by teaching-learning-based optimization. The optimized design parameters are defined with an A of 0.9 mm, a B of 0.8 mm, a C of 0.57 mm, and a D of 0.7 mm. The safety factor gains 1.85, while the displacement achieves 515.7278 mu m. The developed micropositioner is a potential option for biomedical sample testing in a nanoindentation system.

关键词： compliant mechanism XYZ micropositioner displacement sensor optimization teaching-learning-based optimization

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