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Performance based design optimization of an intrinsically compliant 6-dofparallel robot

MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2022年第4期50卷 1237-1252页

作者： Jamwal, Prashant K. Kapsalyamov, Akim Hussain, Shahid Ghayesh, Mergen H. Nazarbayev Univ Dept Elect & Comp Engn Astana Kazakhstan ReL Res LLP Astana Kazakhstan Univ Canberra Fac Sci & Technol Human Ctr Technol Res Ctr Canberra ACT 2601 Australia Univ Adelaide Sch Mech Engn Adelaide SA Australia

Parallel robots are preferred over serial robots owing to their enhanced accuracy and rigidity which comes from their higher stiffness. However, there are applications both in industry and in healthcare where higher accuracy is required alongside high compliance (reduced stiffness). Accuracy and compliance being conflicting to each other are difficult to achieve simultaneously. To address this issue, an intrinsically compliant 6-dofparallel robot is proposed in this work. Kinematic and analytical modeling is performed for its conceptual design to obtain the Jacobian matrix and thereby map the joint and Cartesian spaces. Robot's structure design is analyzed, and the wrench analysis is also carried out to estimate the link forces and stiffness. It is shown that by small changes in the proposed robot design;its compliance can be altered making it suitable for a range of applications. It is also shown mathematically that the robot design can be optimized to maintain higher accuracy together with higher compliance. To carry out design optimization, three important performance criteria, namely;global condition number (for higher accuracies), norm of link forces (to reduce actuator power requirement) and robot compliance (for response to an external wrench) are mathematically formulated. Later, a multi-criteria optimization is performed using an evolutionary algorithm to simultaneously optimize these performance criteria. From the final robot design selected, it is evident that a higher robot compliance with optimal condition number and link forces can be achieved.

关键词： Condition number design optimization Jacobian analysis non-dominated sorting genetic algorithm parallel robot robot compliance

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Application of modified NSGA-II algorithm to multi-objective reactive power planning

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APPLIED SOFT COMPUTING 2012年第2期12卷 741-753页

作者： Ramesh, S. Kannan, S. Baskar, S. Arulmigu Kalasalingam Coll Engn Instrumentat & Control Engn Dept Krishnankoil Tamil Nadu India Kalasalingam Univ Elect & Elect Engn Dept Krishnankoil Tamil Nadu India Thiagarajar Coll Engn Elect & Elect Engn Dept Madurai Tamil Nadu India

This paper discusses the application of Modified non-dominated sorting genetic algorithm-II (MNSGA-II) to multi-objective Reactive Power Planning (RPP) problem. The three objectives considered are minimization of combined operating and VAR allocation cost, bus voltage profile improvement and voltage stability enhancement. For maintaining good diversity in nondominated solutions, Dynamic Crowding Distance (DCD) procedure is implemented in NSGA-II and it is called as MNSGA-II. The standard IEEE 30-bus test system, practical 69-bus Indian system and IEEE 118-bus system are considered to analyze the performance of MNSGA-II. The results obtained using MNSGA-II are compared with NSGA-II and validated with reference pareto-front generated by conventional weighted sum method using Covariance Matrix Adapted Evolution Strategy (CMA-ES). The performance of NSGA-II and MNSGA-II are compared with respect to best, mean, worst and standard deviation of multi-objective performance measures namely gamma, spread, minimum spacing and Inverted Generational Distance (IGD) in 15 independent runs. The results show the effectiveness of MNSGA-II and confirm its potential to solve the multi-objective RPP problem. A decision-making procedure based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used for finding best compromise solution from the set of pareto-solutions obtained through MNSGA-II. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Covariance Matrix Adapted Evolution Strategy (CMA-ES) Dynamic crowding distance Multi-objective Multi-attribute decision making non-dominated sorting genetic algorithm Optimization Reactive power planning TOPSIS

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Strengthened Dominance Relation NSGA-Ⅲ algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

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Computers, Materials & Continua 2024年第1期78卷 375-392页

作者： Liang Zeng Junyang Shi Yanyan Li Shanshan Wang Weigang Li School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China School of Information Science and Engineering Wuhan University of Science and TechnologyWuhan430081China

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing *** involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple *** non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling ***,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and *** enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence ***,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex *** validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test *** experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

关键词： Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover

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Multi-objective re-synchronizing of bus timetable: Model, complexity and solution

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2016年 67卷 149-168页

作者： Wu, Yinghui Yang, Hai Tang, Jiafu Yu, Yang Jiangsu Univ Sci & Technol Sch Econ & Management 2 Mengxi Rd Zhenjiang 212003 Jiangsu Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Dongbei Univ Finance & Econ Coll Management Sci & Engn Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

This work is originally motived by the re-planning of a bus network timetable. The existing timetable with even headways for the network is generated using line by line timetabling approach without considering the interactions between lines. Decision-makers (i.e., schedulers) intend to synchronize vehicle timetable of lines at transfer nodes to facilitate passenger transfers while being concerned with the impacts of re-designed timetable on the regularity of existing timetable and the accustomed trip plans of passengers. Regarding this situation, we investigate a multi-objective re-synchronizing of bus timetable (MSBT) problem, which is characterized by headway-sensitive passenger demand, uneven headways, service regularity, flexible synchronization and involvement of existing bus timetable. A multi-objective optimization model for the MSBT is proposed to make a trade-off between the total number of passengers benefited by smooth transfers and the maximal deviation from the departure times of the existing timetable. By clarifying the mathematical properties and solution space of the model, we prove that the MSBT problem is NP-hard, and its Pareto-optimal front is non-convex. Therefore, we design a non-dominated sorting genetic (NSGA-II) based algorithm to solve this problem. Numerical experiments show that the designed algorithm, compared with enumeration method, can generate high-quality Pareto solutions within reasonable times. We also find that the timetable allowing larger flexibility of headways can obtain more and better Pareto-optimal solutions, which can provide decision-makers more choice. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Bus timetabling Passenger transfer Multi-objective optimization non-dominated sorting genetic algorithm

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Application of multi-objective optimization genetic algorithm to design terahertz metamaterials with fano resonances

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PHYSICA SCRIPTA 2024年第6期99卷 065555-065555页

作者： Qin, Binyi Li, Yun Liao, Qitao Qiu, Jie Yang, Ruizhao Zheng, Jincun Zhong, Hesen Yulin Normal Univ Ctr Appl Math Guangxi Yulin 537000 Peoples R China Yulin Normal Univ Guangxi Coll & Univ Key Lab Complex Syst Optimizat & Big Data Proc Yulin 537000 Peoples R China Yulin Normal Univ Res Ctr Intelligent Informat & Commun Technol Sch Phys & Telecommun Engn Yulin 537000 Peoples R China Yulin Normal Univ Sch Chem & Food Sci Yulin 537000 Peoples R China Yulin Normal Univ Sch Phys & Telecommun Engn Yulin 537000 Peoples R China Yulin Normal Univ Sch Comp Sci & Engn Yulin 537000 Peoples R China Yulin Normal Univ Optoelect Informat Res Ctr Sch Phys & Telecommun Engn Yulin 537000 Peoples R China

In the prosperous development of terahertz (THz) metamaterials, Fano resonances have gained attention due to their potential applications in ultrasensitive systems. The performance of Fano resonance is directly influenced by the geometrical parameters of the element structure. However, the traditional design rules for Fano resonances in metamaterials rely on an empirical trial-and-error strategy, necessitating significant effort to achieve optimal results. To address this issue, we propose a design method in this study that utilizes the finite integration technique in time domain (FITD) along with a multi-objective optimization genetic algorithm for the intelligent design of metamaterial structures exhibiting the Fano resonance phenomenon. The FITD method is primarily used to calculate the Fano resonance with different metamaterial geometric structure parameters, while the genetic algorithm efficiently selects the optimal solution. Our method, characterized by high efficiency and complete independence from prior knowledge, could offer a new design technique for metamaterials with specific functions, thereby contributing to the development of THz applications.

关键词： Terahertz metamaterials design fano resonances multi-objective optimization non-dominated sorting genetic algorithm

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Optimal design of multichannel fiber Bragg grating filters using Pareto multi-objective optimization algorithm

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OPTICS COMMUNICATIONS 2016年 358卷 59-64页

作者： Chen, Jing Liu, Tundong Jiang, Hao Xiamen Univ Sch Informat Sci & Engn Xiamen 361005 Peoples R China Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

A Pareto-based multi-objective optimization approach is proposed to design multichannel FBG filters. Instead of defining a single optimal objective, the proposed method establishes the multi-objective model by taking two design objectives into account, which are minimizing the maximum index modulation and minimizing the mean dispersion error. To address this optimization problem, we develop a two-stage evolutionary computation approach integrating an elitist non-dominated sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS). NSGA-ll is utilized to search for the candidate solutions in terms of both objectives. The obtained results are provided as Paretic) front. Subsequently, the best compromise solution is determined by the TOPSIS method from the Paretic) front according to the decision maker's preference. The design results show that the proposed approach yields a remarkable reduction of the maximum index modulation and the performance of dispersion spectra of the designed filter can be optimized simultaneously. (C) 2015 Elsevier B.V. All rights reserved

关键词： Fiber Bragg grating (FBG) Multichannel filter Multi-objective optimization non-dominated sorting genetic algorithm

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Optimisation of process parameters in friction stir welding based on residual stress analysis: a feasibility study

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SCIENCE AND TECHNOLOGY OF WELDING AND JOINING 2010年第5期15卷 369-377页

作者： Tutum, C. C. Hattel, J. H. Tech Univ Denmark Dept Mech Engn Sect Mfg Engn DK-2800 Lyngby Denmark

The present paper considers the optimisation of process parameters in friction stir welding (FSW). More specifically, the choices of rotational speed and traverse welding speed have been investigated using genetic algorithms. The welding process is simulated in a transient, two-dimensional sequentially coupled thermomechanical model in ANSYS. This model is then used in an optimisation case where the two objectives are the minimisation of the peak residual stresses and the maximisation of the welding speed. The results indicate that the objectives for the considered case are conflicting, and this is presented as a Pareto optimal front. Moreover, a higher welding speed for a fixed rotational speed results, in general, in slightly higher stress levels in the tension zone, whereas a higher rotational speed for a fixed welding speed yields somewhat lower peak residual stress, however, a wider tension zone, leading to a substantially higher residual tensile force.

关键词： Friction stir welding Thermal pseudomechanical model Residual stress Constrained multiobjective optimisation non-dominated sorting genetic algorithm

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Agent-Based Modeling for Evaluation of Crop Pattern and Water Management Policies

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WATER RESOURCES MANAGEMENT 2019年第11期33卷 3707-3720页

作者： Nouri, Alireza Saghafian, Bahram Delavar, Majid Bazargan-Lari, Mohammad Reza Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Tarbiat Modares Univ Dept Water Resources Engn Tehran Iran Islamic Azad Univ East Tehran Branch Dept Civil Engn Tehran Iran

The objective of the present paper is to propose a framework for development of an optimal cropping pattern aimed at ground water recovery using an agent based approach. In the proposed agent-based model (ABM), the agents' learning from each other as well as their self-learning from their own behavioral feedback was studied through simulation of the behavior of agricultural agents using fuzzy inference system (FIS). Moreover, the agents' behavior were determined using linear programming in order to maximize the farmers' income. The governmental agent regulated the interactions between agricultural and environmental agents by imposing its policies in the form of scenarios. The efficiency of the presented methodology was evaluated using hydrological data of Najaf Abad region, located in Iran's central plain, on the basis of three hydrological scenarios (wet, normal, and dry) subject to governmental policy of aquifer recovery. The results showed that in a normal scenario with current groundwater withdrawal, the water level reduced by an average of 0.18 m per year. In contrast, the water level increased by an average of 0.48 m under aquifer recovery scenario. Furthermore, despite 17% reduction in water rights of agricultural agents in the study area, the total long-term agricultural income declined only by less than 4%, and in the planning horizon, their average annual income associated with these two management scenarios were estimated at 123.5 and 119 million US dollars, respectively.

关键词： Agent Based Model Fuzzy Inference System Behavioral Constraints Ground Water Recovery non-dominated sorting genetic algorithm

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A fine-grained parallel approach for the registration areas optimization

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LOGIC JOURNAL OF THE IGPL 2017年第6期25卷 862-876页

作者： Berrocal-Plaza, Victor Vega-Rodriguez, Miguel A. Sanchez-Perez, Juan M. Univ Extremadura Dept Comp & Commun Technol Escuela Politecn Campus Univ S-N Caceres Spain

Mobility management is one of the most important operations in any public land mobile network. For idle terminals, most of mobility management strategies consist of two main procedures: subscriber's location update and paging. These two procedures define a multiobjective optimization problem in which the challenge is to find the network configurations that minimize the number of location updates and the number of paging messages. In this optimization process, the use of accurate information is very important to find the network configurations that best match the subscribers' activity in a mobile network. A mobile activity trace is a very accurate way for representing the subscribers' activity in a mobile network in form of a chronological list of events related to the subscribers' movements and the incoming and outgoing calls. This kind of representation can be used to study the performance of any mobility management strategy at the expense of considerably increasing the time needed to evaluate the objective functions. In fact, we show that more than 99% of the optimizer runtime is used to evaluate the objective functions. With the aim of facing this problem, we propose a fine-grained parallel approach focused on parallelizing the objective functions exclusively. Results show that the efficiency of our approach is always higher than 50% and that the optimizer runtime is drastically reduced.

关键词： OpenMP non-dominated sorting genetic algorithm mobile location management registration areas

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A Memetic Evolutionary Multi-Objective Optimization Method for Environmental Power Unit Commitment

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IEEE TRANSACTIONS ON POWER SYSTEMS 2013年第3期28卷 2660-2669页

作者： Li, Yan-Fu Pedroni, Nicola Zio, Enrico Ecole Cent Paris European Fdn New Energy Elect France Paris France Supelec Paris France Politecn Milan I-20133 Milan Italy

A multi-objective power unit commitment problem is framed to consider simultaneously the objectives of minimizing the operation cost and minimizing the emissions from the generation units. To find the solution of the optimal schedule of the generation units, a memetic evolutionary algorithm is proposed, which combines the non-dominated sorting genetic algorithm-II (NSGA-II) and a local search algorithm. The power dispatch sub-problem is solved by the weighed-sum lambda-iteration approach. The proposed method has been tested on systems composed by 10 and 100 generation units for a 24-hour demand horizon. The Pareto-optimal front obtained contains solutions of different trade off with respect to the two objectives of cost and emission, which are superior to those contained in the Pareto-front obtained by the pure NSGA-II. The solutions of minimum cost are shown to compare well with recent published results obtained by single-objective cost optimization algorithms.

关键词： Environmental/economic dispatch evolutionary algorithm lambda-iteration approach local search memetic algorithm multi-objective optimization non-dominated sorting genetic algorithm power unit commitment

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