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1st International Conference on Innovative Product Design and Intelligent Manufacturing System, ICIPDIMS 2019

作者： Md Riyaz, Sk. Srinivas, P. Ramu, I. Mechanical Department Vishnu Institute of Technology Bhimavaram534202 India

ISBN: (纸本)9789811526954

Electric discharge machining is one of the most popular machines which are capable of machining geometrically complex and hard materials, that are precise and difficult to machine such as heat-treated tools, superalloys, heat-resistant steels, carbides, etc;these types of materials are being widely used in aerospace, automobiles, marine industries, etc. This paper aims to investigate the optimal set of process parameters of die sinker EDM on aluminum alloy 6082 with copper tube electrode by varying the input parameters such as pulse-on time (Ton), pulse-off time (Toff), current (I), and voltage (v). An L16 orthogonal array has been designed using the Taguchi method for input parameters to conduct experiments for getting the output variables such as metal removal rate (MRR) and tool wear rate (TWR). Based on the analysis, the experimental results have been carried out using ANOVA method. To find out the optimal set of values, multi-objective genetic algorithm (MOGA) is used to predict the experimental results. Upon comparing the experimental values and predicted values, an optimal value is obtained which is useful to the manufactures to get the high-performance rate of EDM to machine aluminum alloy 6082. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Electric discharge machining multi-objective genetic algorithm Taguchi method

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Research on the Optimization Design of High-Rise Office Building Performance Based on a multi-objective genetic algorithm

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BUILDINGS 2025年第10期15卷 1636-1636页

作者： Yuan, Zhaohui Pan, Jiajie Chen, Xing Peng, Yiyan Hunan Univ Sch Architecture & Planning Changsha 410006 Peoples R China Hefei Univ Technol Sch Architecture & Art Hefei 230002 Peoples R China

Office buildings often consume a large amount of energy during their operational phase, primarily due to insufficient consideration of the coordination among energy consumption, thermal comfort, and visual comfort in the design process. This study employs a multi-objective genetic algorithm to optimize the overall performance of office buildings by parameterizing seven key design variables: floor plan aspect ratio, building orientation angle, window-to-wall ratios (WWRs) in all directions, shading strategy, shading device orientation, shading device length, and shading device spacing. A building performance simulation model was established to conduct a global optimization search, with simultaneous analysis across the east, south, west, and north fa & ccedil;ades to obtain a set of Pareto-optimal solutions that satisfy multiple performance objectives. The results indicate that optimal comprehensive performance across energy use, thermal comfort, and visual comfort can be achieved under the following conditions: a floor plan aspect ratio of 0.67-1, building rotation of 0-20 degrees clockwise, an east-facing WWR of 0.4, south- and west-facing WWRs of 0.2-0.4, and a north-facing WWR of 0.4-0.6. For shading, horizontal devices with a length of 0.8-1.0 m, downward tilt angle of 10-30 degrees, and spacing of 0.6-1.2 m are recommended. These findings provide scientific parameter references and optimization pathways for the design of high-performance office buildings in various climate conditions.

关键词： building performance optimization visual comfort thermal comfort energy consumption office buildings multi-objective genetic algorithm

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GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

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APPLIED SOFT COMPUTING 2019年 79卷 310-325页

作者： Harkat, H. Ruano, A. E. Ruano, M. G. Bennani, S. D. Univ Algarve Fac Sci & Technol Faro Portugal Univ Sidi Mohamed Ben Abdellah Fac Sci & Technol Fes Morocco Univ Lisbon Inst Super Tecn IDMEC Lisbon Portugal Univ Coimbra CISUC Coimbra Portugal

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target's locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture. Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples' fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hyperbola signatures GPR Classification multi-objective genetic algorithm Mutual information Feature selection Neural networks High order statistics

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Energy, exergy, and exergy-economic optimization of a multigeneration system driven by geothermal primary heat source using multi-objective genetic algorithm (MOGA)

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Energy 2025年 330卷

作者： Ekariansyah, Andi S. Muwonge, Martin Saefuttamam, M. Rifqi Dikaimana, Yophie Nasruddin Department of Mechanical Engineering University of Indonesia Depok16424 Indonesia National Research and Innovation Agency KST BJ Habibie Setu Tangerang Selatan15314 Indonesia Kyambogo University Kampala Uganda

A modified multigeneration system (MGS) using geothermal heat to provide products of cooling, heating, power generation, hydrogen, and fresh water through seawater desalination, has been proposed and analyzed. It uses liquefied natural gas (LNG) as a heat sink during the process and enhanced by incorporating a thermoelectric generation (TEG) module and reverse osmosis (RO) desalination unit. The modified system has been evaluated in terms of thermal efficiency, exergy efficiency, and the sum unit cost of products (SUCP) to assess its thermal, exergy, and exergy-economic performances. These three objective functions were then optimized using the multi-objective genetic algorithm (MOGA) based on selected decision variables from the Engineering Equation Solver (EES) code connected with the MATLAB module and the TOPSIS method to obtain their most optimal values. The system could generate a heating capacity of 3577 kW, cooling capacity of 505.9 kW, output power of 282.5 kW, hydrogen production of 2.969 kg/h, and distillate water of 145.8 m3/hr. The most optimal values for thermal efficiency, exergy efficiency, and SUCP are 60.08 %, 18.65 %, and 159.389 $/GJ, respectively. With the TEG and RO desalination unit, the MGS resulted in a decrease in both thermal and exergy efficiency but with an increase in the SUCP. © 2025 Elsevier Ltd

关键词： Energy Exergo-economic Exergy Geothermal multi-objective genetic algorithm Optimization

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A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 130卷 381-394页

作者： Nitisiri, Krisanarach Gen, Mitsuo Ohwada, Hayato Tokyo Univ Sci Grad Sch Sci & Engn Dept Ind Adm Noda Chiba Japan Fuzzy Log Syst Inst Fukuoka Fukuoka Japan

Railway system is a reliable and efficiency major public transportation. It is supported by many countries since it has a less environmental effect compared to another type of transportation. As the railway networks have become larger and more complex with increasing passenger demand, both aspects from the passenger satisfaction and operational cost need to be satisfied. This paper proposes a Parallel multi-objective Evolutionary algorithm with Hybrid Sampling Strategy and learning-based mutation to solve the railway train scheduling problem. Learning techniques have been coupled with a multi-objective genetic algorithm to guide the search for better solutions. In this paper, we incorporate a learning-based algorithm into a mutation process. The evaluation process is divided into sub-process and calculated by a parallel computational unit using GPU CUDA framework. Two sets of numerical experiments based on a small-scale case of Thailand ARL transit line and a larger case of BTS transit network are implemented to verify the effectiveness of the proposed approaches. The experimental results show the effectiveness of the proposed algorithm comparing to sequential CPU computational and two classical multi-objective evolutionary algorithms. With the same number of operating trains, the proposed algorithm can obtain schedule with less average waiting time and the time used for computational is significantly reduced.

关键词： Railway scheduling multi-objective genetic algorithm Parallel computation CUDA

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Horizontal inventory coordination in supply chain based on multi-objective genetic algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 4803-4810页

作者： Pang, Hui Dong, Shaohua Zhang, Xinwei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China

In this paper, under the circumstance of uncertain customer demand, the enterprises on the same node in multiple supply chains conduct horizontal inventory coordination according to their own inventory and demand. Based on the combination of horizontal inventory replenishment and vertical normal replenishment, the related mathematical model has been established. By studying the inventory strategy in the decentralized and centralized decision-making, the dual marginalization effects are found in the network system composed of multiple supply chain. So the revenue sharing contract is introduced to solve this problem. It has been found in the study that after adopting the horizontal inventory coordination mechanism of the supply chain, the profit of the members in the supply chain system and the overall profit of the system are both higher than those which didn't adopt the mechanism. The improved multi-objective genetic algorithm is used to calculate and verify the problem. In particular, after the revenue sharing contract is adopted, the profits are further enhanced.

关键词： Inventory coordination economic order quantity inventory strategy revenue sharing multi-objective genetic algorithm

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RF design of APEX2 two-cell continuous-wave normal conducting photoelectron gun cavity based on multi-objective genetic algorithm

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2019年 940卷 12-18页

作者： Luo, T. Feng, H. Filippetto, D. Johnson, M. Lambert, A. Li, D. Mitchell, C. Sannibale, F. Staples, J. Virostek, S. Wells, R. Lawrence Berkeley Natl Lab One Cyclotron Rd Berkeley CA 94720 USA Tsinghua Univ Dept Engn Phys Beijing 100084 Peoples R China

High brightness, high repetition rate electron beams are key components for optimizing the performance of next generation scientific instruments, such as MHz-class X-ray Free Electron Laser (XFEL) and Ultra-fast Electron Diffraction/Microscopy (UED/UEM). In the Advanced Photo-injector EXperiment (APEX) at Berkeley Lab, a photoelectron gun based on a 185.7 MHz normal conducting re-entrant RF cavity, has been proven to be a feasible solution to provide high brightness, high repetition rate electron beam for both XFEL and UED/UEM. Based on the success of APEX, a new electron gun system, named APEX2, has been under development to further improve the electron beam brightness. For APEX2, we have designed a new 162.5 MHz two-cell photoelectron gun and achieved a significant increase on the cathode launching field and the beam exit energy. For a fixed charge per bunch, these improvements will allow for the emittance reduction and hence to an increased beam brightness. The design of APEX2 gun cavity is a complex problem with multiple design goals and restrictions, some even competing each other. For a systematic and comprehensive search for the optimized cavity geometry, we have developed and implemented a novel optimization method based on the multi-objective genetic algorithm (MOGA).

关键词： Photoelectron RF gun RF cavity design multi-objective genetic algorithm

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multi-objective genetic algorithm for Economic Statistical Design of the T² Control Chart with Variable Sample Size: The Updated Markov Chain Approach

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JOURNAL OF TESTING AND EVALUATION 2018年第3期46卷 1209-1219页

作者： Seif, Asghar Bu Ali Sina Univ Dept Stat Shahid Fahmideh Blvd Hamadan Iran

T-2 control charts are used to primarily monitor the mean vector of quality characteristics of a process. Recent studies have shown that using variable sample size (VSS) schemes results in charts with more statistical power for detecting small to moderate shifts in the process mean vector. In this study, we have presented a multiple-objective economic statistical design of VSS T-2 control chart with the adjusted average time to signal (AATS) as the statistical objective and the expected cost per hour as the economic objective. Then, a multi-objective genetic algorithm for economic statistical design is proposed for identifying the Pareto optimal solutions of control chart design. Through an illustrative example, the advantages of the proposed approach are shown by providing a list of viable optimal solutions and graphical representations, which indicate the advantage of flexibility and adaptability of our approach.

关键词： economic statistical design Hotelling's T-2 control chart multi-objective genetic algorithm variable sample size

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multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage

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ENERGY 2018年 163卷 351-363页

作者： Abdelkader, Abbassi Rabeh, Abbassi Ali, Dami Mohamed Mohamed, Jemli Univ Tunis High Natl Engn Sch Tunis ENSIT Engn Lab Ind Syst & Renewable Energies LISIER 5 Ave Taha HusseinPOB 56 Tunis 1008 Tunisia Univ Tunis High Natl Engn Sch Tunis ENSIT LaTICE Lab 5 Ave Taha HusseinPOB 56 Tunis 1008 Tunisia Univ Hail Dept Elect Engn Coll Engn POB 2440 Hail Saudi Arabia

The present paper proposes a new approach to optimize the sizing of a multi-source PV/Wind with Hybrid Energy Storage System (HESS). Hence, a developed modeling of all sub-systems composing the integral system has been designed to establish the proposed optimization algorithm. Besides, a frequency management based on Discrete Fourier Transform (DFT) algorithm has been also used to distribute the power provided by the power supply system into different dynamics. Thus, many frequency channels have been obtained in order to divide the roles of each storage device and show the impact of integrating fast dynamics into renewable energies based applications. The reformulation of our optimization problem is considered by the minimization of the Total Cost of Electricity (TCE) and the Loss of Power Supply Probability (LPSP) of the load, simultaneously. In this respect, a multi-objective based genetic algorithm approach was used to size the developed system considering all storage dynamics. In order to achieve an optimal system configuration, different economic analysis cases were established. The obtained results show that the minimum of LPSP is achieved according to a very low TCE which introduces that the exploitation of renewable energy has a very important effect to promote the energy sector in Tunisia. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Renewable energy sources multi-objective genetic algorithm Optimization Sizing Hybrid energy storage system Economic analysis

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Optimal Trajectory Scheme for Robotic Welding Along Complex Joints Using a Hybrid multi-objective genetic algorithm

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IEEE ACCESS 2019年 7卷 158753-158769页

作者： Ogbemhe, John Mpofu, Khumbulani Tlale, Nkgatho Tshwane Univ Technol Dept Ind Engn ZA-0183 Pretoria West South Africa

The problem of trajectory planning is relevant for the proper use of costly robotic systems to mitigate undesirable effects such as vibration and even wear on the mechanical structure of the system. The objective of this study is to design trajectories that are devoid of collision, velocity, acceleration, jerk and snap discontinuities so that the cycle time required to complete the process can be reduced. The trajectory design was constructed for all the six joints, using a 9th order Bezier curve to accommodate the ten boundary conditions required to satisfy the continuity constraints for joints displacement, velocity, acceleration, jerk and snap. The scheme combines the multi-objective genetic algorithm and the multi-objective goal attainment algorithm to solve the problem of total tracking error reduction during arc welding. The use of a hybrid multi-objective algorithm shows an improved average spread, average distance, number of iteration and computational time. Also, it can be concluded from the constraints studied, that the optimal path in terms of the robots dynamic constraints can achieve the expected tracking ability in terms of the optimal joint angles, velocities, acceleration, jerk, snap and torque.

关键词： Industrial robots optimization multi-objective genetic algorithm trajectory planning jerk snap

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