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8th IEEE International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Tanaka, Ryo Sekizaki, Shinya Nishizaki, Ichiro Hayashida, Tomohiro Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan

ISBN: (纸本)9781479998869

In the electricity deregulation, the electricity consumption of consumers depending on the electricity prices will change because the electricity prices are expected to fluctuate according to the market conditions. Therefore, the fluctuation of the electricity consumption can cause difficulty of distribution system operation, e.g. minimizing the distribution line losses, improving the voltage profile, and so on. Previous studies show that Distribution System Reconfiguration (DSR) is effective to minimize distribution line losses and improve voltage profile on the distribution lines. However, the DSR problems in the literatures considering the electricity deregulation are not studied sufficiently. In this paper, we formulate a multi-objective optimization problem about the distribution system operation with DSR, and search for quasi-Pareto optimal solutions using non-dominated sorting genetic algorithm-ii (NSGA-ii).

关键词： Electricity deregulation Reconfiguration Multi-objective optimization non-dominated sorting genetic algorithm-ii

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Multi-Physics Multi-Objective Optimal Design of 5-DOF Magnetic Bearing System with High-speed Permanent Magnet Synchronous Motor 24

Multi-Physics Multi-Objective Optimal Design of 5-DOF Magnet...

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24th International Conference on Electrical Machines and Systems (ICEMS)

作者： Wang, Xiaoyuan Xu, Yuhao Tianjin Univ Tianjin Peoples R China

ISBN: (纸本)9788986510218

In view of the high-precision design requirements of suspension system under high-speed conditions, this paper used multi-objective genetic algorithm (MOGA) to carry out pareto optimization design of five-degree-of-freedom magnetic bearing system, and used finite element method to analyze the structure before and after optimization based on electromagnetic field, stress field and rotor dynamics theory respectively.

关键词： five-degree-of-freedom magnetic bearing system multi-objective genetic algorithm non-dominated sorting genetic algorithm-ii

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Multi-objective flexible job-shop scheduling problem with improved NSGA2 algorithm 43

Multi-objective flexible job-shop scheduling problem with im...

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43rd Chinese Control Conference (CCC)

作者： Xu, Hongyi Li, Minyun Hu Zhou Univ Sch Engn Huzhou 31300 Peoples R China Hu Zhou Univ Sch Intelligent Mfg Huzhou 31300 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

Aiming at the situation of low scheduling efficiency resulting in resource waste in multi-objective flexible job-shop, this paper proposes an improved elitist non-dominated sorting genetic algorithm-ii (NSGA2). A population initialization method is designed based on three optimization objectives. At the same time, the individual fitness is defined according to the individual objective funtion and crowding distance. A parameter adaptive strategy is proposed to realize the adaptability of individual crossover and mutation probability. In the process of optimization, the Beetle Antennae Search (BAS) is used to carry out a second search on the dominated population to improve the single individual search speed of the algorithm and avoid falling into local optimal. Finally, the algorithm is verified on the classic Kacem dataset and the actual production case of the enter-prise, the experimental results show that the proposed method is superior to the existing methods, which proves the feasibility of the algorithm in multi-objective flexible job-shop scheduling problem.

关键词： Adaptive non-dominated sorting genetic algorithm-ii Beetle Antennae Search Initialization

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non-dominated sorting Based Multi-Objective Clustering algorithm for WSN 9

Non-dominated Sorting Based Multi-Objective Clustering Algor...

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9th International Conference on Advanced Infocomm Technology (ICAIT)

作者： Han, Liyuan Wang, Weidong Zhang, Yinghai Wang, Chaowei Qin, Cai Beijing Univ Posts & Telecommun Sch Elect Engn Minist Educ Key Lab Universal Wireless Commun Beijing Peoples R China

ISBN: (纸本)9781538636282

In wireless sensor networks (WSN), energy efficiency is one of the major challenges because of the difficulty of charging nodes in monitored area. Clustering sensor nodes is an effective topology control method to reduce energy consumption of sensor nodes. Studies of clustering algorithm usually focus on the whole lifetime but ignore the stable time (the time at which the first node dies) in WSN. This study proposes a clustering algorithm which aims to improve the stability and extend the lifetime of the network simultaneously by balancing and reducing the energy consumption for each node in WSN. The proposed algorithm is based on an improved non-dominated sorting genetic algorithm-ii (NSGA-ii) which is a multi-objective optimization algorithm to achieve several goals. Five objective functions are used to optimize energy consumption and load balance. In the improved NSGA-ii, a weight value is adopted to evaluate the clustering solutions after the crowding distance to sort the individuals in every generation more reasonably. According to the simulation results, the proposed algorithm achieves longer stable period and longer lifetime than LEACH & clustering algorithm based on traditional NSGA-ii.

关键词： wireless sensor network clustering algorithm balancing energy consumption non-dominated sorting genetic algorithm-ii introduction

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Pareto Optimal Fractional PID for Position Control of Ultrasonic Motor 5

Pareto Optimal Fractional PID for Position Control of Ultras...

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5th International Conference on Control, Instrumentation, and Automation (ICCIA)

作者： Mohammadi, Khatereh Shahnazi, Reza Univ Guilan Dept Elect Engn Rasht Iran

ISBN: (纸本)9781538621349

In this paper, optimal PID and optimal fractional PID controllers for position control of ultrasonic motor are proposed. To design these controllers both minimizing tracking error and control effort are considered. Since these objectives are contradictory the non-dominated sorting genetic algorithm-ii (NSGA-ii) is used as an evolutionary multi-objective method to derive the Pareto-optimal front consisting optimal solutions. The simulation results demonstrate the superiority of the optimal fractional PID controller in minimizing tracking error while utilizing less control effort. Besides, the robustness of the optimal controllers against parameter variations is investigated. The results ensure that the optimal fractional PID controller is more robust than the optimal PID controller.

关键词： Evolutionary algorithm fractional PID controller ultrasonic motors non-dominated sorting genetic algorithm-ii

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A Multi-objective Post Enrolment Course Timetabling Problems: A New Case Study

A Multi-objective Post Enrolment Course Timetabling Problems...

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2010 IEEE World Congress on Computational Intelligence

作者： Abdullah, Salwani Turabieh, Hamza McCollum, Barry McMullan, Paul Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp Bangi 43600 Selangor Malaysia Queens Univ Belfast Dept Comp Sci Belfast BT7 1NN Antrim North Ireland

ISBN: (纸本)9781424481262

This paper presents a multi-objective post enrolment course timetabling problem as a new case study. We added a new soft constraint to the original single objective problem to both increase the complexity and represent a real world course timetabling problem. The new soft constraint introduced here attempts to minimize the total number of waiting timeslots in between courses for every student in a day. We proposed a non-dominated sorting genetic algorithm-ii with a variable population size, called NSGA-ii VPS, based on a given lifetime for each individual that is evaluated at the time of its birth. The algorithm was tested on the standard benchmark problems and experimental results show that the proposed method demonstrably improved upon the original approach (NSGA-ii).

关键词： NSGA-ii VPS constraint handling course timetabling problem educational courses genetic algorithms multi-objective post enrolment non-dominated sorting genetic algorithm-ii soft constraint

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Multi-Objective Optimization of Additive Manufacturing Process

Multi-Objective Optimization of Additive Manufacturing Proce...

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16th IFAC Symposium on Information Control Problems in Manufacturing (INCOM)

作者： Asadollahi-Yazdi, Elnaz Gardan, Julien Lafon, Pascal Univ Technol Troyes CNRS UMR 6281 ICDLASMIS Troyes France EPF Engn Sch Rosiere Pres Troyes France

This paper presents an approach to analyze the critical drawbacks and attributes of Additive Manufacturing (AM) simultaneously to find the best manufacturing parameters to fabricate the AM products. In this study, Fused Deposition Modeling (FDM) is investigated as a common AM technology. For this purpose, a multi-optimization problem is formulated according to the analysis of FDM technology. In this problem, layer thickness and part orientation are determined as the decision variables which are the important parameters of manufacturing. As objective functions, production time and material mass are considered and the surface roughness of FDM products and mechanical behavior of material are defined as the constraint functions. Different methodologies are developed to model the AM criteria according to these decision variables. To find the optimal solutions for manufacturing, non-dominated sorting genetic algorithm-ii (NSGA-ii) is used. Finally, a case study highlighted the reliability of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Multi-Objective Optimization Problem Fused Deposition Modeling Additive Manufacturing non-dominated sorting genetic algorithm-ii Stochastic algorithm

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An Optimal Design of Marine Systems based on Neuro-Response Surface Method 10

An Optimal Design of Marine Systems based on Neuro-Response ...

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10th International Conference on Natural Computation (ICNC)

作者： Lee, Jae-chul Shin, Sung-chul Kim, Soo-young Pusan Natl Univ Dept Naval Architecture & Ocean Engn Busan South Korea

ISBN: (纸本)9781479951512

The shapes of marine system have an effect on various performances;hydrodynamic, structural, propulsion. In case of engineering structure including the marine system, most performances are determined during initial design stage. However, the prediction of system performance is very difficult because performance analysis is time consuming. Thus, if the design techniques considering the performance were developed, it will be very useful design tool in the initial design stage. The major objective in this research is establishing a design methodology for marine system in the initial design stage. For this purpose, we proposed a framework for optimal design based on the Neuro-Response Surface Method (NRSM). The constructed framework is composed of three parts: the definition of the geometry, the generation of the design space using NRSM, and an optimization process. The optimization algorithm of the constructed framework uses a Back-Propagation Neural Network (BPN) and non-dominated sorting genetic algorithm-ii (NSGA-ii). The BPN is used for generating the design space, and the optimization process is done using NSGA-ii. Through case study on a 5MW TLP-type wind turbine and ultimate strength of ship stiffened panel, we have confirmed the usefulness of the constructed framework in view of hydrodynamic performances and structural performances.

关键词： Shape optimization Neuro-Response Surface Method Back-Propagation Neural Network non-dominated sorting genetic algorithm-ii 5MW TLP-type wind turbine ultimate strength of ship stiffened panel

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A multi-objective partitioned design method for integrated energy system

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 162卷

作者： Luo, Hongxuan Zhang, Chen Foo, Eddy Y. S. Gooi, Hoay Beng Sun, Lu Zeng, Tao Chen, Tengpeng Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361102 Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen 518000 Peoples R China Nanyang Technol Univ Ctr Power Engn CPE Sch Elect & Elect Engn Singapore 639798 Singapore Halliburton Control Ctr Excellence Singapore 629215 Singapore

The complexity of a large-scale integrated energy system imposes huge computational burden. Besides, centralised state estimation is not suitable for fast and coordinated optimal management of multi-flow coupled systems and efficient energy utilisation. Furthermore, existing distributed state estimations are dealing with the established static system in the form of partitions. Considering the current method of modelling nonlinear fluids, the final impact on the performance of multiple assessments is non-convex for different partitioning approaches. This paper proposes a multi-objective distributed state estimation design approach for an integrated energy system based on non-dominated sorting genetic algorithm-ii and unscented Kalman filter. The integrated energy system model contains the electric-gas-thermal system and various coupled units. By comparing and evaluating the estimation accuracy, calculation time and economic indicators of the system with different partitions of the system load, the optimal Pareto solution set are obtained from the multiobjective optimisation, which then guides the construction layout to satisfy different application requirements. In situations where the specific requirements are not clear, this paper gives the operator an objective method recommendation with the help of the entropy weight and Topsis synthesis assessment method. The validity of the method is verified by several case studies, and the method not only assists the estimation of the existing integrated energy system, but it also offers engineering significance in guiding the construction of the future integrated energy system.

关键词： Clustering partition Unscented Kalman filter Dynamic state estimation non-dominated sorting genetic algorithm-ii Entropy weighted and topsis

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Two-stage multi-strategy decision-making framework for capacity configuration optimization of grid-connected PV/battery/hydrogen integrated energy system

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JOURNAL OF ENERGY STORAGE 2024年 97卷

作者： Lin, Liangguang Ou, Kai Lin, Qiongbin Xing, Jianwu Wang, Ya-Xiong Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Fuzhou Univ Sch Elect Engn & Automat Fuzhou 350108 Peoples R China REFIRE Grp Shanghai 201800 Peoples R China

The optimal capacity of energy storage facilities is a cornerstone for the investment and low-carbon operation of integrated energy systems (IESs). However, the intermittence of renewable energy and the different operating characteristics of facilities present challenges to IES configuration. Therefore, a two-stage decision-making framework is developed to optimize the capacity of facilities for six schemes comprised of battery energy storage systems and hydrogen energy storage systems. The objectives considered are to minimize the levelized cost of electricity (LCOE), power abandonment rate (PAR) and maximize self-sufficiency rate (SSR) simultaneously. In the first stage, each scheme is solved using NSGA-ii. In the second stage, the weights of objective function are determined by entropy weight method, while the optimal individual is selected from the Pareto solutions by the technique for order preference by similarity to ideal solution approach. Life models of battery, fuel cell, and electrolyzer are introduced to quantify device replacement costs. Meanwhile, carbon trading mechanisms and time-of-use tariffs are considered to assess environmental and economic benefits. The results show that the hydrogen-electric coupling scheme demonstrated superior performance, with LCOE, SSR, and PAR of 0.6416 & YEN;/kWh, 48.9 %, and 1.96 %, respectively, and the hydrogen storage tank is closely related to LCOE and PAR.

关键词： Battery energy storage system Hydrogen energy storage system Configuration optimization non-dominated sorting genetic algorithm-ii Entropy weight method Technique for order preference by similarity to ideal solution

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