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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Yfantis, Vassilios Babskiy, Alexander Doerig, Bastian Winterer, Thorsten Wagner, Achim Ruskowski, Martin RPTU Kaiserslautern Landau Dept Mech & Proc Engn Chair Machine Tools & Control Syst D-67663 Kaiserslautern Germany Flexis AG D-70565 Stuttgart Germany German Res Ctr Artificial Intelligence DFKI Innovat Factory Syst D-67663 Kaiserslautern Germany

ISBN: (纸本)9781713872344

Modern manufacturing systems strive to optimize many different key performance indicators. Additionally, mathematical programming-based scheduling models enable the explicit considerations of constraints. However, due to the ever-increasing demand for customized products it is not guaranteed that all constraints can be met at the same time. An approach to circumvent this problem of infeasibility is to replace the constraints by soft-constraints, i.e., to penalize their violation in the objective function. The presence of multiple competing objectives and soft-constraints gives rise to multi-objective optimization problems, where a decision maker has to weigh and balance the different objectives and soft-constraints according to his/her preferences and priorities. Ideally the values of all objectives and soft-constraints should lie within the same order of magnitude, making it easy to weigh them against each other. However, for heterogeneous objectives and soft-constraints with different scales and units this might be challenging. This paper presents an evolutionary algorithm for the optimal parametrization of multi-objective mixed-integer linear programming-based scheduling models. The goal of the evolutionary algorithm is to compute weights for the different terms of the objective which lead to a balanced influence on the overall objective at the optimum. Furthermore, the algorithm is extended by introducing an a priori weighing of the objectives in the fitness function of the evolutionary algorithm. The method is demonstrated on a scheduling problem in which a given set of orders has to be allocated to machines within a manufacturing environment. Copyright (c) 2023 The Authors.

关键词： evolutionary algorithm Multi-objective optimization Scheduling

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evolutionary algorithm Sandbox A Web-Based Graphical User Interface for evolutionary algorithms

Evolutionary Algorithm Sandbox A Web-Based Graphical User In...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gardner, Brent G. Simon, Dan Cleveland State Univ Dept Elect & Comp Engn Cleveland OH 44115 USA

ISBN: (纸本)9781424427932

The evolutionary algorithm (EA) Sandbox is an Adobe(R) Flex(R)-based graphical user interface (GUI) that provides a visual demonstration of evolutionary algorithm simulations. It allows the user to select EA parameters and algorithms (such as a basic genetic algorithm, biogeography-based optimization, and opposition-based learning), run a simulation, and view the results after each generation. The EA Sandbox is meant to be a learning tool and starting point for users, giving them the ability to examine how different parameters and algorithms perform for a number of common benchmark functions. The EA Sandbox can also be easily extended to incorporate more algorithms and problem functions.

关键词： graphical user interface evolutionary algorithm genetic algorithm biogeography-based optimization opposition-based learning

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evolutionary algorithm with PID control system for central air-conditioning energy saving

Evolutionary algorithm with PID control system for central a...

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International Conference on Industry, Information System and Material Engineering

作者： Lin, Fan Zeng, Wenhua Jiang, Yi Liang, Qi Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China Xiamen Univ Sch Software Xiamen 361005 Peoples R China Xiamen Univ Fujian Key Lab Brain Like Intelligent Syst Xiamen 361005 Peoples R China

ISBN: (纸本)9783037850275

Central air-conditioning energy saving under the control of the process of multi-parameter is a typical time-varying systems and nonlinear complex systems. In this paper, we analyzed and studied the process-oriented control system for central air-conditioning energy saving algorithm and architecture, and used evolutionary algorithm and PID control strategy to improve the existing control system. The new system makes the control system to achieve better function control and energy saving.

关键词： evolutionary algorithm Robust PID control algorithm

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An efficient problem-specific evolutionary algorithm for flexible job shop scheduling problem with specific workers in highly customised manufacturing systems

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Li, Jiahang Liu, Qihao Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan Peoples R China

The integrated optimisation problem of production scheduling and workforce scheduling has emerged as a critical challenge in modern manufacturing systems. Although existing research predominantly addresses single-tasking workers capable of handling one operation at a time, the scheduling complexity introduced by multitasking workers performing multiple operations at a time remains understudied. This gap is particularly significant in highly customised industries such as shipbuilding and aerospace manufacturing, where the versatility of the workforce substantially impacts production efficiency. This study investigates the flexible job-shop scheduling problem with multitasking workers (FJSP-MW), proposing a genetic algorithm with knowledge-based local search (GALS). The proposed algorithm incorporates two key innovations: (1) a disjunctive graph model for FJSP-MW with a total weighted tardiness (TWT) objective function and (2) problem-specific neighbourhood structures based on critical paths. Comprehensive experiments evaluate the algorithm's performance using 20 instances and a case study. The results of the case study demonstrate significant improvements;reductions of 32.14% in TWT and 39.02% in makespan are obtained compared to the original scheduling solution. The results confirm that GALS outperforms state-of-the-art algorithms in solution quality and convergence speed.

关键词： Flexible job-shop scheduling multitasking workers workforce scheduling evolutionary algorithm genetic algorithm

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A novel multi-objective evolutionary algorithm for transit network design and frequency-setting problem considering passengers’ choice behaviors under station congestion

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Transportation Research Part B: Methodological 2025年 197卷

作者： Mingzhang Liang Min Xu Shuaian Wang School of Transportation Southeast University Nanjing 210096 China Department of Logistics and Maritime Studies Faculty of Business The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong

The transit network design and frequency-setting problem (TNDFSP) plays a critical role in urban transit system planning. Due to the conflict between the level of service and operating costs, extensive research has been conducted to obtain a set of trade-off solutions between the interests of users and operators. However, most studies ignored the effects of station congestion in TNDFSP, resulting in unrealistic solutions or a failure to achieve optimal design schemes. Therefore, this study investigates the multi-objective optimization of TNDFSP considering users’ choice behaviors under station congestion. To address the problem, a multi-objective bilevel optimization model is first formulated. The upper level is a bi-objective optimization model with two conflicting objectives: minimizing users’ cost and minimizing operator’s cost. The lower-level problem is a passenger assignment problem under station congestion. Moreover, a novel multi-objective evolutionary algorithm based on objective space decomposition (MOEA-OSD) is proposed to solve the complex problem. When dealing with multi-objective optimizations, a decomposition mechanism is developed to convert the problem into multiple subproblems. These subproblems are optimized using an evolutionary approach with newly designed selection process and elite preservation strategy to achieve desirable convergence and diversity. The computational results obtained using Mandl’s benchmark demonstrate the efficacy of MOEA-OSD and the advantage of the proposed model in achieving more comprehensive trade-off solutions.

关键词： Transit network design Frequency setting Passenger assignment Multi-objective optimization evolutionary algorithm

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A Research Mode Based evolutionary algorithm for Many-Objective Optimization

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Chinese Journal of Electronics 2019年第4期28卷 764-772页

作者： CHEN Guoyu LI Junhua Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University

The development of algorithms to solve Many-objective optimization problems(MaOPs) has attracted significant research interest in recent *** various types of Pareto front(PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm(RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector *** concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.

关键词： Many-objective optimization Reference vector Research mode evolutionary algorithm

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Dose reconstruction with Compton camera during proton therapy via subset-driven origin ensemble and double evolutionary algorithm

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Nuclear Science and Techniques 2023年第4期34卷 135-148页

作者： Zhi-Yang Yao Yong-Shun Xiao Ji-Zhong Zhao Department of Engineering Physics Tsinghua UniversityBeijing 100084China Key Laboratory of Particle&Radiation Imaging(Tsinghua University) Ministry of EducationBeijing 100084China

Compton camera-based prompt gamma(PG) imaging has been proposed for range verification during proton therapy. However, a deviation between the PG and dose distributions, as well as the difference between the reconstructed PG and exact values, limit the effectiveness of the approach in accurate range monitoring during clinical applications. The aim of the study was to realize a PG-based dose reconstruction with a Compton camera, thereby further improving the prediction accuracy of in vivo range verification and providing a novel method for beam monitoring during proton therapy. In this paper, we present an approach based on a subset-driven origin ensemble with resolution recovery and a double evolutionary algorithm to reconstruct the dose depth profile(DDP) from the gamma events obtained by a cadmium-zinc-telluride Compton camera with limited position and energy resolution. Simulations of proton pencil beams with clinical particle rate irradiating phantoms made of different materials and the CT-based thoracic phantom were used to evaluate the feasibility of the proposed method. The results show that for the monoenergetic proton pencil beam irradiating homogeneous-material box phantom,the accuracy of the reconstructed DDP was within 0.3 mm for range prediction and within 5.2% for dose prediction. In particular, for 1.6-Gy irradiation in the therapy simulation of thoracic tumors, the range deviation of the reconstructed spreadout Bragg peak was within 0.8 mm, and the relative dose deviation in the peak area was less than 7% compared to the exact values. The results demonstrate the potential and feasibility of the proposed method in future Compton-based accurate dose reconstruction and range verification during proton therapy.

关键词： Prompt gamma imaging Dose reconstruction Range verification Origin ensemble Compton camera evolutionary algorithm

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An improved master-apprentice evolutionary algorithm for minimum independent dominating set problem

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Frontiers of Computer Science 2023年第4期17卷 1-14页

作者： Shiwei PAN Yiming MA Yiyuan WANG Zhiguo ZHOU Jinchao JI Minghao YIN Shuli HU School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130117China Key Laboratory of Applied Statistics of MOE Northeast Normal UniversityChangchun 130117China

The minimum independent dominance set(MIDS)problem is an important version of the dominating set with some other *** this work,we present an improved master-apprentice evolutionary algorithm for solving the MIDS problem based on a path-breaking strategy called *** proposed MAE-PB algorithm combines a construction function for the initial solution generation and candidate solution *** is a multiple neighborhood-based local search algorithm that improves the quality of the solution using a path-breaking strategy for solution recombination based on master and apprentice solutions and a perturbation strategy for disturbing the solution when the algorithm cannot improve the solution quality within a certain number of *** show the competitiveness of the MAE-PB algorithm by presenting the computational results on classical benchmarks from the literature and a suite of massive graphs from real-world *** results show that the MAE-PB algorithm achieves high *** particular,for the classical benchmarks,the MAE-PB algorithm obtains the best-known results for seven instances,whereas for several massive graphs,it improves the best-known results for 62 *** investigate the proposed key ingredients to determine their impact on the performance of the proposed algorithm.

关键词： evolutionary algorithm combinatorial optimization minimum independent dominating set local search master apprentice path breaking

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evolutionary algorithm Inspired Binary Sequence based Fault Detection in Uniformly Excited Linear Antenna Array

Evolutionary Algorithm Inspired Binary Sequence based Fault ...

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2023 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2023

作者： Chatterjee, Soumyo Mitra, Sandipan Chatterjee, Sayan Majumder, Arijit Heritage Institute of Technology West Bengal Kolkata India Jadavpur University Etce Department West Bengal Kolkata India Sameer West Bengal Kolkata India

ISBN: (纸本)9798350328264

This Present article proposes a hybrid technique of fault finding in uniformly excited linear antenna array with improved execution time. In this technique differential evolution algorithm has been used to approximate the faulty behavior of uniformly excited linear antenna array. After anticipating the approximate behavior, binary sequence based method has been developed facilitating identification of faulty elements in the linear antenna array. Effectiveness of the method has been demonstrated through fault instances in 26 elements uniformly excited linear antenna array of isotropic radiators. © 2023 IEEE.

关键词： Binary Sequence evolutionary algorithm Fault Detection Uniformly excited linear antenna array

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Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization problems

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Journal of Systems Engineering and Electronics 2009年第1期20卷 204-210页

作者： Liu Chun'an Wang Yuping Dept. of Mathematics Baoji Univ. of Arts and Science Baoji 721013 P. R. China School of Computer Engineering and Technology Xidian Univ. Xi'an 710071 P. R. China

A new method to solve dynamic nonlinear constrained optimization problems （DNCOP） is proposed. First, the time （environment） variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem （SNCOP）. Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into a static bi-objective optimization problem. As a result, the original DNCOP is approximately transformed into several static bi-objective optimization problems. Third, a new multiobjective evolutionary algorithm is proposed based on a new selection operator and an improved nonuniformity mutation operator. The simulation results indicate that the proposed algorithm is effective for DNCOP.

关键词： dynamic optimization nonlinear constrained optimization evolutionary algorithm optimal solutions

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