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检索条件"任意字段=2016 International Congress on Computation Algorithms in Engineering"

共 1728 条记录，以下是61-70 订阅

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Synchronous Parallel Heuristics for Solving the Joint Order Batching and Picker Routing Problem 13

Synchronous Parallel Heuristics for Solving the Joint Order ...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Tran, Son Thai Almeida, Rui Jorge Defryn, Christof Van Nieuwenhuyse, Inneke School of Business and Economics Maastricth University Dept. of Quantitative Economics Maastricht Netherlands School of Business and Economics Maastricth University Dept. of Data Analytics and Digitization Maastricht Netherlands University of Antwerp Faculty of Business and Economics Dept. of Engineering Management Antwerp Belgium FlandersMake@UHasselt Data Science Institute Hasselt University Faculty of Sciences Hasselt Belgium

ISBN: (纸本)9798350308365

The joint order batching and picker routing problem is an important problem for improving warehouse efficiency. The goal is to minimize the total distance travel of picking customer orders. It is NP-hard, indicating that exact solutions are intractable for large instances. Many solution methods provided in the current literature use local search to solve the problem sequentially, often including a complex searching procedure for the order batching problem (OBP) followed by a simple heuristic for the picker routing problem (PRP). In this paper, we propose three heuristics that jointly solves the OBP and the PRP: two of these are combinations of variable neighborhood search, simulated annealing, and tabu search, while the third one uses guided local search. We discuss how the search procedure of the proposed algorithms can be parallelized, and benchmark them against state-of-the-art heuristics using well-studied instances. The results show a reduction of 3.06% to 7.63% in the geometric mean of the total travel distance across 64 instances. Increasing the number of threads in parallelization leads to diminishing returns in reducing running time and has no statistical effect on travel distance. In contrast, increasing the chunk size results in a linear increase in running time for all proposed algorithms, and improves the travel distance obtained when the batch size is 75 items. © 2024 IEEE.

关键词： Simulated annealing

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A Hybrid Approach with BRKGA and Data Mining for the Early/Tardy Scheduling Problem 13

A Hybrid Approach with BRKGA and Data Mining for the Early/T...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Mendonça, Israel Fatyanosa, Tirana Noor Aritsugi, Masayoshi Silva, Pedro Henrique González Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan Brawijaya University Faculty of Computer Science Malang Indonesia Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

This paper introduces a novel hybrid genetic algorithm combined with data mining to solve a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys and we use the Biased Random-Key Genetic Algorithm (BRKGA) to establish an order in which jobs are scheduled. A data mining component gathers data from the evolutionary steps of BRKGA and suggests chromosomes based on past observations. In this way, we address a key challenge in BRKGA - the exploration of solutions near an individual gene - by leveraging data-driven insights to refine and enhance the search space. Comparative analysis reveals that our hybrid algorithm significantly benefits from the pattern recognition capabilities of data mining, leading to improved scheduled efficiency. The results of a series of computational experiments underscore the potential of this hybrid approach that, although requiring slightly longer computational times, is better than the previous baseline BRKGA algorithm in terms of solution quality. © 2024 IEEE.

关键词： Scheduling algorithms

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Multi-Objective Genetic-Programming Hyper-Heuristic for Evolving Interpretable Flexible Job Shop Scheduling Rules 13

Multi-Objective Genetic-Programming Hyper-Heuristic for Evol...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Pang, Junwei Mei, Yi Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand

ISBN: (纸本)9798350308365

The job shop scheduling problem is an important combinatorial optimisation problem in the real world. Genetic programming hyper-heuristic has been successfully applied to automatically evolve effective dispatching rules to make a schedule in real time without much domain knowledge. However, the interpretability of GP-evolved rules has been largely neglected, which could lead to the lack of reliability and trustworthiness of the evolved rules in practice. Current work related to interpretable genetic programming algorithms primarily uses the model size as the interpretability metric. This could not fully reflect the interpretability of evolved rules. To overcome the limitation, we employ structural complexity and dimension gap as more comprehensive interpretability measures. In addition, a new multi-objective genetic programming algorithm, which applies the a non-dominated sorting method to solve the objective selection bias issue, is proposed to optimise the makespan (scheduling objective), structural complexity and dimension gap simultaneously. A variety of experiments demonstrates the competitive performance of our proposed algorithm based on effectiveness, convergence and diversity. Furthermore, the semantics of evolved dispatching rules are analysed to show their better interpretability. © 2024 IEEE.

关键词： Genetic programming

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A Cooperative Coevolutionary Approach to Designing Acceptance Tests for Jobs with Weakly Hard Real- Time Constraints 13

A Cooperative Coevolutionary Approach to Designing Acceptanc...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Salamun, Karla Džapo, Hrvoje University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798350308365

Some real-time applications, including control sys-tems, communication systems, and multimedia systems, allow the relaxation of hard real-time constraints in the sense of tolerating deadline violations under certain conditions. The conditions that specify the ratio and distribution of tolerable deadline violations are defined through the weakly hard real-time constraints, and real-time systems that implement these constraints are referred to as weakly hard real-time systems. The majority of the existing approaches for scheduling tasks with weakly hard real-time constraints rely on classifying task instances, i.e., jobs, as either mandatory or optional (skippable), and provide a mechanism for ensuring timely completion of mandatory jobs, while aiming to maximize the number of timely completed optional jobs. However, few approaches implement online acceptance tests for optional jobs which ensure that jobs that are unable to be completed before their respective deadline are not accepted into the system. The existing acceptance tests for optional jobs have high computational complexity with respect to the number of tasks and are therefore not suitable for implementation on real-time computing platforms with limited resources. In this research, we develop a scheduling approach that maximizes the number of completed jobs and implements computationally efficient acceptance tests that ensure early rejection of jobs that cannot be completed before their deadline. This is achieved through a genetic programming approach with cooperative coevolution. Our approach uses two scheduling heuristics: a heuristic for dynamic job priority assignment, and a heuristic for evaluating acceptance for newly activated jobs. The results show that the proposed approach achieves comparable performance to state-of-the-art algorithms in terms of service quality, but with significantly lower computational overhead. Unlike the state-of-the-art scheduling algorithms, the scheduling heuristics generat

关键词： Genetic programming

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Deep Reinforcement Learning for Solving the Vehicle Routing Problem in Practical Logistics 13

Deep Reinforcement Learning for Solving the Vehicle Routing ...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Cai, Junchuang Zhang, Xinzhi Lin, Qiuzhen Dong, Lisha Chen, Weineng Ming, Zhong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Institute of Information Technology College of Sino-German Robotics Shenzhen China SSouth China University of Technology College of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350308365

The vehicle routing problem with simultaneous pickup-delivery and time windows (VRPPDTW) is applicable to a wide range of practical scenarios within the domains of transportation and logistics. When addressing this complex problem, conventional heuristic algorithms have exhibited limitations in effectiveness, primarily due to the need for expert knowledge in heuristic design, which often prioritizes immediate improvements and leads to convergence towards local optima. To overcome this challenge, we propose a deep Q-network-based iterated local search algorithm (DQN-ILS). This algorithm employs a deep Q-learning network to automatically learn a policy for selecting heuristics to improve the current solution. Simultaneously, an adaptive perturbation strategy is designed to avoid becoming trapped in local optima. In this way, our algorithm can better balance the trade-off between exploration and exploitation when solving VRPPDTW. Experimental studies conducted on a real test suite of large-scale VRPPDTW instances validate the superiority of the proposed DQN-ILS over several recently proposed methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Design Challenges of Autonomous Formation Controller for Small Satellite Missions 74

Design Challenges of Autonomous Formation Controller for Sma...

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74th international Astronautical congress, IAC 2023

作者： Thangavel, Sanjeeviraja Radice, Gianmarco Department of Aerospace Engineering Singapore Institute of Technology Singapore

Establishing clear control boundaries between attitude dynamics control systems (ADCS) and autonomous formation flight controls (AFFC) is paramount for small satellite formation flight architecture. The ADCS obtains input data from the controller for guidance, navigation, and control. AFFC has direct control over an actuator and sensors for satellite missions. This paper examines the recent development of miniature AOCS components and control design strategies for both linear and nonlinear and algorithms (propellant optimization, environmental perturbations, trajectory optimization, satellite reconfiguration and planning, attitude determination, orbit correction/prediction, and relative dynamics) to achieve AFF. The results are summarized in the high-level architecture specifications of the individual sensors, actuators, and controllers. In addition, numerical simulation results for the reaction wheel controllers were analysed to determine Euler angles, regulated torques, reaction wheel torque, reaction wheel speed. Furthermore, reduced overall power consumption for the specified (active 4 wheels) maneuver. The proposed quaternion feedback control system model reduces computation time, fuel consumption, and collision avoidance between follow-on satellites. Copyright © 2023 by the international Astronautical Federation (IAF). All rights reserved.

关键词： Attitude dynamics control systems Formation flight architecture Linear and nonlinear algorithms Relative Dynamics Trajectory optimization

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Genetic Programming with Multi-Task Feature Selection for Alzheimer's Disease Diagnosis 13

Genetic Programming with Multi-Task Feature Selection for Al...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Tang, Shanshan Chen, Qi Xue, Bing Huang, Min Zhang, Mengjie College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington New Zealand

ISBN: (纸本)9798350308365

Alzheimer's disease (AD) has been the most common cause of dementia making cognitive score prediction and important feature identification crucial for its diagnosis. Although sparse linear regression has been used for this purpose due to its simplicity, it often selects an excessive number of features to track the disease and assumes a linear relationship between input and output, which might not always hold. To address these limitations, genetic programming-based symbolic regression (GPSR) algorithms have been proposed. GPSR can select the important features by exploring the feature space and learning a regression model without any assumption of model structure. However, the generalization ability of existing GPSR methods still needs to be improved. Considering the multiple related prediction tasks in AD studies, this work proposes a new method called linear scaled GPSR with multi-task feature selection (LSGPMTFS), to promote the prediction performance of each task by knowledge-sharing among multiple tasks. LSGPMTFS has two stages. The first stage learns a specific feature subset for each task. In the second stage, the model for each task is searched on the union of feature subsets selected from the first stage. The experimental results on authentic AD datasets demonstrate that the proposed algorithm can select a small set of important features with better learning and generalization performance compared with other GPSR methods. © 2024 IEEE.

关键词： Genetic programming

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Multi-Objective Optimisation strategy for On-Orbit Fault-Tolerant Decision Making 13

Multi-Objective Optimisation strategy for On-Orbit Fault-Tol...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Cowlishaw, Robert Arulselvan, Ashwin Riccardi, Annalisa University of Strathclyde Mechanical and Aerospace Engineering Glasgow United Kingdom University of Strathclyde Management Science Glasgow United Kingdom

ISBN: (纸本)9798350308365

With an increasing number of satellites in orbit, consensus across a heterogeneous group of satellites can lead to a more neutral, unbiased, and accurate decisions. Fault tolerant consensus algorithms such as Practical Byzantine Fault Tolerance (pBFT) require communication with all other network members up to 4 times. In a network with thousands of satellites in space on different trajectories, this time can approach millennia. There-fore, identifying a subset of satellites that can form a sub-network able to converge to a consensus decision in a useful time window, while maximising the number of members to increase consensus accuracy and trustworthiness, can be formulated as a multi-objective combinatorial optimisation problem. The problem is explained and defined with the optimisation method and the consensus algorithm steps described. Metrics for measuring the output of the optimal pareto front are considered and applied to the front computed. The real satellite positions used generate a non-fixed topology and high latency scenario such as that of a real on-orbit decision being made. The trend shown over 100 days of satellite positions propagation with up to 82 international Charter: Space and Major Disasters satellites shows up to 22 satellites can be used in a subset with a near linear increase in consensus time along the optimal pareto front and exponential trend for the mean values computed over 100 runs of the NSGA-II algorithm. The minimum consensus time is found to be 47 minutes for a subset of 4 satellites for the given time frame. © 2024 IEEE.

关键词： Combinatorial optimization

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Surrogate-Assisted Flip for Evolutionary High-Dimensional Multiobjective Feature Selection 13

Surrogate-Assisted Flip for Evolutionary High-Dimensional Mu...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Yang, Qi-Te Luo, Liu-Yue Chen, Chun-Hua Li, Jian-Yu Zhong, Jing-Hui Zhang, Jun Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9798350308365

Feature selection (FS), which aims to minimize the classification error and the number of selected features, can essentially be modeled as a multiobjective optimization problem. To deal with such multiobjective FS (MOFS) problems, many multiobjective evolutionary algorithms (MOEAs) have been proposed. These MOEAs can find multiple optimal solutions, whereas substantial computational resources are required for fitness evaluations (FEs). More seriously, due to the high dimensionality and sparsity of FS problems, many FEs will be spent on unpromising solutions, resulting in a meaningless loss of computational resources. In this paper, two innovations are made so as to improve the performance of MOEAs for MOFS. First, we propose a surrogate-assisted flip (SF) strategy for MOEAs to reduce the FE waste on potentially unpromising solutions and improve search efficiency. This SF strategy is free of FE consumption and theoretically can be embedded in any MOEA to deal with MOFS problems. The experimental results show that this SF can improve the performance of different MOEAs, especially in reducing the number of selected features. Second, based on SF, we propose a more efficient SF -assisted MOEA for dealing with high-dimensional MOFS problems. The proposed algorithm divides the whole search space into different subspaces based on redundant feature subsets clustering to achieve parallel search, so as to reduce the search difficulty. The experimental results show that this algorithm is even more competitive than other SF -assisted MOEAs. © 2024 IEEE.

关键词： Optimization algorithms

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DEVELOPMENT OF OPEN MULTI-PROCESSING BASED PARALLEL MULTIPHYSICS TOPOLOGY OPTIMIZATION SOFTWARE FOR DC BUS BAR

DEVELOPMENT OF OPEN MULTI-PROCESSING BASED PARALLEL MULTIPHY...

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ASME 2024 international Mechanical engineering congress and Exposition, IMECE 2024

作者： Sinha, Hitesh Kumar Singh, Krishna Mohan Department of Mechanical and Industrial Engineering Indian Institute of Technology Roorkee UK Roorkee247667 India

ISBN: (纸本)9780791888674

This paper deals with developing a parallel multiphysics topology optimization methodology for a DC bus *** developed algorithms have been implemented in an object-oriented C++ *** testing the effect of grid size on the optimal area, the optimal area converges for a medium grid ***, the decrease in the grid size prevents the checkerboard pattern and grayscale region (the area that contains solid and void regions) and shows the finer distribution of optimized *** finer grids, computational time *** the time analysis of serial code, we observe that multiphysics finite element analysis (FEA), optimization algorithm method of moving asymptotes (MMA), and filter process take up significant computation *** the present work, we aim to reduce the computational time and efficiently utilize CPU resources using parallel *** have created a parallel program using an application programming interface (API) based on the OpenMP multithread parallel *** tested our parallel program with different grid sizes for a sample test *** result shows that speed-up increases with an increase in ***, after eight threads, speed up, and parallel efficiency decreases for coarser grid size. Copyright © 2024 by ASME.

关键词： C++ (programming language)

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