NSGA-III algorithm for optimizing robot collaborative task allocation in the internet of things environment

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2024-07-08 DOI:10.1016/j.jocs.2024.102373

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引用次数: 0

Abstract

To improve the performance of intelligent products and reasonably distribute the load of the loading robot, a multi-objective, and multi-objective (Traveling-salesman-problem, TSP) mathematical model was established. A genetic algorithm based on speed invariant and the elite algorithm is proposed to solve the multi-TSP assignment problem. To ensure the integration of the population, a population resettlement strategy with elite lakes was proposed to improve the probability of population transfer to the best Pareto solution. The experiment verified that this strategy can approach the optimal solution more closely during the population convergence process, and compared it with traditional Multi TSP algorithms and single function multi-objective Multi TSP algorithms. By comparing the total distance and maximum deviation of multiple robot systems, it is proven that this algorithm can effectively balance the path length of each robot in task allocation. From the research results, it can be seen that in genetic algorithms, resetting the population after reaching precocity can maintain the optimization characteristics of the population and have a high probability of obtaining Pareto solutions. At the same time, storing elite individuals from previous convergent populations for optimization can better obtain Pareto solutions.

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用于优化物联网环境中机器人协作任务分配的 NSGA-III 算法

为提高智能产品的性能，合理分配装载机器人的负载，建立了多目标、多目标（Traveling-salesman-problem，TSP）数学模型。提出了一种基于速度不变性和精英算法的遗传算法来解决多目标 TSP 分配问题。为确保种群的整合，提出了一种带有精英湖的种群重新安置策略，以提高种群转移到最佳帕累托方案的概率。实验验证了该策略能在种群收敛过程中更接近最优解，并与传统的多目标 TSP 算法和单函数多目标多 TSP 算法进行了比较。通过比较多个机器人系统的总距离和最大偏差，证明该算法能在任务分配中有效平衡每个机器人的路径长度。从研究结果可以看出，在遗传算法中，达到早熟后重置种群可以保持种群的优化特性，获得帕累托解的概率较高。同时，将以前收敛种群中的精英个体存储起来进行优化，可以更好地获得帕累托解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).