Safety-efficiency integrated assembly: The next-stage adaptive task allocation and planning framework for human–robot collaboration

Robotics and Computer-Integrated Manufacturing, Год журнала: 2025, Номер 94, С. 102942 - 102942

Опубликована: Янв. 7, 2025

Язык: Английский

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Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

IEEE/CAA Journal of Automatica Sinica, Год журнала: 2024, Номер 11(8), С. 1819 - 1835

Опубликована: Июль 19, 2024

Язык: Английский

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Personalized Indicator Based Evolutionary Algorithm for Uncertain Constrained Many‐Objective Optimization Problem With Interval Functions

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(3)

Опубликована: Янв. 13, 2025

ABSTRACT In practical engineering problems, uncertainties due to prediction errors and fluctuations in equipment efficiency often lead constrained many‐objective optimization problem with interval parameters (ICMaOPs). These problems pose significant challenges for evolutionary algorithms, particularly balancing solution convergence, diversity, feasibility, uncertainty. To address these challenges, a personalized indicator‐based algorithm (PI‐ICMaOEA) specifically designed ICMaOPs is proposed. The PI‐ICMaOEA integrates comprehensive quality indicator that encapsulates uncertainty, feasibility factors, converting multiple objectives high‐dimensional search spaces into single evaluative metric. Each factor's weight assigned based on individual performance, objective dimension, the evolving conditions of population. By prioritizing individuals excellent values mating environmental selection, effectively enhances selection pressure spaces. Comparative simulations demonstrate highly competitive, offering robust ICMaOPs.

Язык: Английский

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Enhancing constrained MOEA/D with direct mating using hybrid mating strategies and diverse crossover methods

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 27, 2025

Язык: Английский

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Constrained multi-objective evolutionary algorithm based on the correlation between objectives and constraints

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 94, С. 101903 - 101903

Опубликована: Март 14, 2025

Язык: Английский

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Data-driven oil production strategy selection under uncertainties

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113108 - 113108

Опубликована: Апрель 1, 2025

Язык: Английский

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Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework

Designs, Год журнала: 2024, Номер 8(6), С. 136 - 136

Опубликована: Дек. 20, 2024

This study evaluates and compares the computational performance practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic obstacle-rich environments. The Adaptive Multi-Objective Path Planning (AMOPP) framework is highlighted its ability to balance multiple objectives, including length, smoothness, collision avoidance, real-time responsiveness. Through experimental analysis, AMOPP demonstrates superior performance, with a 15% reduction length compared A*, achieving an average 450 m. Its angular deviation 8.0° ensures smoother trajectories than traditional methods like Genetic Algorithm Particle Swarm Optimization (PSO). Moreover, achieves 0% rate across all simulations, surpassing heuristic-based Cuckoo Search Bee Colony Optimization, which exhibit higher rates. Real-time responsiveness another key strength AMOPP, re-planning time 0.75 s, significantly outperforming A* RRT*. complexities each algorithm are analyzed, exhibiting complexity O(k·n) space O(n), ensuring scalability efficiency large-scale operations. also presents comprehensive qualitative quantitative comparison 14 using 3D visualizations, highlighting their strengths, limitations, suitable application scenarios. By integrating weighted optimization penalty-based strategies spline interpolation, provides robust solution UAV planning, particularly scenarios requiring smooth navigation adaptive re-planning. work establishes as promising real-time, efficient, safe operations

Язык: Английский

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Research on optimization of talent cultivation mode of industry-teaching integration for mechanical majors in higher vocational colleges based on genetic algorithm

Applied Mathematics and Nonlinear Sciences, Год журнала: 2024, Номер 9(1)

Опубликована: Янв. 1, 2024

Abstract Many researchers and educational institutions are committed to exploring the modes strategies of industry-education integration, which promotes close connection between education industrial needs by jointly carrying out teaching, research, practice activities. This paper proposes a multi-objective optimization strategy based on genetic algorithms, aims enhance optimize talent cultivation model through adjustments resource matching scheme teaching task allocation for integration. The mechanical specialty higher vocational college puts forward 10 kinds industry-teaching integration schemes tasks, combined with enterprise demand students’ ability, substitutes them into constructed model, solves algorithm arrive at optimal G, has an adaptability value 0.571, degree 6 under G is highest, means that strengthen professional knowledge about 6. Teaching specialized Additionally, satisfaction distribution graph from questionnaire data indicates students feel more content construction development during optimized mode results expert evaluation demonstrate industry not only yields outstanding outcomes in collaborative training (4.32 points) but also partially addresses shortage positions (4.13 points). However, it still requires enhancement environment (3.13

Язык: Английский

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A multi-objective mean–variance portfolio selection model combining sequential three-way decision and regret theory

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 126183 - 126183

Опубликована: Дек. 1, 2024

Язык: Английский

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