Dimensional Synthesis for Multi-Linkage Robots Based on a Niched Pareto Genetic Algorithm

Wu, Hu; Li, Xinning; Yang, Xiaobo

doi:10.3390/a13090203

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…The algorithm hyper-parameters for each evolutionary algorithm are detailed as follows: SPEA2: Population Size (N): 150; Maximum Number of Generations (GM): 500; Tournament Size (T): 3 [78]; Archive Size (K): 2 N; Crossover Rate (CR): 1; Mutation Rate (MR): 0.01; Archive Truncation (Epsilon): 3; [79]. NPGA: N: 150; MR: 0.01; Niche Radius (σ): 150; GM: 500 [80]; CR: 1; T: 3 [81]. MOGA: N: 200; CR: 1; MR: 0.1; T: 4; Elitism: 2; GM: 500 [64].…”

Section: The Proposed Framework For the Studymentioning

confidence: 99%

Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets

Almutairi

2024

Sustainability

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The conceptual fusion of smart city and sustainability indicators has inspired the emergence of the smart sustainable city (SSC). Given the early stage of development in this field, most SSC studies have been primarily theoretical. Notably, existing empirical studies have overlooked the crucial aspect of feature engineering in the context of SSC, despite its significance in advancing SSC initiatives. This paper introduces an approach advocating for feature subset selection to maximize prediction accuracy and minimize computational time across diverse SSC indicators encompassing socio-cultural, economic, environmental, and governance categories. The study systematically collected multiple datasets on SSC indicators, covering various themes within the SSC framework. Employing six carefully chosen multiple-objective evolutionary feature selection algorithms, the research selected feature subsets. These subsets were then utilized in modeling algorithms to predict SSC indicators. The proposal enhanced prediction accuracy for life expectancy, online shopping intentions, energy consumption, air quality, water quality, and traffic flow for a smart and sustainable city by minimizing the subset features. The findings underscore the efficacy of feature subset selection in generating minimal features, thereby enhancing both prediction accuracy and computational efficiency in the realm of SSC indicators. For researchers aiming to develop sustainable systems for real-time data monitoring within SSC, the identified subset features offer a valuable resource, negating the necessity for extensive dataset collection. The provided SSC datasets are anticipated to serve as a catalyst, inspiring researchers to embark on empirical studies that explore SSC development from diverse perspectives, ultimately contributing to a more profound understanding of the SSC dynamics.

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Section: The Proposed Framework For the Studymentioning

confidence: 99%

Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets

Almutairi

2024

Sustainability

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“…With the improvement of multiobjective optimization theory and swarm intelligence evolutionary algorithms, multiobjective optimization algorithms have become an important tool for researching multiobjective optimization problems due to their advantages of good operation parallelism, strong global search ability, wide applicability, robustness, and fast convergence speed. Currently, common multiobjective optimization algorithms include vector evaluated genetic algorithm (VELA) [52], multiobjective genetic algorithm (MOGA) [53], niched Pareto genetic algorithm (NPGA) [54], nondominated sorting genetic algorithm (NSGA), Nondominated Sorting Genetic Algorithm-II (NSGA-II) [55], strength Pareto evolutionary algorithm (SPEA) [56], and Strength Pareto Evolutionary Algorithm-II (SPEA-II) [57]. First, NSGA-II adopts the nondominated sorting technique, which can generate a set of nondominated solution sets and provide more options.…”

Section: Advances In Civil Engineeringmentioning

confidence: 99%

Research on Green Building Design Optimization Based on Building Information Modeling and Improved Genetic Algorithm

Liu,

Ouyang,

Huang

et al. 2024

Advances in Civil Engineering

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The energy consumption of the construction industry has been increasing year by year, posing a huge challenge to China’s dual carbon goals of peaking carbon emissions and achieving carbon neutrality. The Chinese construction industry has huge potential for energy conservation and emission reduction, and the government has therefore put forward requirements for constructing green buildings and formulated strict evaluation standards. The carbon emissions of the construction industry involve various stages of the entire life cycle and are closely related to the green building design standards that meet the requirements. This article sets multiple objective functions based on the two dimensions of the carbon emissions of the entire life cycle of buildings and green building evaluation and uses the NSGA-II algorithm in genetic algorithms to optimize ten indicators selected from the two objectives. Based on this, building information modeling (BIM) modeling was carried out for an office building project in Southwest China, and energy consumption analysis and evaluation were conducted based on the project’s multidisciplinary model. The dialectical relationship between the carbon emissions of the entire life cycle of buildings and the green building evaluation values was discovered, and the optimized parameter combination scheme corresponding to the Pareto solution set was obtained, providing a reference for using improved genetic algorithms and BIM technology to optimize green building design.

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“…The aim is to minimize the robot's position error while ensuring maximum tolerance of the design parameters [27]. To enhance flexibility and reduce energy consumption, Wu (2020) proposed an integrated approach utilizing the niche Pareto genetic algorithm to optimize the size and joint angles of multi-link robots, providing a fresh perspective on the dimension synthesis of such robots [28]. Kavala (2022) used three algorithms based on population optimization, specifically genetic algorithms, particle swarm optimization, and differential evolution techniques, to realize the structural design and controller optimization of five-bar planar manipulators.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of Mechanism Parameters of Bipedal Robot Considering Full-Range Walking Energy Efficiency

Chen,

An,

Wang

et al. 2023

Applied Sciences

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Mechanism parameters of bipedal robots are crucial for achieving efficient locomotion in complex environments. Inspired by the human energy-efficient walking style, this paper proposes a novel concept of full-range walking energy efficiency and explores the optimal linkage mechanism within certain ranges of step length and walking speed for bipedal robots. First, a bipedal model incorporating an upper body is established for dynamic analysis. Next, an optimal walking gait subject to walking constraints is solved by considering the full-range energy efficiency. Further, an optimal linkage mechanism is investigated, and the influence of dynamic parameters on energy efficiency is analyzed. Finally, the push-off impulse, minimum ground support force, and walking torque features are discussed. It shows that the full-range walking energy efficiency can be lowered by reducing the ratio of leg mass, concentrating mass at the hip joint, decreasing the length of the upper body, or increasing the center of mass of the leg. In addition, efficient walking motion can be achieved by designing the coordination of positive hip joint torque and push-off impulse at the ankle. This paper can be used to guide the mechanism parameter optimization and efficient walking gait design of bipedal robots.

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Dimensional Synthesis for Multi-Linkage Robots Based on a Niched Pareto Genetic Algorithm

Cited by 7 publications

References 18 publications

Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets

Evolutionary Multi-Objective Feature Selection Algorithms on Multiple Smart Sustainable Community Indicator Datasets

Research on Green Building Design Optimization Based on Building Information Modeling and Improved Genetic Algorithm

Dynamic Optimization of Mechanism Parameters of Bipedal Robot Considering Full-Range Walking Energy Efficiency

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