Multi-objective optimization of hydraulic shovel using evolutionary algorithm

Xu, Gongyue; Feng, Zemin; Guo, Erkuo; Cai, Changwang; Ding, Huafeng

doi:10.1016/j.autcon.2022.104486

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…Geometrical or operational parameters of hydraulic systems can also be optimized through multiple swarm methods. Xu [63] used twelve multi-objective and many-objective evolutionary algorithms to solve the optimization problem of a hydraulic shovel. The optimization resulted in designing a TriRocker hydraulic shovel and building an 85-ton prototype.…”

Section: Swarm and Neural Optimizationmentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.

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Section: Swarm and Neural Optimizationmentioning

confidence: 99%

Artificial Intelligence Methods in Hydraulic System Design

Filo

2023

Energies

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“…In practical science and engineering applications, problems involving multiple conflicting objectives that need to be optimized at the same time are often referred to as multi-objective optimization problems. For example, urban bus route problem [13], shop scheduling problem [23], multi-agent system optimization problem [12] and engineering vehicle mechanical design [27]. The objective functions in the multi-objective optimization problem conflict with each other, and almost no single solution can satisfy all the objectives at the same time, so it is necessary to make trade-offs between different objectives.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Strategy Guided Multi-Objective Artificial Physical Optimizer Algorithm

Sun,

Guo,

Zhang

et al. 2024

ITC

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Artificial physical optimizer (APO), as a new heuristic stochastic algorithm, is difficult to balance convergence and diversity when dealing with complex multi-objective problems. This paper introduces the advantages of R2 indicator and target space decomposition strategy, and constructs the candidate solution of external archive pruning technology selection based on APO algorithm. A hybrid strategy guided multi-objective artificial physical optimizer algorithm (HSGMOAPO) is proposed. Firstly, R2 indicator is used to select the candidate solutions that have great influence on the convergence of the whole algorithm. Secondly, the target space decomposition strategy is used to select the remaining solutions to improve the diversity of the algorithm. Finally, the restriction processing method is used to improve the ability to avoid local optimization. In order to verify the comprehensive ability of HSGMOAPO algorithm in solving multi-objective problems, five comparison algorithms were evaluated experimentally on standard test problems and practical problems. The results show that HSGMOAPO algorithm has good convergence and diversity in solving multi-objective problems, and has the potential to solve practical problems.

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“…Yu et al performed finite element analysis and optimization on the structure of the excavator's boom [12]. Xu et al employed a multiobjective optimization algorithm to optimize the digging force, working range, and other objectives for a hydraulic shovel [13]. Yu et al designed a lightweight bucket with enhanced structural strength based on uncertain loads [14].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction

Lu,

Lin,

Chen

et al. 2023

Energies

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To address the limitation of existing excavator optimization methods, which primarily focus on the force performance while neglecting energy consumption and fail to realize environmentally friendly and low-carbon designs, this paper proposes a new multi-objective collaborative optimization method for an excavator to reduce energy consumption during the working process while maintaining optimal performance. By formulating a mathematical model that quantifies the energy consumption during the working process, this paper optimizes the working conditions by analyzing the energy consumption characteristics under typical working conditions. To overcome the limitation of existing linear weighting methods in multi-objective optimization, such as imbalanced optimization quality among sub-objectives, this paper proposes a new modeling approach based on the loss degree of sub-objectives. A multi-objective collaborative optimization model for the excavator with reduced energy consumption is established, and a corresponding multi-objective collaborative optimization algorithm is developed and applied to achieve optimal solutions for sub-objectives. The optimization results demonstrate that applying the new multi-objective collaborative optimization method to the excavator achieves better optimization quality than traditional methods. It also provides a more balanced improvement in the optimization values of each sub-objective, resulting in a significant reduction in the energy consumption of the excavator during operation.

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Multi-objective optimization of hydraulic shovel using evolutionary algorithm

Cited by 10 publications

References 45 publications

Artificial Intelligence Methods in Hydraulic System Design

Artificial Intelligence Methods in Hydraulic System Design

A Hybrid Strategy Guided Multi-Objective Artificial Physical Optimizer Algorithm

A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction

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