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Humanoid robot arm performance optimization using multi objective evolutionary algorithm

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2014年第4期12卷 870-877页

作者： Mohamed, Zulkifli Kitani, Mitsuki Kaneko, Shin-ichiro Capi, Genci Toyama Univ Fac Engn Dept Elect & Elect Syst Engn Toyama 9308555 Japan Univ Teknol MARA Fac Mech Engn Shah Alam 40450 Selangor Malaysia Toyama Natl Coll Technol Dept Elect & Control Syst Engn Toyama Japan

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria;minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.

关键词： Genetic algorithm mobile humanoid robot multi-objective evolutionary algorithm robot arm motion generation

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Exploring cluster-dependent isomorphism in multi-objective evolutionary optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 263卷

作者： Zheng, Wei Sun, Jianyong Shanxi Univ Sch Comp & Informat Technol 92 Wucheng St Taiyuan 030006 Shanxi Peoples R China Xi An Jiao Tong Univ Sch Math & Stat 28 West Xianning Rd Xian 710049 Shaanxi Peoples R China

In this paper, a Two-Round learning-based algorithm for Continuous box-constrained multi-objective evolutionary optimization (TRACE) under the decomposition framework is proposed, in which the isomorphism relationship between the clustered Pareto Front and Pareto solution set is explored and anew time-varying adaptive crossover operator is developed. The learning process involves two stages. In the first stage, the K-means is applied to cluster the population of objective vectors. By exploring the property of cluster-dependent isomorphism between the objective space and the decision space, a parent individual for each individual is selected from the corresponding clusters in the decision space. The time-varying adaptive crossover operator is then used together with the classical polynomial mutation operator to generate anew solution based on the selected parent individuals. As part of the environmental selection process, the K-means is applied again to the combination of parent and offspring individuals in the objective space to assist in the selection of suitable solutions for each decomposed subspace. TRACE is compared with 11 state-of-the-art multi-objective evolutionary algorithms on totally 43 difficult problems with different characteristics. Furthermore, TRACE is compared with three promising multi-objective evolutionary algorithms for community detection in attribute networks. Extensive experiments show that TRACE significantly outperforms the compared algorithms inmost instances.

关键词： multi-objective evolutionary algorithm Clustering Decomposition Adaptive evolutionary operator

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Learning-guided bi-objective evolutionary optimization for green municipal waste collection vehicle routing

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JOURNAL OF CLEANER PRODUCTION 2025年 501卷

作者： Liao, Shubing Xu, Yixin Niu, Yunyun Cao, Zhiguang China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China Singapore Management Univ Sch Comp & Informat Syst Singapore 178902 Singapore

Waste management has emerged as a critical issue in modern society, where vehicles are scheduled to visit multiple locations for waste collection and transport. This study focuses on a key problem in waste management: route optimization of waste collection vehicles, and formulate it as a bi-objective vehicle routing problem with stochastic demand (VRPSD), aiming to minimizing both total costs and carbon emissions. Although previous studies have significantly advanced our understanding of solving similar problems, the lack of real-world data and limited problem-solving capabilities still restrict the practical applicability of existing methods. To bridge this research gap, this study designed a regression model using nighttime light data to efficiently and accurately generate two real-case instances in Beijing. Furthermore, a multi-objective evolutionary algorithm integrates Efficient Non-dominated Sorting with Sequential Search and a one-dimensional convolutional neural network (MEAE1C) is proposed to solve the VRPSD problem. MEAE1C integrates a CNN evolver to leverage knowledge from current high-quality solutions to guide subsequent population evolution. Experimental results confirm the superior accuracy in estimates of waste generation, and extensive simulations on benchmark datasets and realcase scenarios consistently demonstrate the superiority of MEAE1C over existing methods. The above results highlight the practical feasibility of the proposed methods in addressing real-world municipal waste management challenges.

关键词： Almeida Municipal waste collection Vehicle routing problem multi-objective evolutionary algorithm 1DCNN Nighttime light NPP-VIIRS

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Sports mental health monitoring based on multi-objective evolutionary algorithms in the Internet of Things

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SOFT COMPUTING 2023年

作者： Li, Xiaoyan Shi, Yunjing Gao, Qiang Shijiazhuang Univ Appl Technol Dept Phys Educ Shijiazhuang 050081 Hebei Peoples R China Hebei Univ Chinese Med Dept Psychol Shijiazhuang 050200 Hebei Peoples R China

With the increasing popularity of sports such as football and basketball, many policy reforms are striving to strengthen the education of sports talents in China. The release of relevant policies can further promote the development of sports and enable more and more people to promote physical and mental health through sports activities. The emergence of the Internet of Things, especially the emergence of intelligent wearable devices, has brought many conveniences to the long-term detection and analysis of human health and motion parameters. By measuring human health and sports information, it is possible to quantitatively monitor the amount of exercise and physical health level of athletes, in order to achieve more effective training effects. In addition, for complex multi-objective problems, multi-objective evolutionary algorithms can effectively solve problems that are difficult to handle, suppress, and balance targets. Therefore, this article uses a multi-objective evolutionary algorithm to complete mental health monitoring based on the Internet of Things. It monitors the impact of sports on students' mental health, compares their mental health before and after the experiment, and analyzes the data changes before and after the experiment to summarize the impact of sports on students' mental health. The main characteristics of multi-objective evolutionary algorithm are excellent convergence and diversity. This article uses its main characteristics to real-time monitor the mental health level of college students during exercise through the Internet of Things, opening up a new way to improve their mental health.

关键词： multi-objective evolutionary algorithm Internet of Things Sports Mental health

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An evolutionary multitasking Method for High-Dimensional Receiver Operating Characteristic Convex Hull Maximization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第2期8卷 1699-1713页

作者： Cheng, Fan Shu, Shengda Zhang, Lei Tan, Ming Qiu, Jianfeng Anhui Univ Inst Informat Mat Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China

Maximizing receiver operating characteristic convex hull (ROCCH) is a hot research topic of binary classification, since it can obtain good classifiers under either balanced or imbalanced situation. Recently, evolutionary algorithms (EAs) especially multi-objective evolutionary algorithms (MOEAs) have shown their competitiveness in addressing the problem of ROCCH maximization. Thus, a series of MOEAs with promising performance have been proposed to tackle it. However, designing a MOEA for high-dimensional ROOCH maximization is much more challenging due to the "curse of dimension". To this end, in this paper, an evolutionary multitasking approach (termed as EMT-ROCCH) is proposed, where a low-dimensional ROCCH maximization task T-a is constructed to assist the original high-dimensional task T-o. Specifically, in EMT-ROCCH, a low-dimensional assisted task T-a is firstly created. Then, two populations, P-a and P-o, are used to evolve tasks T-a and T-o, respectively. During the evolution, a knowledge transfer from P-a to P-o is designed to transfer the useful knowledge from P-a to accelerate the convergence of P-o. Moreover, a knowledge transfer from P-o to P-a is developed to utilize the useful knowledge in P-o to repair the individuals in P-a, aiming to avoid P-a being trapped into the local optima. Experiment results on 12 high-dimensional datasets have shown that compared with the state-of-the-arts, the proposed EMT-ROCCH could achieve ROCCH with higher quality.

关键词： evolutionary computation evolutionary multitasking ROC convex hull multi-objective evolutionary algorithm knowledge transfer classification

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Q-learning assisted multi-objective evolutionary optimization for low-carbon scheduling of open-pit mine trucks

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SWARM AND evolutionary COMPUTATION 2025年 92卷

作者： Huang, Yao Guo, Yinan Chen, Guoyu Wei, Hong Zhao, Xiaoxiao Yang, Shengxiang Ge, Shirong China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China China Univ Min & Technol Beijing Inner Mongolia Res Inst Ordos 017010 Peoples R China Anhui Univ Sci & Technol Sch Artificial Intelligence Huainan 232001 Peoples R China De Montfort Univ Inst Artificial Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England

Mine trucks, as the core equipment of discontinuous open-pit mining technology, account for high transportation costs and vast quantities of greenhouse gases. In order to improve transportation efficiency and decrease carbon emissions, rationally scheduling shovel-truck pairs is a necessary issue. Previous studies give less consideration on carbon emissions of trucks that varies with road and driving conditions. To overcome the shortage, a constraint bi-objective optimization model is built for low-carbon scheduling problem of open-pit mine trucks, in which minimizing both idle time and carbon emissions of trucks are taken as the objectives. More especially, the limits on working time, traffic volume and the number of trucks are modeled as the constraints. Carbon emissions is formulated by multistage nonlinear function that takes road condition, load and driving state of trucks into account. As the problem-solver, Q-learning assisted multi-objective evolutionary algorithm is put forward. Four evolution states are defined by analyzing the improvement on feasibility and convergence of the population, and four problem-specific evolution operators are designed to meet different demands of the evolution. Q-learning-based selection strategy is proposed to select the most appropriate operator, with the purpose of improving the evolution efficiency. Experimental results on the real-world instances expose that the proposed algorithm outperforms the other state-of-the-art algorithms significantly.

关键词： Q-learning Scheduling Low-carbon Open-pit mine truck multi-objective evolutionary algorithm

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A New evolutionary multitasking algorithm for High-Dimensional Feature Selection

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IEEE ACCESS 2024年 12卷 89856-89872页

作者： Liu, Ping Xu, Bangxin Xu, Wenwen Anhui Elect Power Design Inst Co Ltd China Energy Engn Grp Hefei 230601 Peoples R China

Feature selection (FS) is an important dimension reduction technique in practical applications, which has been widely studied during the past decades. Although a large number of FS algorithms have been proposed and shown the promising performance, most of them face with the challenge of "curse of dimensionality". To this end, inspired by evolutionary multitasking (EMT), in this paper, a VariAble multiTasking-based multi-objective evolutionary algorithm, named VAMT-MOEA, is proposed for high-dimensional FS. For the existing EMT-based FS algorithms, they adopt the single or fixed assisted tasks to solve the high-dimensional FS problem (namely the original task). Once they trap into the local optima, it is difficult for them to provide valuable knowledge. Different from them, the proposed algorithm employs the variable multitasking scheme to achieve the feature subsets with high quality. The assisted task is adjusted with the changes of the weights in the evolution, where the weight measures the importance of each feature. Specifically, a variable-weight adjustment strategy is proposed to adjust the assisted task, aiming to overcome the loss of diversity during the evolution. Additionally, a novel knowledge transfer strategy is suggested, where the best and the worst solutions are used to implement the positive knowledge transfer between the assisted task and the original task. Finally, an initialization strategy is designed to generate an initial assisted task with competitiveness. The experimental results on 10 high-dimensional datasets, whose dimension ranges from 2,000 to 19,993, demonstrate the superiority of the proposed VAMT-MOEA in terms of the classification error, the number of selected features and the running time. To be specific, compared with five EA-based FS algorithms, the proposed VAMT-MOEA can achieve the feature subsets with lower classification error and smaller number of features. Moreover, the running time of different algorithms also reveal that

关键词： Task analysis multitasking Feature extraction Statistics Social factors Optimization Knowledge transfer evolutionary computation evolutionary multitasking feature selection high-dimensional data multi-objective evolutionary algorithm knowledge transfer

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Gaussian Adaptive Strategy Based multi-objective evolutionary Optimization for Path Planning on Uneven Terrains

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第1期9卷 539-546页

作者： Zheng, Hui Lu, Yixiao Jie, Jing Hou, Beiping Zhang, Miao Zhang, Yitao Zhejiang Univ Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

To enable mobile robots to safely and effectively accomplish path planning tasks on uneven terrains, this letter proposes a Gaussian adaptive strategy-based multi-objective evolutionary optimization. Firstly, path solutions are generated by employing spline interpolation on the digital elevation model of the terrain map. Subsequently, a multi-objective optimization function is constructed, considering parameters such as path length, uniformity, slope, and relief. These parameters are computed using the elevation values of the terrain. Secondly, an evolutionary optimization algorithm based on a Gaussian adaptive strategy is introduced. This strategy ensures the uniform distribution, adaptability, and size stability of the reference points set. Additionally, it controls the selection rate of non-dominant contribution points during the optimization process. Finally, experimental results conducted in five different real environments demonstrate the suitability of the proposed algorithm for solving path planning problems on uneven terrain.

关键词： Path planning Optimization Interpolation Heuristic algorithms Task analysis Particle swarm optimization Statistics Agricultural automation motion and path planning optimization and optimal control multi-objective evolutionary algorithm uneven terrains

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Optimization of cutting parameters using multi-objective evolutionary algorithm based on decomposition

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JOURNAL OF VIBROENGINEERING 2013年第2期15卷 833-844页

作者： Fu Tao Liu Weijun Zhao Jibin Chinese Acad Sci Shenyang Inst Automat Beijing 100864 Peoples R China

High-strength die steel is widely used for cutting, stamping and molding because of its advantages of high hardness, high toughness and abrasion resistance. However, its efficiency in cutting has always been difficult. In a complex machining process, the optimization of cutting parameters has a significant influence on the stability and quality of products. In this study, an orthogonal experiment of NAK80 for milling is carried out with cutting speed, feed rate and depth of cut, all of which are considered as relevant variables. According to the experimental results, statistical models for cutting force and surface roughness are built using a regression method as objective functions. Superadded the theoretical formula of material removal rate, a multi-objective evolutionary algorithm based on decomposition is used to optimize the cutting parameters and a Pareto solution set is obtained finally.

关键词： NAK80 cutting parameters multi-objective evolutionary algorithm optimization

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A two-phase framework of locating the reference point for decomposition-based constrained multi-objective evolutionary algorithms

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KNOWLEDGE-BASED SYSTEMS 2022年 239卷 107933-107933页

作者： Peng, Chaoda Liu, Hai-Lin Goodman, Erik D. Tan, Kay Chen South China Agr Univ Coll Math & Informat Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Math & Stat Guangzhou Guangdong Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI USA Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Reference point is a key component in decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs). A proper way of updating it requires considering constraint-handling techniques due to the existing constraints. However, it remains unexplored in this field. To remedy this issue, this paper firstly designs a set of benchmark problems with difficulties that a CMOEA must update the reference point effectively. Then a two-phase framework of locating the reference point is proposed to enhance performance of the current decomposition-based CMOEAs by evolving two populations- the main and external population. At the first phase, the external population evolves along with the main population to identify the approximate locations of the constrained and unconstrained Pareto front (PF). At the second phase, a location estimation mechanism is designed to estimate the best fit reference point between the two PFs for the main population by evolving the external population. Besides, a replacement strategy is used to drive the main population to the promising regions. Experimental studies are conducted on 26 benchmark problems, and the results highlight the effectiveness of the proposed framework. (c) 2021 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Referent point Decomposition Constraint-handling technique

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