2022 IEEE Congress on Evolutionary Computation (CEC)最新文献_第5页

Global and Local Area Coverage Path Planner for a Reconfigurable Robot 一种可重构机器人的全局和局部覆盖路径规划

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870308

S. Samarakoon, M. Muthugala, M. R. Elara

{"title":"Global and Local Area Coverage Path Planner for a Reconfigurable Robot","authors":"S. Samarakoon, M. Muthugala, M. R. Elara","doi":"10.1109/CEC55065.2022.9870308","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870308","url":null,"abstract":"Area coverage is essential for robots used in cleaning, painting, and exploration applications. Reconfigurable robots have been introduced to solve the area coverage limitation of fixed-shape robots. The existing global coverage algorithms of reconfigurable robots are limited to consideration of a limited set of predefined shapes for the reconfiguration and do not consider the exact geometrical shape of obstacles. Therefore, degraded coverage performance could be observed from the existing methods. On the other hand, the coverage methods that consider reconfiguring beyond a limited set of predefined shapes are limited to local coverage. Furthermore, these methods only consider a single reconfiguration for the coverage. Therefore, this paper proposes a novel coverage method for a reconfigurable robot consisting of both global and local path planners. The global path planner uses boustrophedon motion combined with the A * algorithm. The optimum grid positioning that maximizes the global coverage is determined through a Genetic Algorithm (GA). The local coverage planner performs continuous reconfig-uration of the robot to adequately cover obstacle zones while navigating through narrow spaces without collisions. A GA is used to determine the reconfiguration parameters of the robot at each instance of the local coverage. Simulation results confirm that the proposed method is effective in performing both global and local coverage path planning for improving the area coverage performance.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114398247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2022 Conference Proceedings 2022年会议记录

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/cec55065.2022.9870379

引用次数: 1

Avoiding strategic behaviors in the egalitarian social welfare under public resources and non-additive utilities 避免公共资源和非附加效用下的平均主义社会福利中的战略行为

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870315

Jonathan Carrero, Ismael Rodríguez, F. Rubio

引用次数: 0

Digital Twin Based Evolutionary Building Facility Control Optimization 基于数字孪生的建筑设施演化控制优化

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870207

Kohei Fukuhara, Ryo Kumagai, Fukawa Yuta, S. Tanabe, Hiroki Kawano, Yoshihiro Ohta, Hiroyuki Sato

引用次数: 2

Parameter Tuning and Control: A Case Study on Differential Evolution With Polynomial Mutation 参数整定与控制:以多项式突变的微分进化为例

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870219

Julian Blank, K. Deb

{"title":"Parameter Tuning and Control: A Case Study on Differential Evolution With Polynomial Mutation","authors":"Julian Blank, K. Deb","doi":"10.1109/CEC55065.2022.9870219","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870219","url":null,"abstract":"Metaheuristics are known to be effective for solving a broad category of optimization problems. However, most heuristics require different parameter settings appropriately for a problem class or even for a specific problem. Researchers address this commonly by performing a parameter tuning study (also known as hyper-parameter optimization) or developing a parameter control mechanism that changes parameters dynamically. Whereas parameter tuning is computationally expensive and limits the parameter configuration to stay constant throughout the run, parameter control is also a challenging task because all dynamics induced by various operators must be learned to make an appropriate adaptation of parameters on the fly. This paper investigates parameter tuning and control for a well-known optimization method - differential evolution (DE). In contrast to most existing DE practices, an additional individualistic evolutionary operator called polynomial mutation is incorporated into the offspring creation. Results on test problems with up to 50 variables indicate that mutation can be helpful for multi-modal problems to escape from local optima. On the one hand, the effectiveness of parameter tuning for a specific problem becomes apparent; on the other hand, its generalization capabilities seem to be limited. Moreover, a generic coevolutionary approach for parameter control outperforms a random choice of parameters. Recognizing the importance of choosing a suitable parameter configuration to solve any optimization problem, we have incorporated a standard implementation of both tuning and control approaches into a single framework, providing a direction for the evolutionary computation and optimization researchers to use and further investigate the effects of parameters on DE and other metaheuristics-based algorithms.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121613872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Performance of Composite PPSO on Single Objective Bound Constrained Numerical Optimization Problems of CEC 2022 复合PPSO在CEC 2022单目标有界约束数值优化问题上的性能

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870369

Bo Sun, Wei Li, Y. Huang

引用次数: 5

Joint Optimization of Topology and Hyperparameters of Hybrid DNNs for Sentence Classification 混合深度神经网络拓扑和超参数联合优化的句子分类

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870285

Brendan Rogers, N. Noman, S. Chalup, P. Moscato

{"title":"Joint Optimization of Topology and Hyperparameters of Hybrid DNNs for Sentence Classification","authors":"Brendan Rogers, N. Noman, S. Chalup, P. Moscato","doi":"10.1109/CEC55065.2022.9870285","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870285","url":null,"abstract":"Deep Neural Networks (DNN) require specifically tuned architectures and hyperparameters when being applied to any given task. Nature-inspired algorithms have been successfully applied for optimising various hyperparameters in different types of DNNs such as convolutional and recurrent for sentence classification. Hybrid networks, which contain multiple types of neural architectures have more recently been used for sentence classification in order to achieve better performance. However, the inclusion of hybrid architectures creates numerous possibilities of designing the network and those sub-networks also need fine-tuning. At present these hybrid networks are designed manually and various organisation attempts are noticed. In order to understand the benefit and the best design principle of such hybrid DNNs for sentence classification, in this work we used an Evolutionary Algorithm (EA) to optimise the topology and various hyperparameters in different types of layers within the network. In our experiments, the proposed EA designed the hybrid networks by using a single dataset and evaluated the evolved networks on multiple other datasets to validate their generalisation capability. We compared the EA-designed hybrid networks with human-designed hybrid networks in addition to other EA-optimised and expert-designed non-hybrid architectures.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122913652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of real-world supply routes by nature-inspired metaheuristics 基于自然启发的元启发式优化现实世界供应路线

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870405

P. Krömer, Vojtěch Uher

引用次数: 1

An Improved Ant Colony Approach for the Competitive Traveling Salesmen Problem 竞争旅行商问题的改进蚁群算法

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870414

Xinyang Du, Ruibin Bai, Tianxiang Cui, R. Qu, Jiawei Li

引用次数: 1

A Surrogate Model Assisted Estimation of Distribution Algorithm with Mutil-acquisition Functions for Expensive Optimization 一种具有多获取函数的代理模型辅助估计分布算法用于昂贵优化

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870436

Hao Hao, Shuai Wang, Bingdong Li, Aimin Zhou

{"title":"A Surrogate Model Assisted Estimation of Distribution Algorithm with Mutil-acquisition Functions for Expensive Optimization","authors":"Hao Hao, Shuai Wang, Bingdong Li, Aimin Zhou","doi":"10.1109/CEC55065.2022.9870436","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870436","url":null,"abstract":"The estimation of distribution algorithm (EDA) is an efficient heuristic method for handling black-box optimization problems since the ability for global population distribution modeling and gradient-free searching. However, the trial and error search mechanism relies on a large number of function evaluations, which is a considerable challenge under expensive black-box problems. Therefore, this article presents a surrogate assisted EDA with multi-acquisition functions. Firstly, a variable-width histogram is used as the global distribution model that focuses on promising areas. Next, the evaluated-free local search method improves the quality of new generation solutions. Fi-nally, model management with multiple acquisitions maintains global and local exploration preferences. Several commonly used benchmark functions with 20 and 50 dimensions are adopted to evaluate the proposed algorithm compared with several state-of-the-art surrogate assisted evaluation algorithms (SAEAs) and Bayesian optimization method. In addition, a rover trajectories optimizing problem is used to verify the ability to solve complex problems. The experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128707315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1