2022 IEEE Congress on Evolutionary Computation (CEC)最新文献_第6页

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

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

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

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

An adaptive variant of jSO with multiple crossover strategies employing Eigen transformation 基于特征变换的jSO多交叉策略自适应变体

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870378

Patrik Kolenovsky, P. Bujok

引用次数: 2

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

Generative Optimisation of Resilient Drone Logistic Networks 弹性无人机物流网络的生成优化

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870306

G. Filippi, M. Vasile, E. Patelli, M. Fossati

引用次数: 2

A multitasking surrogate-assisted differential evolution method for solving bi-level optimization problems 求解双层优化问题的多任务代理辅助差分进化方法

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870241

Igor L. S. Russo, H. Barbosa

{"title":"A multitasking surrogate-assisted differential evolution method for solving bi-level optimization problems","authors":"Igor L. S. Russo, H. Barbosa","doi":"10.1109/CEC55065.2022.9870241","DOIUrl":"https://doi.org/10.1109/CEC55065.2022.9870241","url":null,"abstract":"Bi-level programming (BLP) is a hierarchical decision-making problem in which part of the constraints is determined by solving other optimization problems. Classic op-timization techniques cannot be applied directly, while standard metaheuristics often demand high computational costs. The transfer optimization paradigm uses the experience acquired when solving one optimization problem to speed up a distinct but related task. In particular, the multitasking technique ad-dresses two or more optimization tasks simultaneously to explore similarities and improve convergence. BLPs can benefit from multitasking as many (potentially similar) lower-level problems must be solved. Recently, several studies used surrogate methods to save expensive upper-level function evaluations in BLPs. This work proposes an algorithm based on Differential Evolution supported by transfer optimization and surrogate models to solve BLPs more efficiently. Experiments show a reduction of up to 86% regarding the number of function evaluations of the upper-level problem while achieving similar or superior accuracy when compared to state-of-the-art solvers.","PeriodicalId":153241,"journal":{"name":"2022 IEEE Congress on Evolutionary Computation (CEC)","volume":"99 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":"124088359","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: 3

A Comparative Study on Evolutionary Algorithms and Mathematical Programming Methods for Continuous Optimization 连续优化的进化算法与数学规划方法的比较研究

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870359

Ye Tian, Haowen Chen, Xiaoshu Xiang, Hao Jiang, Xing-yi Zhang

引用次数: 1

Towards Hexapod Gait Adaptation using Enumerative Encoding of Gaits: Gradient-Free Heuristics 基于步态枚举编码的六足动物步态自适应:无梯度启发式

2022 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2022-07-18 DOI: 10.1109/CEC55065.2022.9870257

V. Parque

引用次数: 0