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Performance Analysis of the (1+1) EA on Mobile Robot Path Planning

IEEE ACCESS 2024年 12卷 159920-159934页

作者： Lai, Xinsheng Feng, Yulin Shaoxing Univ Dept Comp Sci & Engn Shaoxing 312000 Zhejiang Peoples R China

Path planning is a fundamental function of mobile robots that have been nowadays used in a variety of areas. evolutionary algorithms (EAs) have been experimentally showed to be efficient for the problem of mobile robot path planning, an NP-problem. However, there is no theoretical work about EAs on the problem. In this paper, to understand more about why EAs are efficient and to give some advice on how to design more efficient EAs for this problem, we perform the first theoretical analysis of the expected runtime of a simple EA called (1+1) EA with three different mutations for the mobile robot path planning problem in two constructed environments. In addition, anew method of producing a path from an uncomplete path is proposed, in which anew Insert process is proposed. Meanwhile, an obvious disadvantage of one of these three mutations is discovered during the expected runtime analysis in the first environment. Based on this discovery, an improved version of this mutation is proposed. In the second constructed environment, the improved mutation is proved to be superior to the others. In detail, the (1+1) EA with the improved mutation can find the shortest path in runtime O(n(5)), while the (1+1) EA with any one of the other mutations may be trapped in local optima. Experiments have been performed on the two constructed environments and six randomly generated environments with different sizes and varying obstacle densities.

关键词： Mobile robots Path planning Heuristic algorithms Runtime Planning Performance analysis Upper bound Stars Safety Polynomials Mobile robot path planning evolutionary algorithms performance analysis NP-hard problem

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Integrated Whale Swarm and Neuro-evolutionary Computing for Large-Scale Sparse Optimization Problems 2nd

Integrated Whale Swarm and Neuro-Evolutionary Computing for ...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Balakrishnan, Biju Shanthini, B. Amudha, R. St. Peter’s Institute of Higher Education and Research Avadi Tamilnadu Chennai India Hindusthan College of Engineering and Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789819995233

Sparse optimization problems at a large scale present considerable difficulties in diverse fields, such as machine learning, data mining, and signal processing. The aim is to identify the most efficient solutions within expansive search spaces, while constraining the number of non-zero variables. The present study aims to tackle this obstacle by introducing an innovative methodology that integrates whale swarm optimization (WSO) with neuro-evolutionary computing (NEC). The WSO algorithm draws inspiration from the social behavior of humpback whales and presents a proficient approach for investigating and utilizing the search space. The integration of evolutionary algorithms with neural networks by NEC has demonstrated potential in effectively addressing intricate problems. Through the simultaneous evolution of neural networks and optimization processes, the algorithm is capable of adapting and enhancing its search strategy progressively. The study aims to improve the efficiency of WSO in addressing optimization problems characterized by sparsity at a significant scale by incorporating techniques from neuro-evolutionary computing. The method under consideration entails the integration of neural networks into the WSO algorithm with the aim of directing and augmenting the exploration procedure. The study evaluates the efficacy of the hybrid algorithm in relation to established methodologies based on criteria such as the quality of solutions, rate of convergence, and computational efficiency. The results obtained from the experiment provide evidence to support the efficacy of the proposed approach. The hybrid algorithm effectively utilizes the sparsity structure inherent in the problems, resulting in expedited convergence and enhanced precision of the solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： evolutionary algorithms

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Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2024年第PT 1期282卷 136708页

作者： Arbia, Wassila Amar, Mohamed Kouider Adour, Lydia Amrane, Abdeltif Natl Polytech Sch Res Unit Engn & Environm URIE Algiers 16000 Algeria Univ Yahia Fares Medea Medea 26000 Algeria Univ Yahia Fares Medea Fac Technol Lab Biomat & Transport Phenomena LBMPT Medea 26000 Algeria Univ Algiers Fac Sci Dept Chem Algiers 16000 Algeria

The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields using multiple linear regression (MLR) and evolutionary algorithms. FT-IR analysis confirmed the presence of characteristic bands in the extracted samples, and SEM analysis further revealed the microfibrillar appearance of the chitin and the dense, non-porous structure of the chitosan. The Ant Lion Optimizer (ALO) was employed to select significant factors and optimize model parameters. A transformation was applied to capture nonlinear relationships, and the finetuned models showed improved predictive power, with p-values of 0.00203 for chitin and 0.00884 for chitosan. Multi-objective optimization (MOO) using the Adaptive Geometry Estimation-based Multi-Objective evolutionary Algorithm (AGE-MOEA) further identified significant factors for optimal yields, achieving 3 g of Arginine, 100 ml of culture medium volume, 7 to 11 days of incubation time, 0.2 to 1.76 ml of Oligochitin, 1.4 g of FeSO4, 1.5 g of K2HPO4, and 1 g of NaCl. Therefore, the integration of ALO and AGE-MOEA algorithms effectively modeled and optimized chitin and chitosan yields by maximizing biopolymer recovery, enabling significant industrial exploitation.

关键词： Chitin Chitosan evolutionary algorithms

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Shake table test and numerical model update for a viscous damper isolated cable-stayed bridge

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2024年 176卷

作者： Guo, Junjun Li, Mingke Wu, Yongmu Xiao, Yifeng Guan, Zhongguo Tongji Univ Dept Bridge Engn Shanghai 200092 Peoples R China China Highway Engn Consulting Grp Co Ltd Beijing 100089 Peoples R China

This study focused on the seismic responses and numerical model update of fluid viscous dampers (FVDs) isolated cable-stayed bridges excited by ground motions with different frequency contents. A 1/20 scaled three-span cable-stayed bridge was designed, constructed, and tested on a facility with four shake tables. Four ground motions with different frequency contents were adopted to excite the shake model. Then, a numerical model update framework of the shake table test was introduced, which combines the differential evolution (DE) algorithm and the parallel finite element computing technique. In addition, a time history response-based objective function was established that considered various responses of the bridge components. The shake table test results reveal that frequency contents affect the seismic responses of the cable-stayed bridge, and the ground motions with strong velocity pulses can amplify the displacements of the pylons and girder. In addition, the accelerations of the girder are relatively small and less sensitive to input ground motions. In general, the proposed method is efficient and accurate for updating the numerical model of the shake table test model.

关键词： Cable -stayed bridge Shake table test Ground motion characteristics Numerical model update evolutionary algorithms

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10 MW FOWT Semi-Submersible Multi-Objective Optimization: A Comparative Study of PSO, SA, and ACO

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ENERGIES 2024年第23期17卷

作者： Drabo, Souleymane Lai, Siqi Liu, Hongwei Feng, Xiangheng Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Ocean Acad Zhoushan 316021 Peoples R China Zhejiang Windey Co Ltd Hangzhou 310012 Peoples R China Zhejiang Key Lab Wind Power Generat Technol Hangzhou 310012 Peoples R China

The present study aims to carry out a comparative Multi-Objective Optimization (MOO) of a 10 MW FOWT semi-submersible using three different metaheuristic optimization techniques and a sophisticated approach for optimizing a floating platform. This novel framework enables highly efficient 3D plots, an optimization loop, and the automatic and comparative output of solutions. Python, the main interface, integrated PyMAPDL and Pymoo for intricate modeling and simulation tasks. For this case study, the ZJUS10 Floating Offshore Wind Turbine (FOWT) platform, developed by the state key laboratory of mechatronics and fluid power at Zhejiang University, was employed as the basis. Key criteria such as platform stability, overall structural mass, and stress were pivotal in formulating the objective functions. Based on a preliminary study, the three metaheuristic optimization algorithms chosen for optimization were Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions were evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm's convergence was meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performances of the optimized FOWT platforms were thoroughly compared both among themselves and with the original model, resulting in validation. Finally, the ACO algorithm delivered a highly effective solution within the framework, achieving reductions of 19.8% in weight, 40.1% in pitch, and 12.7% in stress relative to the original model.

关键词： renewable energy Floating Offshore Wind Turbine metaheuristic evolutionary algorithms Particle Swarm Optimization ant colony analysis Simulated Annealing

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Multi-objective optimization of airfoils with integral tubular high-pressure tanks for hydrogen storage

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AEROSPACE SCIENCE AND TECHNOLOGY 2024年 155卷

作者： Dexl, Florian Hauffe, Andreas Markmiller, Johannes TUD Dresden Univ Technol Inst Aerosp Engn D-01062 Dresden Germany

Reducing the environmental impact of air transport is one of today's most important challenges in aviation industry and research. A promising key enabler is the use of hydrogen as an alternative to fossil fuels. The development of hydrogen-powered aircraft poses new engineering challenges due to its low volumetric energy density requiring high-pressure or cryogenic storage. If the volume in the wing shall be further used for storing hydrogen under high pressure, new demands arise to airfoil design. The present work focuses on this issue by presenting a multi-objective optimization approach aiming for airfoils with both low drag and high volume for internal tubular high-pressure tanks. This allows to directly address the new design objective and to find novel airfoil shapes providing the best compromise between aerodynamic efficiency and high storage volume for pressurized hydrogen. The resulting optimization problem is solved using evolutionary algorithms. For an efficient aerodynamic evaluation, the open source viscous-inviscid panel method XFOIL is used. An application example, based on the flight conditions of a general aviation aircraft, demonstrates the applicability of the method. Comparisons of the resulting aerodynamic characteristics obtained by XFOIL with RANS simulations confirm the feasibility of the results.

关键词： Airfoils Aerodynamic optimization Pressure tanks Hydrogen storage evolutionary algorithms Class-shape transformation

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Hyperbolic Sine Optimizer: a new metaheuristic algorithm for high performance computing to address computationally intensive tasks

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 6703-6772页

作者： Thapliyal, Shivankur Kumar, Narender Doon Univ Dept Comp Sci Dehra Dun Uttarakhand India

In recent decades, the demand for optimization techniques has grown due to rising complexity in real-world problems. Hence, this work introduces the Hyperbolic Sine Optimizer (HSO), an innovative metaheuristic specifically designed for scientific optimization. Unlike conventional approaches, HSO takes a unique approach by engaging individual members of the population, ensuring a comprehensive exploration of solution spaces. Employing distinctive exploration and exploitation phases, coupled with hyperbolic sinh\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$sinh$$\end{document} function convergence, the optimizer enhances speed, simplify parameter adjustment, alleviates slow convergence, and demonstrates efficiency in high-dimensional optimization. This approach is designed to tackle optimization challenges and enhance adaptability in unpredictable real-world scenarios. The evaluation of HSO's performance unfolds through four distinct testing phases. Initially, a set of 65 widely recognized benchmark functions is employed. These functions cover both unimodal and multi-modal varieties across dimensions of 30, 100, 500, and 1000, including fixed-dimensional functions, to comprehensively assess the exploration, exploitation, local optima avoidance, and convergence capabilities of the proposed algorithm. The results of the HSO algorithm are then compared to those of 15 state-of-the-art metaheuristic algorithms and 8 recently published algorithms. Secondly, HSO's performance is assessed in comparison with the benchmark suite from the Institute of Electrical and Electronics Engineers (IEEE) Congress on evolutionary Computation (CEC). This suite includes 15 benchmark functions for CEC-2015 and an additional 30 benchmark functions for CEC-2017. During the third phase, HSO tackles seven real-world

关键词： Metaheuristics Hyperbolic Sine Optimizer HSO Optimization evolutionary algorithms Nature inspired algorithms

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Large-Scale WSNs Resource Scheduling Algorithm in Smart Transportation Monitoring Based on Differential Ion Coevolution and Multi-Objective Decomposition

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 829-838页

作者： Zhang, Lei Liu, Tingting Ding, Xuefeng China Three Gorges Univ Hubei Prov Engn Technol Res Ctr Construct Qual Te Yichang 443002 Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China China Three Gorges Univ Sch Econ & Management Yichang 443002 Peoples R China

At present, wireless sensor networks (WSNs) play an important role in collecting and processing information in smart transportation monitoring. Inevitably, the performance of the resource scheduling algorithm directly determines the quality of service of WSNs. In this paper, we present a novel large-scale resource scheduling algorithm of WSNs based on differential ion coevolution and multi-objective decomposition (DIC-MOD) to optimize the performance of WSNs. We first introduce a certain number of mobile nodes with higher configuration into WSNs and consider them as relay nodes to strengthen the balance of energy consumption in the entire WSNs. Subsequently, we build a multi-index service quality evaluation model, including coverage, connectivity, energy efficiency and the number of nodes required to work, to characterize the comprehensive performance of WSNs. Afterward, to optimize the above complex model effectively, we propose a multi-objective resource scheduling algorithm, in which a differential ion coevolution strategy and a fast individual selection strategy based on multi-objective decomposition optimization are proposed in specific. Compared with other state-of-the-art algorithms, the experimental results finally show that the performance of WSNs on multiple indicators obtained by the proposed algorithm has been improved considerably.

关键词： Wireless sensor networks Monitoring Wireless communication Smart transportation Optimization Scheduling algorithms Relays Multi-objective optimization evolutionary algorithms wireless sensor networks resource scheduling smart transportation

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A novel differential evolutionary algorithm based on population feedback information 4

A novel differential evolutionary algorithm based on populat...

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4th International Conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Yuan, Dongfeng An, Qixun School of Digital Media and Art Design Nanyang Institute of Technology Changjiang Road Henan Nanyang473004 China Zhengzhou Foreign Language School Henan Zhengzhou450001 China

ISBN: (纸本)9781510686106

The differential evolution algorithm has rich, successful experience in parameter settings. How to reasonably control strategies and parameters and effectively utilize feedback information from individuals in the population has become a hot topic of concern. In this paper, an adaptive differential evolution algorithm based on population feedback information (TtDE) is proposed. It uses multi-subpopulation selection methods to guide the direction of evolution. TtDE adopts a framework that combines multi-strategies and multi-parameter sets. One set of fixed strategies and parameter selections is the DE/Best/2 and adaptive parameters, and this method can utilize feedback information from individuals in the population effectively. In addition, during population iteration, the mutant subpopulation may contain better information about individuals than the test subpopulation, and the population diversity reasonably avoids early population convergence or stagnation. The proposed TtDE was evaluated on 23 testing functions of the CEC2005 benchmark suite, and the results showed that it is more competitive than multiple classical DE variants. © 2024 SPIE.

关键词： evolutionary algorithms Genetic algorithms Adaptive control evolutionary optimization Data transmission Fuzzy logic Intelligence systems Lithium Statistical analysis

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Automated design of CNN architecture based on efficient evolutionary search

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NEUROCOMPUTING 2022年 491卷 160-171页

作者： Xie, Yirong Chen, Hong Ma, Yongjie Xu, Yang Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Peoples R China

evolutionary Neural Architecture Search (ENAS) is a promising method for the automated design of deep network architecture, which has attracted extensive attention in the field of automated machine learning. However, the existing ENAS methods often need a lot of computing resources to design CNN architecture automatically. In order to achieve efficient and automated design of CNNs, this paper focuses on two aspects to improve efficiency. On the one hand, efficient CNN-based building blocks are introduced to ensure the effectiveness of the generated architectures and a triplet attention mechanism is incorporated into the architectures to further improve the classification performance. On the other hand, a random forest-based performance predictor is used in the fitness evaluation to reduce the amount of computation required to train each individual from scratch. Experimental results show that the proposed algorithm can significantly reduce the computational resources required and achieve competitive classification performance on the CIFAR dataset. Also, the architecture designed for the traffic sign recognition task exceeds the accuracy of manual expert design.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Convolutional neural networks Neural Architecture Search evolutionary algorithms

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