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28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering, HYDRO 2023

作者： Baladaniya, K.B. Patel, P.L. Timbadiya, P.V. Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat395007 India

ISBN: (纸本)9789819774739

The technique proposed in this paper, the self-adaptive population hybrid Rao algorithm (SAPHR), is intended to handle single- and multi-objective optimization problems. Unlike traditional methods, it does away with the necessity for metaphors and particular algorithm settings. According to improvements in the fitness value seen during the search process, the algorithm under consideration modifies the population size. The population is divided into three sub-population groups through a random process. Randomly assigned perturbation equations are provided to each sub-population, with each Equation guiding solutions to explore distinct regions within the search space. Evaluating the performance of the proposed algorithm involves using the well-known reservoir benchmark problem known as the discrete four-reservoir operation (DFRO) problem. The results of the algorithm are compared with other methods from the past study, revealing that the proposed method achieves superior outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Optimal Reservoir Operation Using Rao algorithms 28th

Optimal Reservoir Operation Using Rao Algorithms

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28th International Conference on Hydraulics, Water Resources, River and Coastal Engineering, HYDRO 2023

作者： Paliwal, V. Ghare, A.D. Mirajkar, A.B. Moze College of Engineering Pune India V.N.I.T Nagpur India

ISBN: (纸本)9789819774661

In this study, Rao algorithms (metaphor-less heuristic algorithms) are applied to derive the optimal operational releases of the Mula reservoir, Upper Godavari basin. The optimal operation releases for 75% probable inflows are compared to the optimal operational releases obtained from Jaya Algorithm (JA) and Non Linear Programming (NLP). Simulation studies are carried out using real inflows corresponding to 75% probable inflows. The results show that Rao algorithms provided better and comparable results to the result obtained from JA and NLP. Rao algorithms are found to be suitable in application to reservoir operation studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Accelerated Successive Convex Approximation for Nonlinear optimization-Based Control

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IEEE Transactions on Automatic Control 2025年

作者： Wu, Jinxian Dai, Li Dou, Songshi Xia, Yuanqing Beijing Institute of Technology School of Automation Beijing100081 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

The successive convex approximation (SCA) methods stand out as the viable option for nonlinear optimization-based control, as it effectively addresses the challenges posed by nonlinear (potentially non-convex) optimization problems by transforming them into a sequence of strongly convex subproblems. However, the current SCA algorithm exhibits a slow convergence rate, resulting in a relatively poor performance within a limited sample time. In this paper, the process of SCA is retreated as solving a fixed point nonlinear equation. By analyzing the derivative properties of this nonlinear equation, we introduce a Newton-based accelerated SCA (ASCA) algorithm designed to enhance the local convergence rate while inheriting all favorable characteristics of the SCA methods. Specifically, our algorithm offers the following benefits: (i) It is capable of effectively tackling nonlinear optimization-based control problems;(ii) It permits flexible termination with all generated intermediate solutions being feasible for the original nonlinear problem;and (iii) It guarantees convergence with locally superlinear convergence rate to the stationary point of the original nonlinear problem. Finally, we conduct experiments in a multi-agent collision avoidance scenario to show its validity. © 1963-2012 IEEE.

关键词： optimization algorithms

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Theoretical Approach on Assessing the Accuracy of the Shortest Path Non-Optimal Algorithm for 2-Dimensional Grids with Obstacles 24

Theoretical Approach on Assessing the Accuracy of the Shorte...

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8th International Conference on algorithms, Computing and Systems, ICACS 2024

作者： Mo, Chenghao Oyster River High School DurhamNH United States

ISBN: (纸本)9798400718304

In many applications such as urban navigation and robotics, finding the shortest path in a 2D grid is crucial but computationally expensive using traditional optimal algorithms like Floyd-Warshall or Dijkstra. These traditional algorithms guarantee to find the shortest path at the cost of time complexity, leading to a time-consuming computation, particularly for large-scale grids. Non-optimal algorithms that trade accuracy for speed have emerged to address the issue. However, the impact of grid obstacle density on the accuracy of the algorithms has not been well understood. This paper presents a theoretical framework for evaluating the accuracy of two non-optimal algorithms. By integrating theoretical analysis with extensive experimental data, this paper demonstrates how obstacle density influences algorithm performance, and proposes a methodology to select the best non-optimal algorithms based on the grid obstacle density. The theoretical framework has practical implications for applications requiring rapid path finding in complex environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： optimization algorithms

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Cooperative multi-function approach: A new strategy for autonomous ground robotics

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 134卷 361-373页

作者： Gul, Faiza Mir, Imran Abualigah, Laith Mir, Suleman Altalhi, Maryam Air Univ Dept Elect Engn Aerosp & Aviat Campus Attock 43600 Pakistan Air Univ Dept Avion Engn Aerosp & Aviat Campus Kamra Attock Pakistan Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Natl Univ Comp & Emerging Sci Peshawar Campus Islamabad Pakistan Taif Univ Dept Management Informat Syst Coll Business Adm POB 11099 Taif 21944 Saudi Arabia

The paper presents multi-objective optimization technique for the exploration of unknown space. Exploration refers to the building of an estimated finite map of the environment using sensor data. Conventionally, in robotics, the optimization is performed utilizing a single optimization technique with a particular objective function. This although simplifies the process but greatly compromise the map accuracy and exploration depth. Realizing this aspect, we present a new exploration technique with augmented objective functions, so as maximize the search area and map accuracy of the exploration space. The proposed framework termed as Multi-Objective Whale optimization Algorithm (MO-WOA), is based on bio-inspired Whale Optimizer. It starts with the initialization of the whale's population, which are referred to as way-points. These way-points are assumed to be constant once they are set in the initial stage/iterations. The next step involves the position update from the non-dominated way-points catered by the robots. The algorithm utilizing this optimization approach formulates the optimal way-points. The performance metrics are presented through extensive simulations. The results efficacy is then demonstrated by comparing the results of the proposed algorithm with those achieved from contemporary techniques namely Coordinated Multi-Robot Exploration (CME), conventional Whale Optimizer (WO) integrated with CME and Arithmetic optimization (AO). Results demonstrate that the proposed algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Multi robot Space exploration Coordinated multi-robot exploration optimization algorithms

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IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search optimization

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IEEE ACCESS 2021年 9卷 123448-123464页

作者： Fatani, Abdulaziz Abd Elaziz, Mohamed Dahou, Abdelghani Al-Qaness, Mohammed A. A. Lu, Songfeng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Umm Al Qura Univ Comp Sci Dept Mecca 24381 Saudi Arabia Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Univ Ahmed Draia Fac Sci & Technol LDDI Lab Adrar 01000 Algeria Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China

The great advancements in communication, cloud computing, and the internet of things (IoT) have opened critical challenges in security. With these developments, cyberattacks are also rapidly growing since the current security mechanisms do not provide efficient solutions. Recently, various artificial intelligence (AI) based solutions have been proposed for different security applications, including intrusion detection. In this paper, we propose an efficient AI-based mechanism for intrusion detection systems (IDS) in IoT systems. We leverage the advancements of deep learnings and metaheuristics (MH) algorithms that approved their efficiency in solving complex engineering problems. We propose a feature extraction method using the convolutional neural networks (CNNs) to extract relevant features. Also, we develop a new feature selection method using a new variant of the transient search optimization (TSO) algorithm, called TSODE, using the operators of differential evolution (DE) algorithm. The proposed TSODE uses the DE to improve the process of balancing between exploitation and exploration phases. Furthermore, we use three public datasets, KDDCup-99, NSL-KDD, BoT-IoT, and CICIDS-2017 to assess the performance of the developed method, which achieved higher accuracy compared to several existing approaches.

关键词： Feature extraction Intrusion detection Transient analysis Deep learning Convolutional neural networks Cloud computing Internet of Things Internet of Things (IoT) security cyberattack intrusion detection system feature selection optimization algorithms

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Precise modeling of PEM fuel cell using improved chaotic MayFly optimization algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第13期45卷 18754-18769页

作者： Shaheen, Mohamed A. M. Hasanien, Hany M. El Moursi, M. S. El-Fergany, Attia A. Future Univ Egypt Elect Engn Dept Cairo 11835 Egypt Ain Shams Univ Elect Power & Machines Dept Fac Engn Cairo Egypt Khalifa Univ Elect Engn & Comp Sci Dept Abu Dhabi U Arab Emirates Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt

The accuracy of the modeling of the fuel cell is important for achieving precise simulation results. This article presents a newly developed optimization method "Chaotic MayFly optimization algorithm" (CMOA) for obtaining the proton exchange membrane fuel cell (PEMFC) parameters. This research mainly targets an accurate modeling of the PEMFC that provides good match between the simulation results and those measured practically. In this regard, the I-V characteristics of the PEMFC's are non-linear, and there are seven design variables are considered because of the manufacturer's shortage in providing such information. The optimization problem formulated in this study is a non-linear problem. The objective function is mathematically expressed as the total squared error between the PEMFC terminal voltage measured in the laboratory vs the estimated terminal voltage from the simulation of the model. Since the metaheuristic optimization techniques are significantly influenced by the problem initialization, a new hybridization between the chaotic mapping and the MOA is employed to tackle the problem of the PEMFC design variables estimation and achieving better results. The CMOA is applied to find the best solution of the objective function that satisfies the preset conditions. The accurateness of the PEMFC approximated model is verified numerically using the optimal design variables. The simulation results are verified under various conditions of temperature and pressure. The estimated numerical results are compared with the measured data in case of many standard PEMFCs, such as Ballard, Mark V 5 kW, 500 W BCS, and 250 W stacks. The robustness of the proposed CMOA applied to the PEMFC model is also tested. The findings of the simulations of the proposed CMOA are compared with other findings obtained by other optimization methods. Applying the CMOA results in an accurate development of the PEMFC model.

关键词： fuel cell modeling optimization algorithms parameter determination polarization curves

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EAOA: An Enhanced Archimedes optimization Algorithm for Feature Selection in Classification

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IEEE ACCESS 2021年 9卷 120795-120814页

作者： Desuky, Abeer S. Hussain, Sadiq Kausar, Samina Islam, Md Akhtarul El Bakrawy, Lamiaa M. Al Azhar Univ Fac Sci Cairo 11754 Egypt Dibrugarh Univ Examinat Branch Dibrugarh 786004 Assam India Univ Kotli Azad Jammu & Kashmir Dept Comp Sci & IT Kotli Azad Kashmir 11100 Pakistan Khulna Univ Stat Discipline Sci Engn & Technol Sch Khulna 9208 Bangladesh

Feature selection plays a crucial role in order to mitigate the high dimensional feature space in different classification problems. The computational cost is reduced, and the accuracy of the classification is improved by reducing the dimension of feature space. Hence, in the classification task, finding the optimal subset of features is of utmost importance. Metaheuristic techniques have proved their efficacy in solving many real-world optimization issues. One of the recently introduced physics-inspired optimization methods is Archimedes optimization Algorithm (AOA). This paper proposes an Enhanced Archimedes optimization Algorithm (EAOA) by adding a new parameter that depends on the step length of each individual while revising the individual location. The EAOA algorithm is proposed to improve the AOA exploration and exploitation balance and enhance the classification performance for the feature selection issue in real-world data sets. Experiments were performed on twenty-three standard benchmark functions and sixteen real-world data sets to investigate the performance of the proposed EAOA algorithm. The experimental results based on the standard benchmark functions show that the EAOA algorithm provides very competitive results compared to the basic AOA algorithm and five well-known optimization algorithms in terms of improved exploitation, exploration, local optima avoidance, and convergence rate. In addition, the results based on sixteen real-world data sets ascertain that reduced feature subset yields higher classification performance when compared with the other feature selection methods.

关键词： Archimedes optimization algorithm classification feature selection machine learning metaheuristic algorithms optimization algorithms

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A Landscape-Aware Differential Evolution for Multimodal optimization Problems

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IEEE Transactions on Evolutionary Computation 2025年

作者： Lin, Guo-Yun Chen, Zong-Gan Liu, Chuanbin Jiang, Yuncheng Kwong, Sam Zhang, Jun Zhan, Zhi-Hui South China Normal University School of Computer Science Guangzhou510631 China Ministry of Education Center for Scientific Research and Development in Higher Education Institutes China Lingnan University Department of Computing and Decision Science Hong Kong Nankai University College of Artificial Intelligence Tianjin300350 China ERICA Hanyang University 15588 Korea Republic of

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this article, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual re-locating an already found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or an already found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution and distinction of the found peaks, which helps explore more peaks. The experiments are conducted on the widely-used benchmark MMOPs and multimodal nonlinear equation system problems. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performing recent algorithms and four winner algorithms in the IEEE CEC competitions for multimodal optimization. © 1997-2012 IEEE.

关键词： optimization algorithms

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An Overview of Interslice and Intraslice Resource Allocation in B5G Telecommunication Networks

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第4期19卷 5120-5132页

作者： Debbabi, Fadoua Jmal, Rihab Fourati, Lamia Chaari Aguiar, Rui Luis Univ Sousse ISITCom Hammam Sousse 4011 Tunisia Sfax Univ Digital Res Ctr Sfax CRNS Lab Signals Syst Artificial Intelligence Networks Sfax 3021 Tunisia Inst Telecomunicacoes Dept Elect Telecomunicacoes & Informat P-3810193 Aveiro Portugal Univ Jendouba Dept Comp Sci ISLAIB Beja 8100 Tunisia Inst Telecomunicacoes P-3810193 Aveiro Portugal

We are now in the first phase of the Fifth Generation (5G) network, and its full potential is still a long way to reach. Network operators and manufacturers are preparing to release new 5G end-users services and products. Each service must be performant to meet the need of users. Network Slicing (NS) is now one of the most popular 5G technologies in the research community. For network softwarization, Network Slicing affords flexibility and scalability with embedded Quality of Experience (QoE) and Quality of Services (QoS) features. In this paper, we explore the latest developments in related research fields. We summarize the 3GPP network slicing evolution, the Radio Access Network, and Core Network architectures. We describe background concepts correlated with inter-slice and intra-slice, as well as their management and orchestration architectures. We extend our discussion to the taxonomy of NGWN resource allocation via a deep comparison of the optimization algorithms. Finally, we enrich our study by including open issues and some recommendations for the future.

关键词： 5G mobile communication Resource management Network slicing 3GPP Radio access networks Quality of experience Next generation networking 5G B5G 6G network slicing resource allocation admission control optimization algorithms QoS QoE

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