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Int Conference Adv Elect Comp Commun ICAECC

作者： Premaratne, Upeka Halgamuge, Saman Univ Moratuwa Dept Elect & Telecommun Engn Katubedda 10400 Moratuwa Sri Lanka Univ Melbourne Dept Mech Engn Melbourne Vic 3010 Australia

ISBN: (纸本)9781479954964

The biological world is an ideal place for seeking inspiration for developing mathematical optimization algorithms. In this paper we propose two hybrid stochastic optimization algorithms that bear resemblance to the sexual reproduction cycle of Jellyfish and asexual reproductive cycle of species of Hydra. The performance of these two algorithms are investigated against other common optimization algorithms on a set of benchmark optimization problems. The results show that the proposed algorithms perform well.

关键词： optimization algorithms Biological Inspiration hybrid optimization algorithms Jellyfish Reproduction Hydra Asexual Reproduction

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Combinations of novel hybrid optimization algorithms-based trajectory planning analysis for an industrial robotic manipulators

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JOURNAL OF FIELD ROBOTICS 2022年第5期39卷 650-674页

作者： Singh, Gurjeet Banga, Vijay Kumar IKG Punjab Tech Univ 322 Rajinder Nagar Amritsar 143001 Punjab India Amritsar Grp Coll Dept ECE Amritsar Punjab India

In this paper, an optimal collision-free trajectory is developed based on the hybrid optimization algorithms for industrial robotic manipulators (IRMs). Three IRMs such as PUMA 560 (six degrees of freedom-6DOF), KUKA LBR iiwa 14 R820 (7DOF), and ABB IRB 140 (6DOF) are considered. The key objective is to enhance the smoothness and efficiency of manufacturing robots by optimum joint trajectory design using the seventh-order polynomial function. The proposed approach is to solve both kinematics and trajectory planning problems by using the different combinations of the hybrid meta-heuristic algorithms. The kinematic parameters including jerk, acceleration, and velocity mostly impact the travel smoothness of the robot end-effector on the trajectory path. Therefore, these parameters are to be constrained for generating the collision-free path. The endurance of velocity and acceleration can be obtained by reducing the jerk which leads to smooth robotic motion. The proposed work is executed using a robotic toolbox in MATLAB with a graphical user interface. The values of acceleration, velocity, and jerk are computed for the robot joints. Each robot obtained the minimum traveling time for without and with an obstacle which is 0.0118 and 0.0313 s for PUMA and 0.0117 and 0.0310 s for KUKA, and 0.0114 and 0.0120 s for ABB IRB 140 robot. From the experimental outcomes, the proposed scheme of the hybrid optimization algorithms is more effective for the trajectory planning of IRMs than that of other works.

关键词： forward kinematics hybrid optimization algorithms industrial robots inverse kinematics kinematic parameters polynomial function trajectory planning traveling time

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Simultaneous allocation of photovoltaic DG and DSTATCOM for techno-economic and environmental benefits in electrical distribution systems at different loading conditions using novel hybrid optimization algorithms

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第8期31卷 p1-35页

作者： Zellagui, Mohamed Lasmari, Adel Settoul, Samir El-Sehiemy, Ragab A. El-Bayeh, Claude Ziad Chenni, Rachid Univ Batna 2 Fac Technol Dept Elect Engn Batna Algeria Univ Quebec Dept Elect Engn Ecole Technol Super ETS Montreal PQ Canada Mentouri Univ Constantine 1 Fac Technol Dept Electrotech Constantine Algeria Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafrelsheikh Egypt Concordia Univ Canada Excellence Res Chairs Team Montreal PQ Canada

Enhancing distribution system operation accomplished with the integration of renewable energy resources (RERs) has several technical, economical, and environmental dimensions. In this regard, this paper presents an optimal integration procedure of Distributed Generation (DG) based on Photovoltaic panel (PV) and Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). The proposed procedure is formulated as a multi-objective function (MOF). The considered objectives that reflect the technical, economic, and environmental issues, are Active Power Loss Level (APLL), Short Circuit Level (SCL), Voltage Deviation Level (VDL), Net Saving Level (NSL), and Environmental Pollution Reduction Level (EPRL). The proposed procedure investigates several hybrid optimization methods that combine the firefly algorithm (FA) with various acceleration coefficients PSO algorithms to improve the overall solution quality of the hybrid algorithms compared with the individual algorithms. To prove the capability of the proposed procedure, four different cases are tested on IEEE 33-bus and 69-bus EDSs. Added to that, the proposed algorithms are extended to practical Algerian EDS in Adrar City 205-bus. Results obtained by the hybrid FA-SCAC-PSO algorithm showed that the simultaneous allocation of multiple DG and DSTATCOM in all standard and practical test systems significantly reduces the loss and enhances the voltage profile. An energy-efficient analysis to proceed for different cases studied based on the best hybrid FA-SCAC-PSO algorithm to reach the best value of MOF compared to other algorithms, moreover the capability to achieve the optimal allocation of DG and DSTATCOM by maintaining the voltages profile within the permissible limit, whatever the variation of load. Significant technical economic and environmental achievements are found for different case studies especially in the existence of DGs and DSTATCOM devices.

关键词： acceleration coefficients PSO algorithms distributed generation (DG) distribution static compensator (DSTATCOM) electrical distribution system (EDS) hybrid optimization algorithms load demand variations multi-objective function (MOF) problem

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New hybrid Approaches Based on Swarm-Based Metaheuristic algorithms and Applications to optimization Problems

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APPLIED SCIENCES-BASEL 2025年第3期15卷 1355-1355页

作者： Uzer, Mustafa Serter Selcuk Univ Ilgin Vocat Sch Elect & Automat TR-42600 Konya Turkiye

Metaheuristic algorithms are favored for solving a variety of problems due to their inherent simplicity, ease of implementation, and effective problem-solving capabilities. This study proposes four new hybrid approaches using swarm-based metaheuristic algorithms. Two of these new approaches are HHHOWOA1 and HHHOWOA2, based on the hybridization of Harris Hawks optimization (HHO) with the Whale optimization Algorithm (WOA), and the others are HHHOWOA1PSO and HHHOWOA2PSO, based on the hybridization of HHHOWOA1 and HHHOWOA2 with particle swarm optimization (PSO). An evaluation of these four innovative approaches is conducted on 23 benchmark functions, and their results are compared to those reported in the literature under equivalent parameter settings. Among the four approaches, HHHOWOA1 and HHHOWOA2PSO have demonstrated more favorable results. According to the literature, the HHHOWOA1 and HHHOWOA2PSO approaches achieve the most optimal results, either better or with the same average fitness values in 15 of the 23 functions and in 18 of the 23 functions, respectively. Moreover, the proposed approaches have been applied to three engineering problems, and the optimum values obtained are compared to the literature. Ultimately, the proposed approaches have proven effective in providing competitive solutions for the majority of optimization problems.

关键词： Harris hawks optimization particle swarm optimization whale optimization algorithm engineering problems hybrid optimization algorithms constrained optimization

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LEVYEFO-WTMTOA: the hybrid of the multi-tracker optimization algorithm and the electromagnetic field optimization

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JOURNAL OF SUPERCOMPUTING 2025年第2期81卷 1-54页

作者： Safi-Esfahani, Faramarz Mohammadhoseini, Leili Larian, Habib Mirjalili, Seyedali Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Univ Technol Sydney Fac Engn & IT Sch Informat Syst & Modelling Sydney NSW 2007 Australia

The field of engineering optimization often faces significant challenges in efficiently exploring complex solution spaces to identify optimal configurations, frequently struggling with local optima and premature convergence. These issues are especially pronounced in traditional optimization algorithms when applied to high-dimensional or intricate engineering problems. This paper introduces the LEVYEFO-WTMTOA algorithm, an innovative hybrid that combines the modified multi-tracker optimization algorithm (MTOA) with the electromagnetic field optimization (EFO) approach. This integration effectively addresses the limitations of previous methods, such as stagnation in local optima and suboptimal search strategies. The evaluations using the CEC2018 benchmark suite demonstrate that the LEVYEFO-WTMTOA algorithm significantly outperforms existing algorithms, reducing the mean error by an average of 20%. Specifically, the presented algorithm achieved a maximum cost improvement of 31.03% in spring design and 32.15% in welded beam design. These results confirm the LEVYEFO-WTMTOA's superior capability in handling complex optimization tasks, offering a powerful tool for algorithmic design in engineering applications and setting a new benchmark for performance in the field.

关键词： hybrid optimization algorithms Electromagnetic field optimization Multi-tracker optimization Engineering design optimization CEC2018 benchmark Global optimization

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Optimizing IoT Routing with a Focus on Service Quality Using Integrated Big Bang-Big Crunch Central Force optimization

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2025年第2期27卷

作者： Lei, Bo Sichuan Vocat & Tech Coll Sch Gen Educ Suining Peoples R China

This paper introduces a novel hybrid optimization algorithm, BB-CFO, which combines the big bang-big crunch (BB-BC) and central force optimization (CFO) algorithms to address key challenges in Internet of Things (IoT) networks, such as energy constraints, delay, and scalability. The proposed BB-CFO algorithm improves both the exploration and exploitation phases of optimization, providing a balanced approach for solving complex routing problems in low-power and lossy networks. The algorithm is integrated into the quality fuzzy routing protocol for low-power and lossy networks (Q-FRPL), which is evaluated through extensive simulations using Cooja and NS2 environments. The contributions of this study are twofold: first, the development of a hybrid optimization technique that enhances routing efficiency in IoT networks, and second, the demonstration of its effectiveness through comparative analysis with conventional algorithms. The obtained results show that the BB-CFO-based Q-FRPL protocol greatly reduces energy consumption as high as 800 mW in Cooja and up to 900 mW in NS2, at reduced end-to-end latency of 40 and 45 ms in Cooja and NS2, respectively, when the packet delivery ratio is 96.985%. These reflect the performance, scalability, and robustness of the proposed method and also show one possible solution toward efficient IoT networks.

关键词： big bang-big crunch energy efficiency hybrid optimization algorithms IoT network efficiency routing optimization

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MBHGA: A Matrix-Based hybrid Genetic Algorithm for Solving an Agent-Based Model of Controlled Trade Interactions

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IEEE ACCESS 2025年 13卷 26843-26863页

作者： Akopov, Andranik S. Russian Acad Sci Cent Econ & Math Inst Dept Dynam Models Econ & Optimizat Moscow 117418 Russia Moscow Inst Phys & Technol Dept Math Modeling Complex Syst & Optimizat Moscow 141701 Russia MIREA Russian Technol Univ Dept Instrumental & Appl Software Moscow 119454 Russia

The article discusses the development and study of a new matrix-based hybrid genetic algorithm (MBHGA) for solving an agent-based model of firms' behavior with controlled trade interactions. The proposed model employs symmetric strategies optimized using the MBHGA algorithm. This algorithm combines evolutionary search with real-coded crossover and matrix binary-coded crossover as genetic operators, creating a hybrid approach. The aim of the designed system is to assist decision-makers in selecting optimal strategies for their firms in situations where some companies impose restrictions on interactions. As demonstrated by the results of optimization experiments, the MBHGA algorithm significantly outperformed other methods, such as RCGA, PSO, GWO, SPEA2, and NSGA-II, in terms of both accuracy (measured by deviations from reference values) and the quality of the Pareto front approximation (measured by LHV, IGD, and CPF) when solving an agent-based model of firms' behavior. As demonstrated in this study, the performance of MBHGA significantly depends on the size of the model (i.e., the number of agents), and it can be enhanced by parallelizing the evolutionary search process, including matrix crossover operations for decision variables. optimization experiments using a genetic algorithm were conducted to maximize utility functions for agent companies under trade restrictions. The results showed that trade restrictions have a negative impact on the utility function values in both single- and multi-objective optimization, for selected countries and for all countries' firms. However, an optimization-based approach using the MBHGA algorithm can help minimize the negative consequences of trade restrictions by providing the ability to find nondominated solutions and the best possible trade-offs.

关键词： optimization Genetic algorithms Multi-agent systems Biological system modeling Decision making Companies Production Games Symmetric matrices Socioeconomics Multiagent systems genetic algorithms agent-based modeling controlled trade interactions matrix crossover hybrid optimization algorithms parallel computing multiobjective optimization Pareto front

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An enhanced method for intrusion detection systems in IoT environment

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-27页

作者： Alzubi, Qusay M. Sanjalawe, Yousef Makhadmeh, Sharif Naser Fakhouri, Hussam N. Yarmouk Univ Informat Technol & Comp Sci Fac Dept Informat Technol Cybersecur Program Irbid 21163 Jordan Univ Jordan UJ King Abdullah Sch Informat Technol 2 Dept Informat Technol Amman 11942 Jordan Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Univ Petra Fac Informat Technol Data Sci & Artificial Intelligence Dept Amman 1196 Jordan

The connectivity and integration of commodities have transformed many industries through the Internet of Things, enhancing both efficiency and functionality. However, this interconnection has also introduced critical security challenges, necessitating a robust Intrusion Detection System. This paper presents a novel, enhanced method for Intrusion Detection Systems in Internet of Things environments, utilizing advanced machine learning techniques and optimization algorithms. The proposed method integrates the Marine Predator Optimizer and Grey Wolf Optimizer for efficient feature selection, improving the detection and classification of anomalies. Two datasets, NFC-SECICIDS2018v2 and BotIoT2018, were used to evaluate the performance and effectiveness of the proposed Intrusion Detection System. The results demonstrate that the proposed method achieves an accuracy of 97.59% on the NFC-SECICIDS2018v2 dataset and 99.97% on the BotIoT2018 dataset. Additionally, the method shows a perfect sensitivity of 100% on the BotIoT2018 dataset and a sensitivity of 99.58% on the NFC-SECICIDS2018v2 dataset, significantly reducing the false alarm rate. The findings underscore the effectiveness of combining the Marine Predator Optimizer and Grey Wolf Optimizer to enhance the performance of Intrusion Detection Systems, offering a promising solution for securing the Internet of Things networks against evolving cyber threats.

关键词： Anomaly detection BotIoT2018 dataset Cybersecurity in smart devices Feature selection for IDS Grey wolf optimizer hybrid optimization algorithms Intrusion detection in IoT Internet of Things security Marine predator optimizer NFC-SECICIDS2018v2 dataset

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Smart grid management: Integrating hybrid intelligent algorithms for microgrid energy optimization

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ENERGY REPORTS 2024年 12卷 2997-3019页

作者： Pramila, V. Kannadasan, R. Bharathsingh, J. Rameshkumar, T. Alsharif, Mohammed H. Kim, Mun-Kyeom BS Abdur Rahman Crescent Inst Sci & Technol Dept Elect & Elect Engn Chennai 600048 India Sri Venkateswara Coll Engn Dept Elect & Elect Engn Sriperumbudur 602117 India Kalasalingam Acad Res & Educ Krishnankoil Dept Comp Sci & Engn Srivilliputhur 626126 India KPR Inst Engn & Technol Elect & Elect Engn Coimbatore 641407 India Sejong Univ Coll Elect & Informat Engn Dept Elect Engn 209 Neungdong Ro Seoul 05006 South Korea Chung Ang Univ Sch Energy Syst Engn 84 Heukseok Ro Seoul 06974 South Korea

The increasing global demand for power drives the need for advancements in generation, control, and supply technologies. While bulk power generation remains cost-effective, it depends on transmission and distribution systems for delivery. Integrating distributed generators into the grid enhances reliability and direct power supply to consumers. Micro Grids (MGs) are a promising solution, offering smoother and more reliable operations. This study explores MGs, incorporating the latest loads and distributed generators to minimize operating costs for electrical power providers. This work utilizes intelligent algorithms, including the Firefly algorithm, Spider Monkey optimization (SMO), and a hybrid approach combining both. The Firefly algorithm, inspired by swarm intelligence, mimics fireflies' light emission to optimize solutions. SMO, based on spider monkeys' social behavior, balances exploration and exploitation to achieve optimal results. A novel hybrid algorithm is developed to minimize MG operating costs by combining SMO's exploration with the Firefly algorithm's neighborhood optimization;enhancing navigation through the complex constraint solution space of MGs. Case studies on a modified IEEE 33-bus test feeder are conducted. The study compares three metaheuristic methods with nonconvex solution spaces to MG operational algorithms. The results demonstrate the hybrid method's efficacy in determining the optimal location and size of real and reactive power support devices for MG operation. Numerical results show the hybrid algorithm's superiority over existing methods, reducing operating costs by 7.5 %, real power loss by 36.5 %, and reactive power loss by 1.7 %, and voltage deviation by 16.895. By considering MG constraints, the hybrid algorithm consistently delivers better outcomes, optimizing operating costs and ensuring efficient power supply. This study presents a novel MG optimization approach, enhancing future energy systems with improved solutions.

关键词： Microgrid optimization Intelligent algorithms Distributed Generation Firefly Algorithm Spider Monkey optimization hybrid optimization algorithms

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The hybrid algorithms in constrained optimization of the permanent magnet motors

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2024年第9期18卷 613-619页

作者： Knypinski, Lukasz Devarapalli, Ramesh Gillon, Frederic Poznan Univ Tech Inst Elect Engn & Elect Piotrowo 3a PL-60965 Poznan Poland Inst Chem Technol Dept Elect Elect & Instrumentat Engn Bhubaneswar India Univ Lille Cent Lille Arts & Metiers ParisTech Lille France

Here, different hybrid optimization algorithms have been applied to the constrained optimization of the line-start permanent magnet synchronous motor. The optimal results for the hybrid Cuckoo Search and hybrid Grey Wolf optimization have been compared. In the primary objective function, the parameters describing motor functional parameters in the steady state have been included. Moreover, the non-linear constraint function has been taken into account. The optimization procedures have been elaborated in the Delphi and Matlab environments. The mathematical model of the line-start permanent magnet motor has been developed in the Maxwell environment. Selected optimization results are presented and discussed. Here, the comparison of two hybrid optimization algorithms is presented. The HGWO algorithm is elaborated as a connection of two heuristic algorithms: grey wolf algorithm and sine cosine algorithm;thus the HSC is developed as a connection heuristic (cuckoo search algorithms) and deterministic (Hooke-Jeeves method). Therefore, it is very important to proper select the coefficients characterizing each hybrid algorithm for the solved optimization task (analytical function or electromagnetic devices). image

关键词： electromagnetic fields optimisation permanent magnet machines synchronous machines hybrid optimization algorithms

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