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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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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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hybrid optimization algorithms Resembling the Life Cycles of Organisms of Phylum Cnidaria

Hybrid Optimization Algorithms Resembling the Life Cycles of...

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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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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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A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: hybrid meta-heuristic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Seydanlou, Pourya Jolai, Fariborz Tavakkoli-Moghaddam, Reza Fathollahi-Fard, Amir M. Univ Tehran Dept Ind Engn Alborz Campus Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Univ Quebec Ecole Technol Super Dept Elect Engn Montreal PQ Canada

A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable ClosedLoop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO2) emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran.

关键词： Sustainable closed-loop supply chain Multi-objective Perishable products Olive industry hybrid optimization algorithms

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Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

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SCIENTIFIC REPORTS 2024年第1期14卷 1-40页

作者： Al-Dhaifallah, Mujahed Refaat, Mohamed M. Alaas, Zuhair Abdel Aleem, Shady H. E. Ali, Ziad M. King Fahd Univ Petr & Minerals Control & Instrumentat Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals IRC Sustainable Energy Syst IRC SES Dhahran 31261 Saudi Arabia Elect Res Inst Photovolta Cells Dept Cairo 11843 Egypt Jazan Univ Fac Engn Dept Elect Engn Jizan 45142 Saudi Arabia Inst Aviat Engn & Technol Dept Elect Engn Giza 25152 Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Elect Engn Dept Wadi Addawaser 11991 Saudi Arabia Aswan Univ Aswan Fac Engn Elect Engn Dept Aswan 81542 Egypt

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes-Series hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme-are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model's effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed

关键词： Hosting capacity Electric vehicles hybrid optimization algorithms Transmission expansion planning

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