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A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails

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MATERIALS TESTING 2020年第3期62卷 251-260页

作者： Kurtulus, Dies Yildiz, Ali Riza Sait, Sadiq M. Bureerat, Sujin Kaen, Khon Bursa Uludag Univ Dept Automot Engn Bursa Turkey KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand

In this paper, a novel hybrid optimization algorithm is introduced by hybridizing a Harris hawks optimization algorithm(HHO) and simulated annealing for the purpose of accelerating its global convergence performance and optimizing structural design problems. This paper is the first research study in which the hybrid Harris hawks simulated annealing algorithm (HHOSA) is used for the optimization of design parameters for highway guardrail systems. The HHOSA is evaluated using the well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a guardrail system that has an H1 containment level as a case study is optimized to investigate the performance of the HHOSA. The guardrail systems are designed with different cross-sections and distances between the posts. TB11 and TB42 crash analyses are performed according to EN 1317 standards. Twenty-five different designs are evaluated considering weight, the guardrail working width, and the acceleration severity index (ASI). As a result of this research, the optimum design of a guardrail is obtained, which has a minimum weight and acceleration severity index value (ASI). The results show that the HHOSA is a highly effective approach for optimizing real-world design problems.

关键词： Harris hawks algorithm Simulated annealing crash analysis hybrid optimization algorithm guardrails road safety barriers

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Parameter optimization of electromagnetic suspension-type maglev train control system based on multi-objective grey wolf non-dominated sorting hybrid algorithm-II hybrid algorithm

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2024年第2期43卷 956-978页

作者： Wang, Meiqi Zeng, Siheng Liu, Pengfei He, Yixin Chen, Enli Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ Sch Mech Engn Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ Hebei Prov Key Lab Engn Machinery Power & Transmis Shijiazhuang Peoples R China Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn 17 NortheastInner Ring 2 Shijiazhuang 050043 Hebei Peoples R China

This paper presents a novel hybrid algorithm based on CMOGWO-ADNSGA-II to solve the vibration stability problem during the operation of a EMS-type maglev train dynamics model subjected to strong non-linear magnetic buoyancy. The proposed algorithm optimizes the control system parameters of EMS-type maglev train suspensions by combining an improved multi-objective chaotic grey wolf algorithm (CMOGWO) with an improved non-dominated Sorting genetic algorithm-II (ADNSGA-II) to enhance the search capability of the algorithm and ensure population diversity. The efficacy of the algorithm is demonstrated by applying it to the EMS-type maglev train suspension frame control system to find the optimal control parameters. Experimental results show that the system with the optimal parameters applied significantly reduces the suspension gap amplitude and the corresponding standard deviation, as well as the vertical acceleration amplitude and the corresponding standard deviation during operation. The proposed algorithm provides a good solution for EMS-type maglev train suspension vibration control, which can improve its performance and safety.

关键词： electromagnetic suspension-type maglev trains hybrid optimization algorithm vibration control non-linear magnetic buoyancy multi-objective problem

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Optimal Placement and Sizing of Inverter-Based Distributed Generation Units and Shunt Capacitors in Distorted Distribution Systems Using a hybrid Phasor Particle Swarm optimization and Gravitational Search algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第6-7期48卷 543-557页

作者： Milovanovic, Milos Tasic, Dragan Radosavljevic, Jordan Perovic, Bojan Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica Serbia Univ Nis Fac Elect Engn Nish Serbia

In this paper, a novel hybrid population-based meta-heuristic algorithm, called the hybrid Phasor Particle Swarm optimization and Gravitational Search algorithm (PPSOGSA), is proposed to solve the problem of optimal placement and sizing of inverter-based distributed generation (DG) units and shunt capacitors in radial distribution systems with linear and non-linear loads. The objective of the problem is reduction of active power losses considering constraints of the fundamental frequency active and reactive power balance, RMS voltage, and total harmonic distortion of voltage (THDV) at each bus of the network, as well as the branch flow constraints. The performance of the PPSOGSA-based approach is evaluated on the standard IEEE 33- and 69-bus test systems under sinusoidal and non-sinusoidal operating conditions. Compared to the original PPSO and GSA and other algorithms commonly used in the optimal sitting and sizing problem of DG units and shunt capacitors, it is found that the proposed algorithm has yielded better results.

关键词： distribution system inverter-based distributed generation shunt capacitor optimal placement harmonic distortion hybrid optimization algorithm phasor particle swarm optimization gravitational search algorithm

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An improved hybrid ant particle optimization (IHAPO) algorithm for reducing travel time in VANETs

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APPLIED SOFT COMPUTING 2018年 64卷 526-535页

作者： Jindal, Vinita Bedi, Punam Univ Delhi Dept Comp Sci Keshav Mahavidyalaya Delhi India Univ Delhi Dept Comp Sci Delhi India

With the increase in traffic volume day by day, cities are facing the problem of extreme congestion in most of the developing countries. The congestion on the road leads to increase in travel time and travel cost as well as having a significant impact on the health of people. This paper proposes a novel Improved hybrid Ant Particle optimization (IHAPO) algorithm for reducing the travel time for enabling smart transportation. The aim of the proposed algorithm is to select a best path in peak hours by avoiding the optimal path, if congested and resuming the optimal path when congestion eases. This algorithm is an improvement of the existing Modified Ant Colony optimization (MACO) algorithm. It combines both MACO and Particle Swarm optimization (PSO) algorithms using the global best exchange method. Initially, both algorithms work separately and produce their best solutions. Then a comparison has been made between both the solutions and a new global best solution found for the whole network. According to the best solution obtained, the position of both ants and particles are changed for the next iterations. MACO algorithm works under the assumption that all roads are in working condition, whereas the proposed IHAPO algorithm works under normal road conditions. Another difference between these algorithms is the pheromone update process that makes the new algorithm more effective. The proposed algorithm is tested on a map of North-West Delhi, India using Simulation of Urban MObility (SUMO) for traffic simulation. It was found that the travel time is reduced significantly by using the proposed IHAPO algorithm over the existing algorithms in consideration. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Ant colony optimization Nature inspired algorithms Particle swarm optimization hybrid optimization algorithm Vehicular adhoc networks

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A new hybrid algorithm for simultaneous size and semi-rigid connection type optimization of steel frames

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2015年第1期15卷 89-102页

作者： Hadidi, Ali Rafiee, Amin Univ Tabriz Dept Civil Engn Tabriz Iran

A hybrid algorithm based on Harmony Search (HS) and Big Bang-Big Crunch (BB-BC) optimization methods is proposed for optimal design of semi-rigid steel frames. The algorithm selects suitable sections for beams and columns and assigns suitable semi-rigid connection types for beam-to-column connections, such that the total member plus connection cost of the frame, is minimized. Stress and displacement constraints of AISC-LRFD code together with the size constraints are imposed on the frame in the design procedure. The nonlinear moment-rotation behavior of connections and P-Delta effects of beam-column members are taken into account in the non-linear structural analysis. Three benchmark steel frames are designed and the results are compared with those of standard BB-BC and of other studies. The comparisons demonstrate that proposed algorithm performs better than standard BB-BC and HS methods in all examples and that the total cost of a frame can be reduced through suitable selection of its beam-to-column connection types.

关键词： Big Bang-Big Crunch Harmony Search hybrid optimization algorithm size optimization steel frames semi-rigid connections

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Load frequency control in autonomous microgrids using hybrid algorithm GWO-CS based fuzzy logic sliding mode controller

RESULTS IN ENGINEERING

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RESULTS IN ENGINEERING 2025年 25卷

作者： Bouaddi, Abdessamade Rabeh, Reda Ferfra, Mohammed Mohammed V Univ Rabat Mohammadia Sch Engn RTEEC Team Rabat Morocco Int Univ Rabat ECINE LERMA Sala El Jadida Morocco

Load Frequency Control (LFC) in power systems remains a critical and challenging task for power system engineers, particularly in systems incorporating multiple power generation sources, including renewable energy. These sources add significant complexity due to their inherent variability and nonlinearity. To address this, a novel fuzzy logic sliding mode controller (FL-SMC) has been proposed for the LFC problem in autonomous microgrid systems. This controller uniquely combines the strengths of intelligent controllers, such as fuzzy logic, with the robustness and precision of nonlinear sliding mode controllers, offering a promising solution to the LFC challenge. The design of the FL-SMC controller is further enhanced by employing a hybrid optimization algorithm that combines the Gray Wolf Optimizer (GWO) and Cuckoo Search (CS). This hybrid algorithm (hGWO-CS) is leveraged to optimize the controller's parameters by minimizing the integral time absolute error (ITAE), ensuring optimal system performance. To validate the effectiveness of the proposed controller, it has been rigorously compared with existing control methods through extensive MATLAB-Simulink simulations. The simulations encompass various scenarios, including arbitrary load changes, fluctuations in wind and solar power generation, and parametric variations in the system. These scenarios are designed to test the controller's robustness and adaptability under real-world conditions. The results unequivocally demonstrate that the FL-SMC controller outperforms conventional methods, achieving faster response times and superior robust performance metrics, such as minimal deviations in frequency. Moreover, the robustness study highlights the controller's ability to maintain system stability despite significant uncertainties and variations in operating conditions.

关键词： Load Frequency Control Fuzzy logic Sliding Mode Autonomous Microgrid Renewable Energy Sources hybrid optimization algorithm

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A hybrid glowworm swarm optimization algorithm to solve constrained multimodal functions optimization

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optimization 2015年第4期64卷 1057-1080页

作者： Zhou, Yongquan Zhou, Guo Zhang, Junli Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. The HGSO algorithm embeds predatory behaviour of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the GSO with differential evolution on the basis of a two-population co-evolution mechanism. In addition, to overcome the premature convergence, the local search strategy based on simulated annealing is applied to make the search of GSO approach the true optimum solution gradually. Finally, several benchmark functions show that HGSO has faster convergence efficiency and higher computational precision, and is more effective for solving constrained multi-modal function optimization problems.

关键词： differential evolution artificial fish swarm glowworm swarm optimization multimodal function optimization hybrid optimization algorithm 68W20 68T20

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hybrid optimization Approach for Optimal Location of Electric Vehicle Charging Station (EVCS) in Distribution Network Considering Voltage Stability

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Smart Grids and Sustainable Energy 2025年第2期10卷 1-20页

作者： Kanojia, Sarika S. Suthar, Bhavik N. Giri, Ashutosh K. Research Scholar Electrical Engineering Department GTU Gujarat Ahmedabad India Electrical Engineering Department Nirma University Gujarat Ahmedabad 382487 India Electrical Engineering Department L. E. College Gujarat Morbi 363642 India Electrical Engineering Department Government Engineering College Bharuch Affiliated to GTU Gujarat 392001 India

Abstract: Modern power networks face a significant problem with voltage instability since there isn't enough generation and transmission capacity to meet growing demand, such as the load associated with EV charging. To enable the industry's sustainable expansion, an infrastructure for electric vehicle charging stations (EVCS) must be developed. Higher voltage variation and energy losses in the electrical distribution system may arise from the unplanned placement of EVCS. This research proposes an operational approach to sustain the power system's voltage stability, reducing power losses and increasing the system voltage following the optimal placement of EVCS. To determine the best location, this research suggests a novel hybrid coati-pelican optimization technique. The recommended method's efficacy has been assessed using the standard IEEE-33 and IEEE-69 bus test system. A forward–backward power flow method has been applied to compute load flow. The convergence criterion is compared with the other latest optimization techniques. The results of the simulation validate the effectiveness and performance of the hybridized optimization technique. The MATLAB/Simulink tool is used to implement this suggested approach. Key messages: Rising demand, especially from EV charging, is straining power networks and causing voltage instability. Uncoordinated placement of Electric Vehicle Charging Stations (EVCS) can lead to voltage fluctuations and higher energy losses in distribution systems. A well-planned EVCS infrastructure is essential to support the sustainable growth of the electric vehicle industry without compromising grid stability. This research introduces a novel hybrid Coati-Pelican optimization technique for the optimal placement of EVCS to enhance voltage stability and reduce power losses. The proposed method has been tested on standard IEEE-33 and IEEE-69 bus test systems, demonstrating its effectiveness through simulation. A forward-backward sweep method is used fo

关键词： Distribution network EV charging station hybrid optimization algorithm Optimal placement Solar PV Voltage stability Voltage stability index

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hybrid Artificial Glowworm Swarm optimization algorithm for Solving Constrained Engineering Problem

Hybrid Artificial Glowworm Swarm Optimization Algorithm for ...

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International Conference on Industry, Information System and Material Engineering

作者： Luo, Qifang Zhang, Junli Guangxi Univ Nationalities Coll Math & Comp Sci Nanning 530006 Guangxi Peoples R China

ISBN: (纸本)9783037850275

In this paper, based on glowworm swarm (GS) and artificial fish swarm (AFS) with differential evolution (DE) optimization algorithm, a new hybrid artificial glowworm swarm optimization (HGSO) algorithm is proposed. We use HGSO to solve engineering optimization design problem. The results show that the HGSO has faster convergence, higher precision and is more effective for solving constrained engineering optimization problem.

关键词： Glowworm swarm optimization hybrid optimization algorithm engineering optimization design

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STRATEGIC INIITIALIZATION OF A hybrid PARTICLE SWARM optimization-SIMULLATED ANNEALING algorithm (HPSOSA) FOR PID CONTROLLER DESIGN FOR A NONLINEAR SYSTEM

STRATEGIC INIITIALIZATION OF A HYBRID PARTICLE SWARM OPTIMIZ...

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25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Tharmalingam, Mathiruban Raahemifar, Kaamran Ryerson Univ Dept Elect Engn & Comp Engg Toronto ON Canada

ISBN: (纸本)9781467314336

There exist some variations of the particle swarm optimization - simulated annealing optimization technique (PSOSA) hybrid algorithm for solving the PID control design problem, however most of these algorithms use the simulated annealing as a tool to escape local minimums that the PSO algorithm may get trapped in and also these algorithms initialize the particles within the solution space randomly. In this paper, the effects of initializing the particles strategically within the solution space along with the application of the SA algorithm to the hybrid algorithm at each iteration are explored. To test the effectiveness of the proposed modifications the algorithms are compared on common benchmark functions before the modified hybrid algorithm (MPSOSA) is used to design a PID controller for the inverted Pendulum problem.

关键词： Nonlinear systems PID Controller hybrid optimization algorithm Particle Swarm optimization (PSO) Simulated Annealing (SA)

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