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Autonomous bonobo optimization algorithm for power allocation in wireless networks

MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 217卷 294-310页

作者： Eid, Heba F. Cuevas, Erik Mansour, Romany F. Al Azhar Univ Fac Sci Cairo Egypt Univ Guadalajara Dept Elect Guadalajara Mexico New Valley Univ Fac Sci Dept Math Kharga Egypt

Bonobo optimizer (BO) is a recent metaheuristic algorithm inspired by the social behavior of bonobos. BO maintains a robust search strategy based on two distinct phases that involve interesting mechanisms, such as the fission-fusion method for selection and an efficient process for producing new candidate solutions. Despite its remarkable capacity, BO presents a critical flaw that corresponds to an inappropriate balance between exploration and exploitation. Under such conditions, suboptimal or even poor solutions can be obtained. This paper proposes a modified BO algorithm in which the trajectory of each search agent is modified dynamically through the adaptation of the main parameters of the algorithm. With this new mechanism, the algorithm allows for the exploration and exploitation of different regions of the solution space and determines the global optimum in a faster manner. The performance of the proposed algorithm is evaluated by considering two scenarios. One is the optimization of 23 benchmark functions and the second is the problem of power allocation in wireless networks. The results show that the proposed scheme enhances the performance of the basic BO method by increasing the robustness and providing a better solution quality.

关键词： Bonobo optimization algorithm Bio-inspired optimization swarm intelligence algorithm Wireless networks Secure communication Energy-spectral efficiency

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Dynamic formation optimisation for energy saving of a fleet of unmanned surface vehicles based on robust optimisation over time strategy

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OCEAN ENGINEERING 2024年 301卷

作者： Dong, Zhenpeng Liang, Xiao Liu, Dianyong Yu, Changdong Li, Wei Dalian Maritime Univ Coll Naval Architecture & Ocean Engn Dalian Liaoning Peoples R China Dalian Maritime Univ Coll Artificial Intelligence Dalian Liaoning Peoples R China Dalian Maritime Univ Coll Environm Sci & Engn Dalian Liaoning Peoples R China

This paper presents a dynamic formation optimisation strategy that focuses on constructing an energy-saving formation for a fleet of unmanned surface vehicles (USVs) by considering two optimisation objectives: minimum energy consumption and minimum switching cost. The dynamic formation optimisation strategy based on robust optimisation over time (ROOT) is used to determine the energy-saving formation with the optimal position layout. The formation switching cost is first proposed to define the energy consumption cost of switching from the original position to the new position. Dynamic environments are established using the modified moving peak benchmark (mMPB) to simulate the movement of waves. A new swarm intelligence algorithm for solving ROOT is designed using an adaptive gradient training predictor and a solver embedded the dingo optimisation algorithm. The ROOT results are compared with the tank test results, which confirms that the dynamic formation optimisation strategy has a high credibility. The energy-saving formation optimised by dynamic formation optimisation based on ROOT not only ensures the minimum energy consumption, but also ensures the minimum switching cost. The survival time of the spacing configuration is prolonged, indicating that the acquired energysaving formation can be consistently utilized over an extended period without necessitating positional adjustments. This mitigates the escalating energy consumption resulting from frequent formation switching when a fleet encounters dynamic marine conditions.

关键词： Dynamic formation optimisation Energy-saving formation Robust optimisation over time swarm intelligence algorithm Switching cost

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Competing leaders grey wolf optimizer and its application for training multi-layer perceptron classifier

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Yang, Zhenlun Guangzhou Panyu Polytech Sch Informat Engn Guangzhou 511483 Guangdong Peoples R China

The multi-layer perceptron (MLP) is a highly popular artificial neural network used for classification across various applications. swarm intelligence algorithms, such as the grey wolf optimizer, battle royale optimizer, sine cosine algorithm, and their improved variants, have been widely utilized for training MLP classifiers, addressing the challenging optimization problem that covers various difficulties in determining the optimal network parameters to minimize errors while processing datasets. Nevertheless, these prevailing algorithms frequently encounter problems like stagnation in local optima and inefficient search, leading to a deficiency in their ability to deliver efficient and accurate solutions. In order to overcome this challenge and provide a powerful tool for MLP classifier training, the competing leaders grey wolf optimizer (CGWO) is proposed in this study. CGWO benefits from a novel mechanism of competing leaders that provides a flexible wolf pack leadership hierarchy to avoid stagnation in local optima and accelerate convergence speed. In addition, a population diversity-enhanced initiation method is designed to help improve the efficiency of the mechanism of competing leaders. The performance of CGWO in training MLP classifiers is evaluated in an experiment comparing CGWO with nine state-of-the-art algorithms on eleven classification datasets representing a wide range of optimization challenges. CGWO substantially outperforms its competitors regarding optimal fitness, classification accuracy, and convergence speed;and shows competitive performance in computation time comparison.

关键词： Multi-layer perceptron Grey wolf optimizer Competing leaders Neural networks swarm intelligence algorithm

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Improved African Vulture Optimization algorithm Based on Quasi-Oppositional Differential Evolution Operator

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IEEE ACCESS 2022年 10卷 95197-95218页

作者： Liu, Renju Wang, Tianlei Zhou, Jing Hao, Xiaoxi Xu, Ying Qiu, Jiongzhi Wuyi Univ Dept Intelligent Mfg Jiangmen 529020 Peoples R China

In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.

关键词： Statistics Sociology Mathematical models Particle swarm optimization Metaheuristics Convergence Benchmark testing African vulture optimization algorithm benchmark function differential evolution quasi-oppositional learning swarm intelligence algorithm

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Hierarchy Strengthened Grey Wolf Optimizer for Numerical Optimization and Feature Selection

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IEEE ACCESS 2019年 7卷 78012-78028页

作者： Tu, Qiang Chen, Xuechen Liu, Xingcheng Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Xinhua Coll Sch Informat Sci Guangzhou 510520 Guangdong Peoples R China

Grey wolf optimizer (GWO) is an efficient swarm intelligence algorithm for kinds of optimization problems. However, GWO tends to be trapped in local optimum when solving large-scale problems. Social hierarchy is one of the main characteristics of GWO which affect the searching efficiency. Thus, an improved algorithm called hierarchy strengthened GWO (HSGWO) is proposed in this paper. First, the pack of wolves is roughly divided into two categories: dominant wolves and omega wolves. Second, the enhanced elite learning strategy is performed for dominant wolves to prevent the misguidance of low-ranking wolves and improve the collective efficiency. Then, the hybrid GWO and differential evolution (DE) strategy is executed for omega wolves to avoid falling into local optimum. In addition, a new hybrid one-dimensional and total-dimensional selection strategy is designed for omega wolves to balance the exploration and the exploitation during optimization. Finally, a perturbed operator is used to maintain the diversity of the population and further improve the exploration. To make a complete evaluation, the proposed HSGWO is first compared with six representative GWO variants for 50-dimensional problems based on CEC2014 benchmarks. The scalability of HSGWO is further tested by comparing it with eight state-of-the-art non-GWO algorithms for large-scale optimization problems with 100 decision variables. In addition, feature selection problem is used for testing the effectiveness of HSGWO on real-world applications. The experimental results demonstrated that the proposed algorithm outperforms other algorithms in terms of solution quality and convergence rate in most of the experiments.

关键词： swarm intelligence algorithm grey wolf optimizer (GWO) social hierarchy numerical optimization feature selection

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Intelligent Neural Integral Sliding-mode Controller for a space robotic manipulator mounted on a free-floating satellite

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ADVANCES IN SPACE RESEARCH 2023年第9期71卷 3734-3747页

作者： Lavin-Delgado, J. E. Chavez-Vazquez, S. Gomez-Aguilar, J. F. Alassafi, Madini O. Alsaadi, Fawaz E. Ahmad, Adil M. Univ Politecn Estado Guerrero UPEG Direcc Ingn Redes & Telecomunicac Carretera Fed Iguala Taxco KM 105 Taxco De Alarcon 40321 Guerrero Mexico Tecnol Nacl Mexico CENIDET Interior Internado Palmira S-N Col Palmira Cuernavaca 62490 Morelos Mexico CONACyT Tecnol Nacl Mex CENIDET Interior Internado Palmira S N Col Palmira Cuernavaca 62490 Morelos Mexico King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah Saudi Arabia

In this paper, a fractional neural integral sliding-mode controller based on the Caputo-Fabrizio derivative and Riemann-Liouville integral for a robot manipulator mounted on a free-floating satellite is proposed and developed. The mathematical model is obtained by using the Euler-Lagrange formalism and generalized through the Caputo-Fabrizio derivative. The conventional neural integral sliding-mode controller reported in the literature were also developed to make a comparative analysis between both controllers. The con-trollers are tuned using a swarm intelligence algorithm. External disturbances are also considered. Simulation results show the superiority and robustness of the proposed control system for trajectory tracking tasks through the root-mean-square error. Additionally, two addi-tional reference trajectories were considered. In this sense, the numerical simulations were done by using the same orders and gains, showing the robustness of our fractional-order control strategy under different operating conditions. Further, the proposed controller uses the fractional derivative in the sense of Caputo-Fabrizio which has not been used enough in the area of robotics, so this is a start to analyse the contributions that arise when using said derivative in this robotic.(c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Robot manipulator Fractional calculus Neural network controller Artificial intelligence Big Data Analytics Decision Making Aerospace systems swarm intelligence algorithm

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Cooperative Networking Strategy of UAV Cluster for Large-Scale WSNs

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IEEE SENSORS JOURNAL 2022年第22期22卷 22276-22290页

作者： Chen, Yidi Liu, Hao Guo, Jiapeng Wang, Yujie Liu, Fei Ding, Shanshan Chen, Renwen Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China

To solve the shortcomings of wireless sensor networks (WSNs) in data collection in a wide range, long distance, and other complex environments, such as high networking cost, short network life cycle, and difficult data collection, a data collection strategy based on the unmanned aerial vehicle (UAV) cluster collaborative networking is proposed. First, the node structure and deployment application of WSNs are analyzed, and the architecture of UAV collaborative data collection is designed. Second, the related models of communication energy consumption between WSNs' network and UAV cluster, UAV cluster task allocation, and path planning are established successively. Then, a simple but efficient task planning strategy considering UAV forward and return distances is proposed. Finally, for the problem of model solving, the solution strategy of the heuristic swarm intelligence algorithm is proposed. The simulation results verify that the strategy reduces the overall network energy consumption by 42.16% and the total range of UAV by 9.7% and realizes the relative minimization of WSNs energy consumption and UAV flight cost on the premise of ensuring the full coverage of WSNs' nodes.

关键词： Wireless sensor networks Sensors Optimization Data collection Autonomous aerial vehicles Energy consumption Heuristic algorithms Collaborative perception data collection swarm intelligence algorithm unmanned aerial vehicle (UAV) wireless sensor networks (WSNs)

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Coordinated control strategy for braking and shifting for electric vehicle with two-speed automatic transmission

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ETRANSPORTATION 2022年 13卷

作者： Liu, Yonggang Gao, Dekun Zhai, Kening Huang, Qi Chen, Zheng Zhang, Yi Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Dongfeng Liuzhou Automobile Co Ltd Passenger Car Technol Ctr Liuzhou 545005 Guangxi Peoples R China Chongqing Univ Sci & Technol Sch Mech & Power Engn Chongqing 401331 Peoples R China Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Yunnan Peoples R China Univ Michigan Dearborn Dept Mech Engn Dearborn MI 48128 USA

This paper presents a coordinated control strategy for braking and shifting of electric vehicles that are equipped with a two-speed automatic transmission. The coordinated control strategy mainly includes braking force distribution and synthetic shifting regulation for braking operations. Firstly, the vehicle model is established for investigation of the shift process during braking. Then, the braking force distribution considering energy efficiency is presented, and the regenerative braking force is calculated. With the target of maximizing energy recovery and ensuring driving comfort, an optimal shift schedule during regenerative braking is advanced based on the multi-objective cuckoo search algorithm. Due to the interaction of regenerative braking force in torque releasing and restoring process, an adaptive braking mode switching control is proposed to improve the shifting smoothness. The detailed simulations and hardware-in-the-loop validations are conducted to verify the anticipated performance of the proposed strategy. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Electric vehicle Regenerative braking Multi-objective cuckoo search swarm intelligence algorithm

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Soft computing applied to the build of textile defects inspection system

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IET COMPUTER VISION 2013年第5期7卷 373-381页

作者： Darwish, Saad Mohamed Univ Alexandria Dept Informat Technol Inst Grad Studies & Res Alexandria 21526 Egypt

The inspection of textile defects is challenging because of the large number of defects categories that are characterised by their imprecision and uncertainty. In this study, novel interval type-2 fuzzy system is proposed for resolving defects recognition problem of textile industries. The proposed system mixes interval type-2 fuzzy reasoning and swarm optimisation algorithm together in order to enhance the defects classification capabilities. Interval type-2 fuzzy logic is powerful in handling high level of indecisions in the human decision making process, including uncertainties in measurements of textile features and data used to calibrate the examination's parameters. swarm intelligence algorithm is used to optimise parameters of the membership functions to increase the accuracy of fuzzy controller. Besides, the problem of fuzzy linguistic rules learning has been tackled by utilising ant colony meta-heuristic method to reduce the complexity of the inspection system. Excellent recogniser results on real textile samples, using this system, are demonstrated.

关键词： decision making fuzzy logic fuzzy reasoning heuristic programming inspection learning (artificial intelligence) particle swarm optimisation production engineering computing textile industry ant colony optimisation soft computing textile defect inspection system defects recognition problem textile industries interval type-2 fuzzy reasoning system particle swarm optimisation algorithm defects classification capability interval type-2 fuzzy logic human decision making process examination parameter calibration swarm intelligence algorithm membership functions fuzzy controller fuzzy linguistic rules learning ant colony metaheuristic method

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Intelligent proportional-integral-derivate controller using metaheuristic approach via crow search algorithm for vibration suppression of flexible plate structure

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2024年第1期43卷 560-574页

作者： Shaari, Aida Nur Syafiqah Hadi, Muhamad Sukri Wahab, Abdul Malek Abdul Ab Talib, Mat Hussin Yatim, Hanim Mohd Darus, Intan Zaurah Mat Tokhi, M. Osman Univ Teknol MARA Coll Engn Sch Mech Engn Shah Alam Malaysia Univ Teknol Malaysia Sch Mech Engn Johor Baharu Malaysia London South Bank Univ Sch Engn London England Univ Teknol MARA Coll Engn Sch Mech Engn Shah Alam 40450 Malaysia

Proportional-integral-derivate (PID) controller has gained popularity since the advancement of smart devices especially in suppressing the vibration on flexible structures using different approaches. Such structures required accurate and reliable responses to prevent system failures. swarm intelligence algorithm (SIA) is one of the optimization methods based on nature that managed to solve real-world problems. Crow search is a well-known algorithm from the SIA group that can discover optimum solutions in both local and global searches by utilizing fewer tuning parameters compared to other methods. Hence, this study aimed to simulate a PID controller tuned by SIA via crow search for vibration cancellation of horizontal flexible plate structures. Prior to that, an accurate model structure is developed as a prerequisite for PID controller development. After the best model is achieved, the proportional-integral-derivative-crow-search (PID-CS) performance was compared to a traditional tuning approach known as the Ziegler Nichols (ZN) to validate its robustness. The result revealed the PID-CS outperformed the proportional-integral-derivative-Ziegler Nichols (PID-ZN) with attenuation values of 44.75 and 42.74 dB in the first mode of vibration for single sinusoidal and real disturbances, respectively. In addition, the value of mean squared error (MSE) for PID-ZN and PID-CS for single sinusoidal disturbance are 0.0167 and 0.0081, respectively. Meanwhile, PID-ZN and PID-CS achieved 2.3981 x 10-4 and 2.3737 x 10-4 when they were exerted with real disturbance. This proves that the PID-CS is more accurate compared to the PID-ZN as it achieved the lowest MSE value.

关键词： Crow search proportional-integral-derivate controller swarm intelligence algorithm flexible structure active vibration control

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