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

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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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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Enhanced Differential Evolution: A Novel Strategy for Global Optimization and Its Application to Optimal Microgrid Planning

Journal of Network Intelligence

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Journal of Network intelligence 2025年第1期10卷 1-19页

作者： Lin, Jin-Yang Shi, Xiang-Yu Zhu, Xi Lin, Jun-De Nguyen, Trong-The Research Center for Microelectronics Technology Fujian University of Technology Fuzhou350118 China Multimedia Communications Lab. VNU-HCM University of Information Technology Vietnam National University Ho Chi Minh City700000 Viet Nam

This study proposes a novel strategy called Enhanced Crossover and Mutation Differential Evolution (ECMDE) to address the limitations of the Differential Evolution (DE) algorithm. DE often suffers from slow convergence speed, low accuracy, and vulnerability to local optima. The ECMDE algorithm aims to overcome these issues by employing adaptive crossover and mutation techniques. A chaotic mapping approach is used to initialize the population in the initialization stage, ensuring that the initial population is well-distributed across the search space. In the improved mutation operation, a new mutation strategy is proposed using a dynamic adaptive factor instead of F to address the late convergence issue. Additionally, an adaptive crossover operator is employed. To evaluate the effectiveness of the ECMDE algorithm, it is compared with other algorithms using the CEC2013 benchmark function. The results of the comparative study demonstrate that the ECMDE algorithm exhibits superior global convergence ability and faster convergence speed compared to the other algorithms tested. Finally, the ECMDE algorithm is applied to optimal microgrid planning issues, providing effective optimization performance and reducing the operating cost of the microgrid scheme. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Crossover and mutation strategy Differential Evolution Energy storage optimization Microgrid planning swarm intelligence algorithm

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An enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based learning

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第4期20卷 6422-6467页

作者： Wang, Yufei Zhang, Yujun Yan, Yuxin Zhao, Juan Gao, Zhengming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Acad Arts Jingmen 448000 Peoples R China Jingchu Univ Technol Inst Intelligent Comp Technol Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Hubei Engn Res Ctr Specialty Flowers Biol Breeding Jingmen 448000 Peoples R China

The aquila optimization algorithm (AO) is an efficient swarm intelligence algorithm proposed recently. However, considering that AO has better performance and slower late convergence speed in the optimization process. For solving this effect of AO and improving its performance, this paper proposes an enhanced aquila optimization algorithm with a velocity-aided global search mechanism and adaptive opposition-based learning (VAIAO) which is based on AO and simplified Aquila optimization algorithm (IAO). In VAIAO, the velocity and acceleration terms are set and included in the update formula. Furthermore, an adaptive opposition-based learning strategy is introduced to improve local optima. To verify the performance of the proposed VAIAO, 27 classical benchmark functions, the Wilcoxon statistical sign-rank experiment, the Friedman test and five engineering optimization problems are tested. The results of the experiment show that the proposed VAIAO has better performance than AO, IAO and other comparison algorithms. This also means the introduction of these two strategies enhances the global exploration ability and convergence speed of the algorithm.

关键词： aquila optimizer simplified aquila optimization algorithm swarm intelligence algorithm velocity-aided global search adaptive opposition-based learning engineering problem

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A salp swarm algorithm based on Harris Eagle foraging strategy

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 203卷 858-877页

作者： Zhang, Xuncai Wang, Shida Zhao, Kai Wang, Yanfeng Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China

For the operation of the salp swarm algorithm(SSA), the leaders fall into local optimum, which leads to the population falling into local optimum problem. In this paper, a salp swarm algorithm based on Harris Eagle foraging strategy is proposed(ISSAHF). Harris hawk optimization algorithms of four types of feeding mechanism are integrated into salp location update process optimization algorithm follower. To strengthen the exploitation of potential areas, complemented by a multi-point leadership crossover strategy to maintain a balance between exploitation and exploration. The performance of the proposed ISSAHF was compared, and Wilcoxon's statistical analysis was performed on 20 benchmark functions including unimodal, multimodal, and partial CEC2014. Finally, ISSAHF is applied to three practical engineering optimization problems to evaluate its optimization performance further.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Salp swarm algorithm Harris Eagle optimization algorithm Multi -leader strategy swarm intelligence algorithm

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FMFO: Floating flame moth-flame optimization algorithm for training multi-layer perceptron classifier

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APPLIED intelligence 2023年第1期53卷 251-271页

作者： Yang, Zhenlun Guangzhou Panyu Polytech Guangzhou Peoples R China

As one of the most popular artificial neural networks, multi-layer perceptron (MLP) has been employed to solve classification problems in many applications. The main challenge in MLP application is finding the ideal set of network connection weights and biases in the training process, which minimizes the error of MLP in processing datasets. To efficiently address this challenge, numerous swarm intelligence (SI) algorithms with powerful search capabilities have been adopted for training MLP classifiers. However, these existing algorithms often suffer from problems of local optima stagnation, premature convergence, and inefficient search. In this study, a novel floating flame moth-flame optimization (FMFO) algorithm with remarkable exploitation and exploration search capabilities is proposed, offering an advantageous option for training MLP classifiers. To verify the performance of the proposed FMFO in training MLP classifiers, the FMFO-based MLP training approach (FMFO-MLP) is evaluated on eleven classification datasets that represent a wide range of the variable dimension scale. In addition, some recently developed well-known and state-of-the-art SI algorithms are applied to compare with the proposed FMFO. Experimental results demonstrate that the proposed FMFO outperforms the other competing algorithms in terms of approximating the optimal objective function value and achieving classification accuracy. Moreover, the proposed FMFO achieves a competitive computational efficiency in the experiment, confirming that it is an efficient optimizer for training MLP classifiers in practical applications.

关键词： Moth-flame optimization swarm intelligence algorithm Multi-layer perceptrons Neural network Floating flame

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A novel elitist fruit fly optimization algorithm

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SOFT COMPUTING 2023年第8期27卷 4823-4851页

作者： He, Jieguang Peng, Zhiping Qiu, Jinbo Cui, Delong Li, Qirui Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming Peoples R China Guangdong Univ Petrochem Technol Coll Elect Informat Engn Maoming Peoples R China

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed.

关键词： swarm intelligence algorithm Fruit fly optimization algorithm Elite guidance Boundary information Population diversity

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swarm intelligence in Internet of Medical Things: A Review

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SENSORS 2023年第3期23卷 1466-1466页

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Izadi, Navid Hoseini Gravina, Raffaele Kabir, H. M. Dipu Nahavandi, Darius Alinejad-Rokny, Hamid Khosravi, Abbas Acharya, U. Rajendra Nahavandi, Saeid Fortino, Giancarlo Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia Fasa Univ Fac Engn Dept Comp Engn Vali asr Blvd Fasa *** Iran Isfahan Univ Technol Dept Elect & Comp Engn Daneshgah e Sanati Hwy Esfahan 83111 Iran Univ Calabria Dept Informat Modeling Elect & Syst DIMES I-87036 Cosenza Italy UNSW Grad Sch Biomed Engn Biomed Machine Learning Lab BML Sydney NSW 2052 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore 599494 Singapore Asia Univ Dept Biomed Informat & Med Engn Taichung 41354 Taiwan Harvard Univ Harvard Paulson Sch Engn & Appl Sci Allston MA 02134 USA

Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

关键词： internet of things internet of medical things internet of things in health swarm intelligence algorithm wearable devices wireless sensor network

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A swarm intelligence algorithm based game theory

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2013年第3期4卷 287-297页

作者： Bai, Yan-ping Zhang, Yu Capital Normal Univ Sch Management Beijing 100048 Peoples R China China Univ Min & Technol State Key Lab GeoMechan & Deep Underground Engn Beijing 100083 Peoples R China Beijing Univ Civil Engn & Architecture Dept Comp Teaching & Network Informat Beijing 100044 Peoples R China

In this paper, we established a relationship between particle swarm optimisation algorithms and game theory. On that basis, a swarm intelligence-based search mechanism is proposed and applied to solving the attribute reduction problem in the context of rough sets. The proposed attribute reduction algorithm can set up different participatory groups and game strategies, construct corresponding pay utility matrix, and produce optimal combinations through gaming procedure. Numerical experiments on a number of UCI datasets show the proposed game strategies-based reduction algorithm is superior to particle swarm optimisation, tabu search, gene algorithm and PSO with mutation operator in terms of solution quality, and has lower computational cost.

关键词： swarm intelligence algorithm rough set pay utility matrix game computing science

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Technical, economic, and environmental assessment of a stand-alone power system based on diesel engine with/without energy storage using an optimization algorithm: A case study in China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第27期31卷 38585-38602页

作者： Chen, Yujie Zhang, Shuo Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450000 Henan Peoples R China

In stand-alone power systems, technical, economic, and environmental (TEE) assessment of hybrid energy systems under uncertainty is an important issue. This paper focuses on the TEE assessment of a stand-alone hybrid energy system composed of photovoltaic (PV) and diesel generator (DG) with/without battery energy storage (BS) in remote islands in China. So, determining the optimal sizes of PV and DG with/without BS for economic, reliable, and efficient operation of a hybrid power system in a microgrid is important. For this goal, a modified swarm intelligence algorithm is used to optimize, techno-economic feasibility and avoid potential CO2 emission. To demonstrate the effectiveness of the modified swarm intelligence algorithm, it is compared with the standard swarm intelligence method and simple simulated annealing method in terms of operational cost reduction and power loss reduction. The aim of the optimization is to minimize the cost of a stand-alone solar power system based on diesel engine with/without battery energy storage system by optimal determination of the load uncertainty and CO2 emission. The optimal results are developed further by performing sensitivity analysis, such as the effect of the fuel cost and the penalty cost of CO2 emission. Over the case study, simulation results show that the proposed algorithm obtains more promising results in terms of TEE aspects. The reliability, low carbon, and cost-effectiveness of stand-alone solar power systems based on diesel engine with battery energy storage system can be easily calculated using the correlations derived in this analysis. The resulting cost of energy is in the range of 0.2845 to 0.6492 $/kWh.

关键词： Stand-alone power system swarm intelligence algorithm Techno-economic and environmental feasibility Penalty cost of CO2 emission Fuel cost

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