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An arithmetic optimization algorithm with balanced diversity and convergence for multimodal multiobjective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Zhang, Ruyue Mao, Shuhua Zhao, Shangrui Liu, Chang Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Yantai Huang Bohai New Area Nat Resources & Planni Yantai 265500 Peoples R China

Multimodal multiobjective optimization problems are widely prevalent in real life. Addressing these challenges is crucial as they directly impact the efficiency and effectiveness of solutions across various domains. This paper proposes a novel Multi-Modal Multi-Objective Arithmetic optimization algorithm (MMOP-AOA), aimed at achieving a high balance between diversity and convergence in both decision and objective spaces. Arithmetic optimization algorithm (AOA) is a highly competitive metaheuristic optimization algorithm with strong exploration and exploitation capabilities. MMOP-AOA extends the AOA for the first time to solve multimodal multiobjective problems, with the following ideas: Firstly, a new exploration and exploitation strategy (NBC-NEE) is designed based on the characteristics of *** strategy utilizes Neighborhood-Based Clustering (NBC) to partition the decision space into multiple clusters, aiding MMOP-AOA in capturing more equivalent Pareto subsets (ePSs). Secondly, a convergence and diversity balance mechanism (CDBM) is developed. This mechanism involves comparing the convergence indicator and diversity indicator to select different mutation strategies. Thirdly, an improved crowding distance (ICD) is proposed to address the deficiencies of existing special crowding distance measures. The effectiveness of CDBM and ICD is demonstrated in the paper through experiments on 22 benchmark functions from CEC-2019 and a real-world problem of signal timing optimization at road intersections. The research also reveals that compared to four other advanced multimodal multiobjective optimization algorithms, MMOP-AOA exhibits superior search capability and stability. Furthermore, MMOP-AOA utilizes Neighborhood-Based Clustering (NBC) to partition the decision space into multiple clusters, aiding MMOP-AOA in capturing more equivalent Pareto subsets (ePSs) and provides a theoretical framework for other metaheuristic optimization algorithms to tackle multimodal multi

关键词： Multimodal optimization Arithmetic optimization algorithm Multiobjective optimization metaheuristic optimization algorithm

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A Novel Hybrid GOA-XGB Model for Estimating Wheat Aboveground Biomass Using UAV-Based Multispectral Vegetation Indices

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REMOTE SENSING 2022年第14期14卷 3506-3506页

作者： Han, Yixiu Tang, Rui Liao, Zhenqi Zhai, Bingnian Fan, Junliang North West Agr & Forestry Univ Coll Resources & Environm Yangling 712100 Shaanxi Peoples R China Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China North West Agr & Forestry Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Minist Agr Key Lab Plant Nutr & Agrienvironm Northwest China Yangling 712100 Shaanxi Peoples R China

The rapid and nondestructive determination of wheat aboveground biomass (AGB) is important for accurate and efficient agricultural management. In this study, we established a novel hybrid model, known as extreme gradient boosting (XGBoost) optimization using the grasshopper optimization algorithm (GOA-XGB), which could accurately determine an ideal combination of vegetation indices (VIs) for simulating wheat AGB. Five multispectral bands of the unmanned aerial vehicle platform and 56 types of VIs obtained based on the five bands were used to drive the new model. The GOA-XGB model was compared with many state-of-the-art models, for example, multiple linear regression (MLR), multilayer perceptron (MLP), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), random forest (RF), support vector machine (SVM), XGBoost, SVM optimization by particle swarm optimization (PSO), SVM optimization by the whale optimization algorithm (WOA), SVM optimization by the GOA (GOA-SVM), XGBoost optimization by PSO, XGBoost optimization by the WOA. The results demonstrated that MLR and GOA-MLR models had poor prediction accuracy for AGB, and the accuracy did not significantly improve when input factors were more than three. Among single-factor-driven machine learning (ML) models, the GPR model had the highest accuracy, followed by the XGBoost model. When the input combinations of multispectral bands and VIs were used, the GOA-XGB model (having 37 input factors) had the highest accuracy, with RMSE = 0.232 kg m(-2), R-2 = 0.847, MAE = 0.178 kg m(-2), and NRMSE = 0.127. When the XGBoost feature selection was used to reduce the input factors to 16, the model accuracy improved further to RMSE = 0.226 kg m(-2), R-2 = 0.855, MAE = 0.172 kg m(-2), and NRMSE = 0.123. Based on the developed model, the average AGB of the plot was 1.49 +/- 0.34 kg.

关键词： metaheuristic optimization algorithm feature selection XGBoost Gaussian process regression aboveground biomass mapping

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Systematic Development of a Multi-Objective Design optimization Process Based on a Surrogate-Assisted Evolutionary algorithm for Electric Machine Applications

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ENERGIES 2023年第1期16卷 392页

作者： Choi, Mingyu Choi, Gilsu Bramerdorfer, Gerd Marth, Edmund Inha Univ Dept Elect Engn Incheon 22212 South Korea Johannes Kepler Univ Linz Inst Elect Drives & Power Elect A-4040 Linz Austria

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method.

关键词： electric machine design multi-objective design optimization interior permanent magnet synchronous machine (IPMSM) surrogate model (SM) metaheuristic optimization algorithm

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Dynamic Hunting Leadership optimization: algorithm and applications

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 69卷

作者： Ahmadi, Bahman Giraldo, Juan S. Hoogsteen, Gerwin Univ Twente Dept Elect Engn Math & Comp Sci Enschede Netherlands

The Dynamic Hunting Leadership (DHL) algorithm is an innovative heuristic technique that draws inspiration from nature to find almost optimal solutions for various optimization problems. It consists of four variants, each highlighting distinct leadership strategies to guide the hunting process. The development of the algorithm was based on the realization that effective leadership during the hunting process can significantly improve its efficacy. The concept behind these methods is to dynamically modify the number of leaders, which can enhance the algorithm's performance. The stability of DHL variants in exploring the unknown area of the search space and exploitation phases is compared, and the advantages of exploration or exploration ability for the different variants of DHL are discussed. Moreover, the results are compared with more than twenty well-known algorithms. The efficacy of the proposed algorithms in discovering near-optimal solutions is tested across several real-world applications, and the outcomes demonstrate that the DHL algorithm outperforms other competing algorithms. The distinctiveness of the DHL algorithm is its ability to identify the global minimum on various benchmark problems and its superior performance in enhancing the objective value for the welded beam design problem and tension-compression spring problem, surpassing the values achieved by other algorithms. The algorithms' performance is also tested on an optimal allocation problem of distributed generation (DG) and energy storage system (ESS) for balanced electrical distribution systems. The results show that all four variants of DHL obtained the global minimum for the problem. For the optimal control strategy problem for voltage regulators in three-phase unbalanced power systems, the DHL algorithm improved the objective value by 35.7% compared to the best results found by other algorithms. Based on the analysis and comparison of the best objective values and convergence behavior for all

关键词： metaheuristic optimization algorithm Nature-inspired computing optimization Power system planning Swarm intelligence

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metaheuristics-optimized ensemble system for predicting mechanical strength of reinforced concrete materials

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STRUCTURAL CONTROL & HEALTH MONITORING 2021年第5期28卷 e2706-e2706页

作者： Chou, Jui-Sheng Nguyen, Ngoc-Mai Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei Taiwan

This paper develops a novel artificial intelligence (AI)-based approach, called the metaheuristics-optimized ensemble system (MOES), to assist civil engineers significantly in achieving accurate estimations of the mechanical strength of reinforced concrete (RC) materials. MOES integrates the advantages of hybrid and ensemble models by combining a metaheuristic optimization algorithm and efficient AI models. The metaheuristic algorithm finds the optimal hyperparameters of individual AI techniques and simultaneously adjusts their weights to yield the best optimized-weight-ensemble model. Particularly, the developed MOES was established by integrating the forensic-based investigation optimization algorithm, the radial basis function neural network, and the least squares support vector regression. Four case studies of predicting structural mechanics of RC beams were performed to evaluate the performance of MOES and compare it to those of other single AI models, conventional ensemble models, hybrid models, and empirical methods. The analytical results of cross-validation reveal that MOES was the most reliable approach, achieving the best values of all performance evaluation indexes. The automated predictive analytics revealed the robustness, efficiency, and stability of MOES. Thus, the proposed approach is a highly promising tool for predicting the structural mechanics of RC beams. The success of MOES in estimating the mechanical strength of RC beams has redefined the way of optimizing an ensemble AI model, which is the primary contribution of this research to the relevant body of knowledge.

关键词： automated predictive analytics machine learning metaheuristic ensemble of hybrid learning metaheuristic optimization algorithm reinforced concrete structural mechanics

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Deploying UAV-based detection of bridge structural deterioration with pilgrimage walk optimization-lite for computer vision

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Chou, Jui-Sheng Liu, Chi-Yun Guo, Pin-Jun Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei Taiwan

Bridges are crucial components of national infrastructure, requiring rigorous maintenance and inspections to ensure their safety and functionality. Inspections are incredibly challenging and costly when accessing elevated bridge decks and substructures, particularly over riverbeds. To address these challenges, this study introduces a novel system integrating advanced computer vision-based deep learning, metaheuristic optimization, and Unmanned Aerial Vehicle (UAV) technology to revolutionize bridge inspections. This system uses UAVs to capture high-resolution images, which are then processed by the You Only Look Once (YOLO) models for instance segmentation. The YOLOv7 model, fine-tuned with the Pilgrimage Walk optimization (PWO)-Lite algorithm, achieved the highest accuracy, recording a 65.6 % mAP50 on the testing set. The PWOLite algorithm enhanced the YOLOv7's performance, increasing mean average precision by 4.1 % compared to YOLOv7 with augmented images and 13.9 % compared to YOLOv7 using original photos. These improvements significantly boost the model's generalization capability. This integration facilitates precise, automated deterioration quantification and supports accurate, efficient maintenance cost estimation. Explicitly designed for detecting underside deterioration in composite bridges, this system provides bridge management authorities and construction firms with a comprehensive tool. Our findings indicate improvements in predictive maintenance for bridges, offering actionable insights that aid informed decision-making in bridge inspections and maintenance planning. This advancement enhances the sustainability and safety of bridge infrastructure, representing a substantial step forward in infrastructure management and UAV applications.

关键词： Bridge deck and substructure Bridge inspection Unmanned aerial vehicle Computer vision-based deep learning Instance segmentation metaheuristic optimization algorithm Engineering information integration Maintenance cost estimation system

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Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks

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INTERNATIONAL JOURNAL OF COAL GEOLOGY 2023年第1期275卷

作者： Nguyen, Hoang Bui, Xuan-Nam Topal, Erkan Hanoi Univ Min & Geol Dept Surface Min Min Fac 18 Vien StrDuc Thang Ward Hanoi 100000 Vietnam Hanoi Univ Min & Geol Duc Thang Ward Innovat Sustainable & Responsible Min ISRM Res Grp 18 Vien Str Hanoi 100000 Vietnam Curtin Univ Western Australian Sch Mines WASM Min Engn Met Engn Dept Perth WA Australia

The objective of this paper is to present a method for predicting blast-induced ground vibration in open-pit mines that is based on the use of self-organizing neural networks (SONIA) and metaheuristic algorithms. In order to improve the accuracy of the SONIA model, several metaheuristic algorithms were employed, including the Manta Ray Foraging optimization (MRFO), Hunger Games Search (HGS), Aquila optimization (AO), and Naked Mole Rat algorithm (NMRA). Additionally, the k-fold cross-validation technique was used to identify the best parameters for the algorithms, which were then used to retrain the models for predicting blast-induced ground vibration. The effectiveness of the proposed method was evaluated using a case study of an open-pit coal mine in Vietnam, which had 288 blasting events. The study found that SONIA was a suitable neural network for predicting blast-induced ground vibration due to its self-organizing structure, even with a small dataset containing complex relationships. However, the SONIA model could be further optimized using the metaheuristic algorithms to improve its accuracy. The study found that the MRFO-SONIA model was the most reliable and accurate, with the lowest error (MAE = 0.379, RMSE = 0.453, MAPE = 0.08) and the highest reliability (R2 = 0.896). Meanwhile, the HGS-SONIA, AO-SONIA, and NMRA-SONIA models provided lower performance, with MAE values of 0.455, 0.500, and 0.492, RMSE values of 0.552, 0.603, and 0.580, MAPE values of 0.100, 0.112, and 0.111, and R2 values of 0.845, 0.815, and 0.829, respectively. The results of this study demonstrated the potential of using metaheuristic-based SONIA models to enhance predictions of blast-induced ground vibration in open-pit mines. This approach could prove useful in other operations in open-pit mines where there is a need to predict vibrations or other disturbances/negative effects resulting from specific mining activities.

关键词： Peak particle velocity Blast-induced ground vibration Self-organizing neural network metaheuristic optimization algorithm Open-pit coal mine Applied soft computing

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Pioneer Pareto artificial bee colony algorithm for three-dimensional objective space optimization of composite-based layered radar absorber

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APPLIED SOFT COMPUTING 2020年 96卷 106696-106696页

作者： Toktas, Abdurrahim Ustun, Deniz Erdogan, Nursev Karamanoglu Mehmetbey Univ Fac Engn Dept Elect & Elect Engn TR-70200 Karaman Turkey Tarsus Univ Fac Engn Dept Comp Engn TR-33400 Tarsus Mersin Turkey Turkish Aerosp Ind Inc Adv Mat Proc & Energy Technol Ctr TR-06980 Ankara Turkey

A three-dimensional objective space (3DOS) optimization strategy using an enhanced multi-objective artificial bee colony (ABC) algorithm for the design optimization of layered radar absorbing material (LRAM) is presented in this study. The multi-objective exploitation ability of ABC is improved with regard to the convergence and diversity by integrating a pioneer Pareto (PP) solution to the onlooker bee phase, which is selected from the Pareto optimal set. Initially, the performance of PP-ABC is successfully verified by a comparison with ABC and the well-known multi-objective counterparts like particle swarm optimization (PSO) and differential evolution (DE) algorithms. The comparison is carried out through five multi-objective benchmark functions with respect to three favorable and reliable multi-objective indicators such as hypervolume (HV), HV ratio and Pareto sets proximity (PSP). The employed three objective functions to be the dimensions of 3DOS are weighted bandwidth-based total reflection coefficient involving sub-reflection waves of a wide oblique incident angular range 0 degrees-75 degrees, the total thickness and the number of layers. By using PP-ABC, a 3D designed LRAM operating at a large frequency band of 2-18 GHz is then designed for synchronously minimizing the three objective vectors by finding out the design variables: thickness and material types. Meanwhile, the material types of the proposed LRAM are optimally picked up from a composite material database with 51 specimens from 9 previously reported studies (51 /9#database). In order to point out the effectiveness of the proposed 3DOS optimization strategy, three LRAMs are also compared with respective reported designs whose material type is selected from a database with 6 specimens (6/1#database). The results show that the proposed LRAMs are hence the global optimal designs in terms of all objective functions thanks to the proposed 3DOS optimization strategy based on PP-ABC. (C) 2020 Elsevier B.V

关键词： metaheuristic optimization algorithm Multi-objective optimization Enhanced algorithm Pareto optimality Radar absorbing material (RAM) Layered RAM

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Novel Selective Mapping with Oppositional Hosted Cuckoo optimization algorithm for PAPR Reduction in 5G UFMC Systems

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2022年第2期29卷 464-471页

作者： Jayamathi, A. Jayasankar, T. Kumar, K. Vinoth KSR Coll Engn Dept ECE Tiruchengode India Anna Univ Dept ECE Univ Coll Engn BIT Campus Tiruchirappalli Tamil Nadu India SSM Inst Engn & Technol Dept Elect & Commun Engn Dindigul 624002 Tamil Nadu India

In recent times, there is a continuous requirement of achieving high data rates owing to an increase in the number of devices and significant demand for various services with maximum reliability and minimum delay. It results in the development of fifth generation (5G) to offer better services with enhanced data rate. Recently, a major alternative to OFDM technology for 5G networks called universal filtered multi-carrier (UFMC) is presented where every individual sub-band is filtered that reduces the OOB radiation and eliminates guard band. But high peak-to-average power ratio (PAPR) is a crucial issue which arises from the utilization of several subcarriers to generate the time domain transmission signal. For resolving this issue, this paper presents a novel selective mapping with oppositional hosted cuckoo optimization (SM-OHOCO) algorithm for PAPR reduction in 5G UFMC systems. In the SM-OHOCO algorithm, rather than the generation of several random phase sequences, SM-OHOCO algorithm is performed iteratively to attain a better solution with few searching rounds, showing the novelty of the work. As the optimization of phase sequence in the SLM technique is considered as an NP hard optimization problem, the OHOCO algorithm is applied, which is derived by incorporating the concepts of the HOCO algorithm with oppositional based learning (OBL) strategy. To validate the effective performance of the proposed SM-OHOCO algorithm, an extensive experimental analysis is performed to highlight the improved performance in 5G networks. The resultant values pointed out the superior outcome of the proposed SM-OHOCO algorithm over the other existing methods in terms of distinct measures

关键词： metaheuristic optimization algorithm PAPR reduction selective mapping universal filtered multi-carrier 5G networks

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Negative Gradient Differential Biogeography-based optimization for Mobile Robot Path Planning

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2022年第5期31卷 2250030-2250030页

作者： Wang, Jiaqian Na, Xiaodong Li, Zhihui Han, Min Li, Decai Dalian Univ Technol Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Liaoning Peoples R China Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Minist Educ Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Profess Technol Innovat Ctr Distributed Control I Dalian 116024 Peoples R China

Mobile robot path planning (MRPP) plays an irreplaceable role in the process of intelligent robots and practical artificial intelligence. The traditional global path planning methods have some shortcomings, such as difficulty in digging into environmental information and finding the optimal path effectively. To solve the above problems, this paper proposes a negative gradient differential biogeography-based optimization (NG-DBBO), which has strong local search ability and global optimization ability. Firstly, we present a differential migration approach to increase the population diversity in the iterative process of NG-DBBO, which can realize the information sharing between feature solutions effectively. Then a negative gradient descent strategy based on negative gradient descent is introduced to improve the learning rate, which not only enhances initial global search ability, but also avoids premature convergence. Noteworthily, the convergence of the algorithm is analyzed for single-peak and multi-peak problems respectively. After that, NG-DBBO is combined with the cubic spline interpolation to realize MRPP by the defined coding method and fitness function. The simulation experiments are used to demonstrate the availability of our method, which consist of two parts. In the first part, we select 23 benchmark functions to verify the accuracy and convergence speed of the NG-DBBO algorithm. The practicability of path planning in different environments is demonstrated in the second part.

关键词： Mobile robot path planning biogeography-based optimization negative gradient strategy differential evolution metaheuristic optimization algorithm

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