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Golden Jackal optimization With Joint Opposite Selection: An Enhanced nature-inspired optimization algorithm for Solving optimization Problems

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IEEE ACCESS 2022年 10卷 128800-128823页

作者： Arini, Florentina Yuni Sunat, Khamron Soomlek, Chitsutha Univ Negeri Semarang Dept Comp Sci Semarang Indonesia Khon Kaen Univ Coll Comp Khon Kaen Thailand

This paper presents the logical relationships of Aristotle's square of opposition on four basic categorial prepositions (i.e., contrary, contradictory, subcontrary, and subaltern) of Joint Opposite Selection (JOS). JOS brings a mutual reinforcement by a joint of the two opposition strategies Dynamic Opposite (DO) and Selective Leading Opposition (SLO). The DO and SLO improve the balance of exploration and exploitation, respectively, in a given search space. We also propose an enhancement of Golden Jackal optimization (GJO) with a Joint Opposite Selection named GJO-JOS. In the optimization process, JOS assists GJO in assaulting the prey swiftly using SLO. DO assists GJO in finding better chances to locate the fittest prey. With JOS, the GJO succeeds in elevating its performance. We evaluated the performance of GJO-JOS on the CEC 2017 benchmark functions. The benchmark includes unimodal, multimodal, hybrid, and composition functions. The evaluation results of GJO-JOS were better than GJO using each of the seven single opposition-based learning strategies (OBLs). We also compared GJO-JOS to eight nature-inspired algorithms including the original version of GJO. GJO-JOS produced promising results among seven single OBLs, eight nature-inspired algorithms, and GJO. The experimental results confirmed that GJO-JOS effectively generated equilibrium in the balance mechanism.

关键词： Joint opposite selection nature-inspired optimization algorithm opposition-based learning unconstraint optimization problem

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Wind Driven optimization (WDO): A Novel nature-inspired optimization algorithm and its Application to Electromagnetics

Wind Driven Optimization (WDO): A Novel Nature-Inspired Opti...

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2010 IEEE International Symposium Antennas and Propagation/CNC-USNC/URSI Radio Science Meeting

作者： Bayraktar, Zikri Komurcu, Muge Werner, Douglas H. Penn State Univ Dept Elect Engn University Pk PA 16802 USA Penn State Univ Dept Meteorol University Pk PA 16802 USA

ISBN: (纸本)9781424449682

This paper introduces a novel nature-inspired global optimization technique, which we call Wind Driven optimization. WDO is a population based iterative global optimization method, where the velocity and the position of wind controlled air parcels are updated based on the physical equations that govern atmospheric motion. The effectiveness of WDO as a tool in optimization problems is first shown with its performance on benchmark functions and then with its application to optimizing the design for a thin double-sided AMC surface at 10 GHz.

关键词： atmospheric motion double-sided AMC surface electromagnetics electromagnetism frequency 10 GHz iterative global optimization method nature-inspired optimization algorithm optimisation wind driven optimization

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Artificial hummingbird algorithm: Theory, variants, analysis, applications, and performance evaluation

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COMPUTER SCIENCE REVIEW 2025年 56卷

作者： Sasmal, Buddhadev Das, Arunita Dhal, Krishna Gopal Saha, Ramesh Rai, Rebika Bharasa, Totan Houssein, Essam H. Midnapore City Coll Dept Pure & Appl Sci Paschim Medinipur W Bengal India Midnapore Coll Autonomous Dept Comp Sci Paschim Medinipur W Bengal India Indian Inst Informat Technol IIIT Sonepat Dept Comp Sci & Engn Sonipat Haryana India Sikkim Univ Dept Comp Applicat Gangtok Sikkim India Belda Coll Dept Nat Sci Paschim Medinipur W Bengal India Minia Univ Fac Comp & Informat Al Minya Egypt

The Artificial Hummingbird algorithm (AHA) is a metaheuristic optimization technique inspired by the behaviours and foraging strategies of hummingbirds. Known for their extraordinary agility and accuracy in collecting nectar, hummingbirds provide an exemplary framework for tackling complex optimization problems. Developed by Zhao et al. in 2022, AHA has swiftly attracted interest within the research community because to its exceptional performance and adaptability. This study provides a detailed and comprehensive review of AHA, exploring the diverse versions and modifications published in multiple research papers since its inception in 2022, with 23 % appearing in international conference papers and 75% in esteemed peer-reviewed journals. The variants of AHA covered in this paper include 55 % of classical AHA, 17 % of improved AHA, 11 % of hybridization, 2 % of binary, 15 % of multi-objective variants, respectively. Furthermore, the applications of AHA illustrate its effectiveness and adaptability across various fields, with 42 % in power and control engineering, 11 % in optimizing deep learning models, 10 % in engineering design challenges, and 8 % in renewable energy sources. The algorithm has been utilized substantially in the domain of IoT, wireless sensor networks, wind energy, and fog computing. Furthermore, we also evaluate the performance of the AHA in the image clustering domain, and the findings revealed that the AHA performs better in comparison to the other tested methods. The main objectives of this study are to deliver a comprehensive review of AHA, emphasizing its novel methodology, and analyzing its various variants and their applications in numerous fields. As nature-inspired optimization methods continue to evolve, this survey paper expected to serves as a valuable resource for researchers aiming to gain a comprehensive understanding of AHA, its progression, and its diverse applications in solving complex optimization problems.

关键词： Artificial hummingbird algorithm nature-inspired optimization algorithm Metaheuristic algorithm optimization Swarm intelligence

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Nonconvex Compressed Sensing by nature-inspired optimization algorithms

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第5期45卷 1028-1039页

作者： Liu, Fang Lin, Leping Jiao, Licheng Li, Lingling Yang, Shuyuan Hou, Biao Ma, Hongmei Yang, Li Xu, Jinghuan Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China

The l(0) regularized problem in compressed sensing reconstruction is nonconvex with NP-hard computational complexity. Methods available for such problems fall into one of two types: greedy pursuit methods and thresholding methods, which are characterized by suboptimal fast search strategies. nature-inspired algorithms for combinatorial optimization are famous for their efficient global search strategies and superior performance for nonconvex and nonlinear problems. In this paper, we study and propose nonconvex compressed sensing for natural images by nature-inspired optimization algorithms. We get measurements by the block-based compressed sampling and introduce an overcomplete dictionary of Ridgelet for image blocks. An atom of this dictionary is identified by the parameters of direction, scale and shift. Of them, direction parameter is important for adapting to directional regularity. So we propose a two-stage reconstruction scheme (TS_RS) of nature-inspired optimization algorithms. In the first reconstruction stage, we design a genetic algorithm for a class of image blocks to acquire the estimation of atomic combinations in all directions;and in the second reconstruction stage, we adopt clonal selection algorithm to search better atomic combinations in the sub-dictionary resulted by the first stage for each image block further on scale and shift parameters. In TS_RS, to reduce the uncertainty and instability of the reconstruction problems, we adopt novel and flexible heuristic searching strategies, which include delicately designing the initialization, operators, evaluating methods, and so on. The experimental results show the efficiency and stability of the proposed TS_RS of nature-inspired algorithms, which outperforms classic greedy and thresholding methods.

关键词： Clonal selection algorithm (CSA) genetic algorithm (GA) nature-inspired optimization algorithm nonconvex compressed sensing overcomplete dictionary structured sparsity

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Analysis of Marine Predators algorithm using BIAS toolbox and Generalized Signature Test

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ALEXANDRIA ENGINEERING JOURNAL 2024年 95卷 38-49页

作者： Kumar, Manish Rajwar, Kanchan Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Indian Inst Technol Roorkee Mehta Family Sch Data Sci & Artificial Intelligenc Roorkee 247667 Uttarakhand India

The Marine Predators algorithm (MPA) is a prominent nature-inspired optimization algorithm (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using the L & eacute;vy or Brownian approach for its foraging strategy. Despite its acclaim, the structural bias within MPA has not been thoroughly investigated, marking a significant gap in the current research. This absence of targeted research forms the core rationale behind initiating this study. Structural bias has recently been identified in NIOAs, causing the population to revisit specific regions of the search space without gaining new information. As a result, it may lead to increased computational costs and slow down the rate of convergence. Therefore, identifying structural bias is essential to better understand the search mechanism of MPA. To ascertain the presence of any structural bias, two recently introduced models are employed: the BIAS toolbox and the Generalized Signature Test. These examinations reveal a notable structural bias in MPA, predominantly towards the center of the search space. Also, possible future research directions for MPA are discussed. Our findings provide valuable insights into the search dynamics of the algorithm, fostering the development of new, unbiased, and efficient algorithms.

关键词： Metaheuristic algorithm nature-inspired optimization algorithm (NIOA) Marine Predator algorithm (MPA) Structural bias BIAS toolbox Generalized Signature Test (GST)

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Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil

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JOURNAL OF EARTH SYSTEM SCIENCE 2024年第3期133卷 159页

作者： Thapa, Ishwor Ghani, Sufyan Sharda Univ Dept Civil Engn Greater Noida India

This study presents a pioneering approach that combines artificial intelligence and a nature-inspired optimization algorithm to predict soil unconfined compressive strength (UCS). The traditional laboratory-based method of UCS measurement, involving soil sample preparation, is time-consuming, labour-intensive, and prone to low accuracy. In this work, we propose a non-destructive soil UCS measurement technique utilizing robust AI-based models based on ensemble learning and hybrid learning techniques. Support vector machine (SVM) coupled with particle swarm optimization (PSO), extreme gradient boost (XGB), K-nearest neighbour (KNN), and nature-inspired optimization algorithm-based six hybrid ANFIS models, employing input features from experimental data, were adopted for UCS prediction. Model performance was assessed using standard metrics such as root mean square error, mean absolute error, variance account factor (VAF), expanded uncertainty (U95), and coefficient of determination (R2) between predicted and actual unconfined compressive strength. The study employed 274 data points generated in our laboratory. Sensitivity analysis and Pearson correlation techniques were employed to select relevant elements as input features. Fine content, coarse content, liquid limit, plastic limit, plasticity index, and cohesion of soil were identified as the most effective configurations for accurate soil UCS predictions. XGB demonstrated the highest prediction efficiency in the training and testing phase, achieving an impressive R2 of 99.2 and 96.8%, respectively. The results also emphasize the importance of the selected features. The experimental validation accuracy of 97% for the developed XGB model, whose data were not used during model calibration and verification, confirmed the generalization capability of the models. This study provides valuable insights for policymakers and industry stakeholders, facilitating optimized soil unconfined strength management practices.

关键词： Unconfined compression strength artificial intelligence nature-inspired optimization algorithm ensemble learning K-nearest neighbour

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Joint Opposite Selection (JOS): A premiere joint of selective leading opposition and dynamic opposite enhanced Harris' hawks optimization for solving single-objective problems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 188卷 116001-116001页

作者： Arini, Florentina Yuni Chiewchanwattana, Sirapat Soomlek, Chitsutha Sunat, Khamron Khon Kaen Univ Dept Comp Sci Fac Sci Khon Kaen Thailand Khon Kaen Univ Adv Smart Comp Lab AIRC Khon Kaen Thailand

In this paper, we proposed Joint Opposite Selection (JOS) operator that is a joint of two opposition learning techniques: the Selective Leading Opposition (SLO) and the Dynamic Opposite (DO). SLO uses a linearly decreasing threshold value to determine the close distance dimension of the search agents. DO provides the search agents chances to expand their abilities in the search space. We applied JOS to the Harris Hawks Opti-mization (HHO), the performance is increased because JOS balances the capability of exploration phase by using SLO and exploitation phase by using DO. The new algorithm, named Harris' Hawks optimization-Joint Opposite Selection (HHO-JOS), is also proposed in this research as an enhanced version of HHO to solve single-objective problems. When the hawks deploy JOS, SLO assists the hawks to succeed in exploitation phase by changing their close distance dimension and DO tries to diverse the search space range of the hawks in the exploration phase using a Random Jump Strategy (RJS). The sufficient Jumping rate (Jr) of DO in HHO-JOS is 0.25, according to our experimental results. The proposed algorithm was included in a competition conducted on 30 benchmark functions of CEC 2014 and 29 benchmark functions of CEC 2017. Both benchmarks contain collections of single-objective problems for real parameter numerical optimization. The problems were employed to evaluate and compare the proposed HHO-JOS to the original HHO, three variations of OBLs embedded in the original HHO, and 31 nature-inspired algorithms by using a scoring metric. The results of the competition showed that the premiere JOS on HHO consistently achieves robustness performance on CEC 2014 and CEC 2017. Comprehensive statistical analysis also demonstrated that HHO-JOS can compete with many leading optimization algorithms. Therefore, we can conclude that the proposed joint opposite selection is well-matched to HHO and succeeds in elevating HHO-JOS.

关键词： Harris' Hawks optimization (HHO) Joint Opposite Selection (JOS) Selective Opposition (SO) Dynamic Opposite (DO) Selection Leading Opposition (SLO) nature-inspired optimization algorithm

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A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications

Franklin Open

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Franklin Open 2024年 8卷

作者： Hassan, Ibrahim Hayatu Abdullahi, Mohammed Isuwa, Jeremiah Yusuf, Sahabi Ali Aliyu, Ibrahim Tetengi Institute for Agricultural Research Ahmadu Bello University Zaria Nigeria Department of Computer Science Ahmadu Bello University Zaria Nigeria Computer Science Deparment Federal University Kashere Gombe State Nigeria

Metaheuristic algorithms are commonly used in solving complex and NP-hard optimization problems in various fields. These algorithms have become popular because of their ability to explore and exploit solutions in various problem domains. Honey Badger algorithm (HBA) is a population-based metaheuristic optimization algorithm inspired by the dynamic hunting strategy of honey badgers, utilizing honey and digging-seeking techniques. Since its introduction in 2020, HBA has garnered widespread attention and has been applied across various domains. This review aims to comprehensively survey the improvement and application of HBA in solving various optimization problems. Additionally, the survey conducts a meta-analysis of the HBA's improvements, hybridization and application since its introduction. According to the result of the survey, 52 studies presented improved HBA using chaotic maps, levy flight mechanism, adaptive mechanisms, transfer functions, multi-objective mechanism and opposition based learning techniques, 20 studies presented a hybrid HBA with other metaheuristics algorithms and 101 studies uses the original HBA for solving various optimization problems. According to the survey, the wide acceptance of the HBA within the research community stems from its straightforwardness, ease of use, efficient computational time, accelerated convergence speed, high efficacy, and capability to address different kind of optimization issues, distinguishing it from the well-known optimization approches presented. © 2024

关键词： Honey badger algorithm Metaheuristic algorithms nature-inspired optimization algorithm Population-based optimization algorithms

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Aquila-particle swarm based cooperative search optimizer with superpixel techniques for epithelial layer segmentation

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APPLIED SOFT COMPUTING 2023年第PartA期149卷

作者： Sasmal, Buddhadev Das, Arunita Dhal, Krishna Gopal Ray, Swarnajit Midnapore Coll Autonomous Dept Comp Sci & Applicat Paschim Medinipur West Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata West Bengal India

The segmentation of epithelial layers from oral histopathology images plays a crucial role for early detection of oral cancer disease. As a result, more accurate segmentation of this layer is of the utmost importance for a Computer Aided Diagnosis (CAD) system. Therefore, this paper presents a superpixel image-based clustering technique using an improved nature-inspired optimization algorithm (NIOA), called the Cooperative Search (CS) algorithm. Here, superpixel image is utilized to construct a faster and noise-resistant oral histopathology image clustering method. The designed CS, on the other hand, is based on two popular NIOAs, the Aquila Optimizer (AO) and the Particle Swarm Optimizer (PSO), has better exploration-exploitation abilities. This CS has been used in the image clustering field to circumvent the problem of local optima trapping. On the other hand, a comparative investigation of three popular superpixel strategies with the proposed CS optimizer has been conducted to determine the optimal superpixel strategy for epithelial layer segmentation from clean as well as noisy oral histopathology images. Finally, the optimal superpixel strategy with CS has been tested with other state-of-the-art image segmentation techniques in the epithelial layer segmentation domain. The results obtained by the proposed segmentation technique are 98.82%, 96.70%, 97.60%, and 95.25%, in terms of average Accuracy, MCC, Dice, and Jaccard, respectively, which all are better than the cutting-edge segmentation methods. The proposed segmentation model is also evaluated over the leaf segmentation model and achieved better results visually and numerically. The optimization efficiency of the CS has also been measured over CEC2019 mathematical benchmark test functions and produced competitive results compared to other tested NIOAs.

关键词： Oral pathology Image segmentation Superpixel nature-inspired optimization algorithm Swarm intelligence Clustering

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Waypoint Mobile Robot Exploration Based on Biologically inspired algorithms

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IEEE ACCESS 2020年 8卷 190342-190355页

作者： Kamalova, Albina Kim, Ki Dong Lee, Suk Gyu Yeungnam Univ Dept Elect Engn Gyongsan 38541 South Korea

This article proposes stochastic exploration algorithms for mobile robot exploration problems. Navigation with uncertain conditions in the absence of initial parameters is a situation wherein precomputation and prediction are impossible for a robot. Therefore, stochastic optimization techniques were applied to find the optimal solution for the robot exploration problem. Driving to the unknown areas, the robot updates the frontier line of sensor visibility during the exploration mission. The points of the frontier line are assumed as the swarm population with their own positions and costs, which allows the computation of the next global waypoint. The calculation of global waypoints is carried out by a nature-inspired optimization algorithm that can place a waypoint in uncertainties. This study offers to apply three metaheuristic algorithms individually, such as Whale optimization, Grey Wolf Optimizer, and Particle Swarm optimization algorithms, for comparison and testing their performances in the mobile robotics. At first, the simulations based on the proposed exploration algorithms were implemented and evaluated in a created environment. The results were compared in a single and average cases. Then, the real-world experiments using Grey Wolf Optimizer exploration algorithm were conducted in the different types of environments using MATLAB-ROS integration tool. These results proved the effectiveness and applicability of the bio-inspired optimization algorithm in the mobile robotics.

关键词： Robot sensing systems optimization Collision avoidance Uncertainty Mobile robots Navigation Autonomous control exploration nature-inspired optimization algorithm mapping mobile robot system navigation real-experiment simulation uncertainties

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