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Chaotic aquila optimization algorithm for Solving Phase Equilibrium Problems and Parameter Estimation of Semi-empirical Models

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Journal of Bionic Engineering 2024年第1期21卷 486-526页

作者： Oguz Emrah Turgut Mert Sinan Turgut Erhan Kırtepe Department of Industrial Engineering Faculty of Engineering and ArchitectureIzmir Bakircay UniversityMenemenizmirTurkey Department of Mechanical Engineering Faculty of EngineeringEge UniversityBornovaizmirTurkey Department of Motor Vehicles and Transportation Technologies Sirnak UniversitySirnakTurkey

This research study aims to enhance the optimization performance of a newly emerged aquila optimization algorithm by incorporating chaotic sequences rather than using uniformly generated Gaussian random *** work employs 25 different chaotic maps under the framework of aquila *** considers the ten best chaotic variants for performance evaluation on multidimensional test functions composed of unimodal and multimodal problems,which have yet to be studied in past literature *** was found that Ikeda chaotic map enhanced aquila optimization algorithm yields the best predictions and becomes the leading method in most of the *** test the effectivity of this chaotic variant on real-world optimization problems,it is employed on two constrained engineering design problems,and its effectiveness has been ***,phase equilibrium and semi-empirical parameter estimation problems have been solved by the proposed method,and respective solutions have been compared with those obtained from state-of-art *** is observed that CH01 can successfully cope with the restrictive nonlinearities and nonconvexities of parameter estimation and phase equilibrium problems,showing the capabilities of yielding minimum prediction error values of no more than 0.05 compared to the remaining algorithms utilized in the performance benchmarking process.

关键词： aquila optimization algorithm Chaotic maps Parameter estimation Phase equilibrium Unconstrained optimization

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Empowering Retail Through Advanced Consumer Product Recognition Using aquila optimization algorithm With Deep Learning

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IEEE ACCESS 2024年 12卷 71055-71065页

作者： Alghamdi, Mohammed Mengash, Hanan Abdullah Aljebreen, Mohammed Maray, Mohammed Darem, Abdulbasit A. Salama, Ahmed S. Univ Jeddah Coll Comp Sci & Engn Dept Informat Syst & Technol Jeddah 21959 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Community Coll Dept Comp Sci Riyadh 11437 Saudi Arabia King Khalid Univ Coll Comp Sci Dept Informat Syst Abha 61421 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 76321 Saudi Arabia Future Univ Egypt Fac Engn & Technol Dept Elect Engn New Cairo 11845 Egypt

Recently, consumer product recognition comprises harnessing cutting-edge technologies namely artificial intelligence (AI) and computer vision (CV) to develop the purchasing expedition. This technology allows retailers to utilize robust product recognition systems that precisely identify and categorize products in real time. The comprehension of automatic product identification becomes of major importance for both social and economic improvement since it is more reliable and time-consuming than manual function. Product detection through images is a complex task in the domain of CV. This can be obtained the improving consideration because of the excellent application viewpoint like visually impaired assistance, stock tracking, automatic checkout, and planogram compliance. Currently, deep learning (DL) prefers a successful progression with great achievements in object detection and image classification. Therefore, this study presents Advanced Consumer Product Recognition using the aquila optimization algorithm with Deep Learning (ACPR-AOADL) technique. The proposed ACPR-AOADL model utilizes hyperparameter-tuned DL concepts for the identification of consumer products. To achieve this, the ACPR-AOADL model first pre-processes the input data utilizing a Wiener filter (WF) to improve the image quality. Besides, the YOLO-v8 model with a deep residual network (DRN) as a backbone network can be applied for the product detection process. For product classification, the deep belief network (DBN) approach can be used. To boost the complete product detection process, the ACPR-AOADL technique involves AOA based hyperparameter selection process. The performance analysis of the ACPR-AOADL method can be examined under the Product-10K dataset. Wide-ranging results stated that the ACPR-AOADL technique reaches enhanced classification performance over other compared approaches.

关键词： Consumer products Object recognition Deep learning Brain modeling Noise Classification algorithms optimization Product development Wiener filters Computer vision Product recognition aquila optimization algorithm deep learning Wiener filter computer vision

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Chaotic aquila optimization algorithm for solving global optimization and engineering problems

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ALEXANDRIA ENGINEERING JOURNAL 2024年 108卷 135-157页

作者： Gopi, S. Mohapatra, Prabhujit Vellore Inst Technol Sch Adv Sci Dept Math Vellore 632014 Tamil Nadu India

The aquila optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting behavior of aquila birds in nature. However, in complex optimization problems, the AO has shown a slow convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, a hybrid with AO and twelve chaotic maps has been proposed to adjust its main parameter. This new mechanism, namely the Chaotic aquila optimization (CAO) algorithm, is employed with chaotic maps with the AO algorithm. The proposed chaotic AO (CAO) approach takes seriously a variety of chaotic maps while setting the main AO parameter, which helps in managing exploration and exploitation. To validate the performance of the CAO algorithm, estimates for CEC 2005 and CEC 2022 test functions and the first chaotic map results are compared with the AO algorithm to select the best results of the CAO algorithm, and then CAO results are compared with nine popular optimization algorithms such as FFA, AVOA, MGO, AGTO, SSA, GWO, MVO, SCA, TSA, and AO. Moreover, statistical analyses such as the Wilcoxon rank-sum test and the t-test are performed to analyze the significant difference between the proposed CAO and other algorithms. Furthermore, the proposed CAO has been employed to solve six real-world engineering problems. The results demonstrate the CAO's superiority and capability over other algorithms in solving complex optimization problems. The results demonstrate that CAO achieved outstanding performance and effectiveness in solving an extensive variety optimization problems.

关键词： aquila optimization algorithm Chaotic maps Chaotic aquila optimization algorithm Test functions Real-life problems

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Robot Path Planning Based on Interval Type-2 Fuzzy Controller Optimized by an Improved aquila optimization algorithm

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IEEE ACCESS 2023年 11卷 111655-111671页

作者： Li, Kun Zhang, Xiang Han, Ying Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China

Uncertainty and complexity in the local path planning are hot topics. In this paper, a novel IAOFC algorithm is proposed for local path planning in the complex environment. Considering the uncertainty and complexity of local path planning, this paper uses interval type-2 fuzzy control to design path planning method, which can respond more quickly to the uncertainty of the environment and improve the computation speed and efficiency. To further improve the performance of the fuzzy controller, this paper uses an improved aquila Optimizer (AO) algorithm to optimize the membership function of the interval type-2 fuzzy controller (renamed by IAOFC). By using the optimized fuzzy controller, the time cost and path cost can be reduced. In simulation experiments, the path planning in static environment is designed to verify the basic performance and efficiency of the algorithm, and the path planning in dynamic environment is validated to verify the robustness of the algorithm. Finally, the superiority of the IAOFC algorithm is proved by comparing it with some other algorithms. According to experiment results, IAOFC has an average cost reduction of 15% and 6% than other algorithms in static and dynamic environments, respectively.

关键词： Path planning interval type-2 fuzzy control aquila optimization algorithm membership function uncertainty

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Local search enhanced aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 206卷 302-374页

作者： Turgut, Oguz Emrah Turgut, Mert Sinan Izmir Bakircay Univ Dept Ind Engn Izmir Turkey Ege Univ Dept Mech Engn Izmir Turkey

aquila optimization algorithm (aquila) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard aquila to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base aquila to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with aquila has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： aquila optimization algorithm Constrained optimization Local search Mexican-hat wavelet Wavelet mutation operator

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Fractional-order chaotic oscillator-based aquila optimization algorithm for maximization of the chaotic with Lorentz oscillator

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NEURAL COMPUTING & APPLICATIONS 2023年第29期35卷 21645-21662页

作者： Cavlak, Yakup Ates, Abdullah Abualigah, Laith Elaziz, Mohammed Abd Inonu Univ Comp Engn Dept Engn Fac Malatya Turkiye Al Al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Yuan Ze Univ Coll Engn Taoyuan Taiwan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Pulau Pinang Malaysia Zagazig Univ Fac Sci Dept Math Zagazig Egypt Galala Univ Fac Comp Sci & Engn Suez 435611 Egypt Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Lebanese American Univ Dept Elect & Comp Engn 13-5053 Byblos Lebanon

The random structures in the aquila optimization algorithm are modeled with fractional chaotic oscillators, and the fractional-order chaotic oscillator-based aquila optimization (FOCOBAO) algorithm was suggested in this study. First of all, the basic AO algorithm was examined. In particular, random variables that affect the optimization performance of the AO algorithm have been determined. Then, instead of the determined random variables, the coefficients were derived with fractional chaotic oscillators and used in the FOCOBAO. The superiority of the proposed algorithm was primarily demonstrated via twenty-three benchmark functions. The results were matched with GO, EO, GWO, MPA, WOA, SMA and basic AO optimization algorithms. Then, the design of the Lorenz chaotic oscillator, according to maximum chaotic objective function, is a topic that remains up to date in the literature. In this study, a fractional chaotic Lorenz oscillator was designed with FOCOBAO as an engineering application. Especially for maximum chaoticity, maximum positive Lyapunov exponents were determined. In this way, a different design process has been proposed in the literature. The basic AO algorithm, which includes stochastic processes, was developed with fractional chaotic oscillators, and a deterministic method was obtained. The parameters of the Lorenz system were calculated for maximum chaoticity, and the results were presented comparatively.

关键词： Fractional-order chaotic oscillator aquila optimization algorithm Lorenz system

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The heterogeneous aquila optimization algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第6期19卷 5867-5904页

作者： Zhao, Juan Gao, Zheng-Ming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

A new swarm-based optimization algorithm called the aquila optimizer (AO) was just proposed recently with promising better performance. However, as reported by the proposer, it almost remains unchanged for almost half of the convergence curves at the latter iterations. Considering the better performance and the lazy latter convergence rates of the AO algorithm in optimization, the multiple updating principle is introduced and the heterogeneous AO called HAO is proposed in this paper. Simulation experiments were carried out on both unimodal and multimodal benchmark functions, and comparison with other capable algorithms were also made, most of the results confirmed the better performance with better intensification and diversification capabilities, fast convergence rate, low residual errors, strong scalabilities, and convinced verification results. Further application in optimizing three benchmark real-world engineering problems were also carried out, the overall better performance in optimizing was confirmed without any other equations introduced for improvement.

关键词： aquila optimization algorithm meta-heuristic algorithm nature-inspired algorithm multiple updating principle

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The Simplified aquila optimization algorithm

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IEEE ACCESS 2022年 10卷 22487-22515页

作者： Zhao, Juan Gao, Zheng-Ming Chen, Hua-Feng Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

Considering the complicated multimodality, separability, scalability, symmetry characteristics of real-world engineering problems, we are still under the way to find more capable algorithms to solve them. The aquila optimization (AO) algorithm was just proposed recently, however, the original version of this algorithm had some defects in the exploitation procedure, and resulted in incapability to exploit more information. Therefore, a simplified improved AO (IAO) algorithm was proposed and the equation controlling the exploitation procedure was removed, the former two strategies were kept and the latter two were dropped. To confirm the capability of the proposed IAO, simulation experiments on unimodal, multimodal benchmark functions and the CEC21 basic functions were carried out. Simulation experiments on three famous real-world engineering problems were also carried out. Comparison were made with the original AO algorithm, and some other newly proposed nature-inspired algorithms. Results on benchmark functions and the CEC14 basic functions verified the better performance, however, results on the real-world engineering problems demonstrated an incapable conclusion. Detailed future work is needed to tell the reason.

关键词： optimization Mathematical models Benchmark testing Statistics Sociology Convergence Licenses aquila optimization algorithm meta-heuristic algorithm nature-inspired algorithm benchmark function engineering problems

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A Hybrid Mayfly-aquila optimization algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks

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SENSORS 2022年第17期22卷 6405-6405页

作者： Natesan, Gobi Konda, Srinivas Perez de Prado, Rocio Wozniak, Marcin Dr Mahalingam Coll Engn & Technol Dept Comp Sci & Engn Pollachi 642003 Tamil Nadu India CMR Tech Campus Dept Data Sci Hyderabad 501401 Telangana India Univ Jaen Telecommun Engn Dept Linares 23700 Spain Silesian Tech Univ Fac Appl Math PL-44100 Gliwice Poland

In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network's lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.

关键词： aquila optimization algorithm cluster head mayfly routing protocol wireless sensor networks

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EAO: Enhanced aquila optimizer for solving optimization problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第2期46卷 4361-4380页

作者： Guo, Hairu Wang, Jin'ge Liu, Yongli Zhang, Yudong Henan Polytech Univ Dept Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia

The aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm's convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO's viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO's robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design.

关键词： aquila optimization algorithm optimization function kapur entropy threshold optimization pressure vessel design

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