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Recent Advances of Chimp optimization Algorithm:Variants and Applications

Journal of Bionic Engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence nature-inspired optimization algorithms Chimp optimization algorithm optimization problems

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Flood algorithm (FLA): an efficient inspired meta-heuristic for engineering optimization

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JOURNAL OF SUPERCOMPUTING 2024年第15期80卷 22913-23017页

作者： Ghasemi, Mojtaba Golalipour, Keyvan Zare, Mohsen Mirjalili, Seyedali Trojovsky, Pavel Abualigah, Laith Hemmati, Rasul Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran Jahrom Univ Fac Engn Dept Elect Engn Jahrom Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia VSB TU Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove Czech Republic Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan Marquette Univ Dept Elect & Comp Engn Milwaukee WI 53210 USA

Introducing a novel meta-heuristic optimization algorithm, the Flood Algorithm (FLA) draws inspiration from the intricate movement and flow patterns of water masses during flooding events in river basins. FLA mathematically models key phenomena such as the movement of water toward slopes, flow rates over time, soil permeability effects, and periodic increases and decreases in water levels from precipitation and losses. Leveraging these models, the algorithm guides the movement and evolution of a population of potential solutions toward enhanced optimality. The algorithm endeavors to establish an appropriate correlation between the fundamental aspects of natural flood events and the optimization process. Its formulation and working mechanism are described in detail. It operates in two main phases-a regular movement phase, where the population moves naturally toward current best solutions, and a flooding phase, which introduces random disturbances to increase diversity. New solutions are periodically introduced while weaker ones are removed, mirroring the natural cycles of water levels. FLA's effectiveness is demonstrated through its application on well-known benchmark optimization problems and engineering design problems. Extensive comparisons have been carried out on CEC2005 functions using 16 algorithms in both basic and enhanced modes, as well as on CEC2014 functions with dimensions 30, 50, and 100 using a total of 20 other algorithms. These rigorous studies unequivocally confirm the robustness and strength of the proposed algorithm. Furthermore, the algorithm's performance on 12 constrained engineering problems demonstrates its ability to tackle real-world challenges. The FLA's source code is publicly available at https://***/projects/fla.

关键词： Engineering optimization Modern algorithms nature-inspired optimization algorithms Flood Algorithm (FLA)

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A penalty-based algorithm proposal for engineering optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第10期35卷 7635-7658页

作者： Oztas, Gulin Zeynep Erdem, Sabri Pamukkale Univ Dept Business Adm TR-20160 Denizli Turkey Dokuz Eylul Univ Dept Business TR-35390 Izmir Turkey

This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature.

关键词： Natural facts Evolutionary computation Constrained nonlinear optimization Engineering benchmark problems Metaheuristics nature-inspired optimization algorithms

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A Novel Social Insect optimization Algorithm for the Optimal Design of an Interior Permanent Magnet Synchronous Machine

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IEEE TRANSACTIONS ON MAGNETICS 2018年第12期54卷 1-6页

作者： Kwon, Hyuk-Sung Ro, Jong-Suk Jung, Hyun-Kyo Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea

optimization of an electric machine is a nonlinear multi-variable problem. For optimization of the nonlinear multi-variable problem, many function evaluations are required, which in turn requires much time. To address this problem, we propose a novel optimization algorithm of which the convergence speed, accuracy, and reliability are superior compared to those of widely used conventional algorithms. The performance of the proposed algorithm is verified through mathematical test functions and applied to a practical optimization scenario of cogging torque minimization for an interior permanent magnet synchronous machine.

关键词： Electric machine interior permanent magnet synchronous machine (IPMSM) multivariable problem nature-inspired optimization algorithms optimization algorithm

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The Genetic Algorithm-Artificial Neural Networks Integration in the optimization: An Application for Transportation Systems 1

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Caparoglu, Omer Faruk Ok, Yesim Ozaydin, Nadide Caglayan Erzurum Tech Univ Dept Ind Engn Erzurum Turkiye Ataturk Univ Dept Ind Engn Erzurum Turkiye

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

The transportation sector stands as one of the broadest and most significant industries worldwide. Today, one of the key environmental consequences of the transportation sector is the increase in carbon dioxide (CO2) emissions. This research focuses on CO2 emissions specifically from road transportation. The primary objectives of the study are to investigate how yearly fluctuations on highways impact CO2 emissions. The study utilizes genetic algorithm-based artificial neural networks (GA-ANN) to design a transportation optimization model. Artificial neural networks are employed to estimate the proportion of CO2 emissions from roadways in the total transportation-related CO2 emissions. The article proposes an optimization method using genetic algorithms (GA) to optimize the parameters of the artificial neural networks. The development of the ANN model was carried out in the MATLAB program. The constructed GA-ANN model was compared to a variety of other machine learning (ML) models linear regression (LR), support vector machine (SVM), random forest (RF), artificial neural network (ANN).

关键词： Artificial Neural Network Genetic Algorithm Transportation nature-inspired optimization algorithms optimization Machine Learning Greenhouse Gas Emission

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Optimal Design of Deformation Monitoring Networks Using the Global optimization Methods 1

Optimal Design of Deformation Monitoring Networks Using the ...

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1st IAG International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS)

作者： Yetkin, M. Inal, C. Izmir Katip Celebi Univ Dept Geomat Engn TR-35620 Izmir Turkey Selcuk Univ Dept Geomat Engn TR-42250 Konya Turkey

ISBN: (纸本)9783319108285;9783319108278

Geodetic networks are very important tools that can be used to monitor crustal movements or the deformation of structures. However, a geodetic network must be designed to sufficiently meet some network quality requirements such as accuracy, reliability, sensitivity and economy. This is the subject of geodetic network optimization. Traditional methods have been used for solving geodetic optimization problems. On the other hand, some evolutionary algorithms such as the particle swarm optimization algorithm have been started to be recently used. These methods are inspired by optimization and adaptation processes that are encountered in the nature. They are iterative procedures for quickly and efficiently solving complex optimization problems. They may provide global optimum solution or at least near-optimum solutions to problems. In this paper, the use of the shuffled frog-leaping algorithm for the optimal design of a deformation monitoring network is studied. The aim is to design and optimize a geodetic network in terms of high reliability.

关键词： First-order design Geodetic network optimization nature-inspired optimization algorithms Network reliability Stochastic search methods

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Efficient leader exchange for Migrating Birds optimization when solving Machine-Part Cell Formation Problems 11

Efficient leader exchange for Migrating Birds Optimization w...

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11th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Soto, Ricardo Crawford, Broderick Almonacid, Boris Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Univ San Sebastian Santiago Chile Univ Cent Chile Santiago Chile

ISBN: (纸本)9789899843462

The machine-part cell formation (MPCF) problem is to organize an assembly as a set of cells, where each cell contains certain machines that process a sub-set of parts. In recent years, different types of metaheuristics have been used to solve the problem of MPCF. This publication focuses on solving the MPCF problem using a metaheuristic inspired on birds, called Migrating Birds optimization (MBO) algorithm. Experiments have been conducted to 180 test instances using 2 types of leader exchange in the flock of birds. The results obtained using MBO are equal to or better than other algorithms reported in the literature.

关键词： Cell formation problem nature-inspired optimization algorithms Migrating Birds optimization Metaheuristics

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An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

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COMPUTERS IN INDUSTRY 2015年 74卷 75-94页

作者： Precup, Radu-Emil Angelov, Plamen Jales Costa, Bruno Sielly Sayed-Mouchaweh, Moamar Politehn Univ Timisoara Dept Automat & Appl Informat Timisoara 300223 Romania Univ Lancaster Data Sci Grp Sch Comp & Commun Lancaster LA1 4WA England Univ Carlos III Madrid Chair Excellence Madrid Spain Fed Inst Rio Grande Norte IFRN Natal RN Brazil Ecole Mines Douai F-59500 Douai France

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Data-driven control Data mining Evolving soft computing techniques Fault diagnosis nature-inspired optimization algorithms Wind turbines

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Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2023年第4期30卷 2543-2578页

作者： Dhal, Krishna Gopal Ray, Swarnajit Rai, Rebika Das, Arunita Midnapore Coll Autonomous Dept Comp Sci & Applicat Midnapore W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata W Bengal India Sikkim Univ Dept Comp Applicat Gangtok Sikkim India

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics inspired optimization algorithms (PIOA), the Archimedes optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.

关键词： nature-inspired optimization algorithms Image segmentation optimization Tsallis t-entropy Archimedes optimization algorithm Physics inspired optimization algorithms

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Archimedes optimizer-based fast and robust fuzzy clustering for noisy image segmentation

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JOURNAL OF SUPERCOMPUTING 2023年第4期79卷 3691-3730页

作者： Dhal, Krishna Gopal Das, Arunita Ray, Swarnajit Rai, Rebika Ghosh, Tarun Kumar Midnapore Coll Autonomous Dept Comp Sci & Applicat Paschimmedinipur W Bengal India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata W Bengal India Sikkim Univ Dept Comp Applicat Sikkim India Haldia Inst Technol Dept Comp Sci & Engn Kolkata W Bengal India

Fuzzy C-means (FCM) is one of the prominent and effective cluster-based image segmentation techniques exceedingly susceptible to noise and initial cluster centers, thereby effortlessly converging toward local optima. However, FCM consumes exceptionally high computation time due to the repetitive computation of the distance amid cluster centers and pixels. To resolve this apprehension, this paper aims to consider a histogram-based fast fuzzy image clustering (HBFFIC) procedure that primarily tends to carry out morphological reconstruction (MR) operation over the image to assure noise immunity and safeguard details of the imagery. Further, as a replacement for pixels of a summed image, clustering is carried out based on gray-level histogram. This with no qualm radically trims down the computational time as the number of gray levels in an image is normally to a great extent lesser than that of the number of its pixels. Though HBFFIC is a proficient local optimizer however, owing to arbitrary initialization that is carried out in FCM, HBFFIC has the utmost possibility to get effortlessly wedge into local optima. Consequently, this is where the role of nature-inspired optimization algorithms (NIOA) comes into picture. For that reason, this paper successfully makes use of NIOA to prevail over the dilemma using Archimedes optimizer (AO) to discover the most favorable cluster centers. The real-world images particularly synthetic, grayscale, and color pathology images are exercised to perform the experimental study. The experimental study clearly highlights that the proposed hybrid algorithm (HBFFIC-AO) for noisy image segmentation outperforms the other state-of-art algorithms in terms of segmentation accuracy (SA), comparison score (CS), MSE, and PSNR. The visual along with numerical outcomes projected in the experimental study point toward the pre-eminence of the proposed algorithm as compared with the prevailing leading-edge image segmentation algorithms.

关键词： Fuzzy C-means Archimedes optimizer Noisy image segmentation nature-inspired optimization algorithms Swarm intelligence Histogram-based fast fuzzy image clustering Fuzzy membership Membership filtering Gray-level histogram Morphological reconstruction Image segmentation algorithms

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