Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Год журнала: 2023, Номер 8(8), С. 619 - 619

Опубликована: Дек. 17, 2023

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in wild. The fundamental inspiration design GAO derived from hunting strategy armadillos moving towards prey positions and digging termite mounds. theory expressed mathematically modeled two phases: (i) exploration based on simulating movement mounds, (ii) exploitation armadillos' skills order to rip open performance handling optimization tasks evaluated solve CEC 2017 test suite for problem dimensions equal 10, 30, 50, 100. results show that able achieve effective solutions problems by benefiting its high abilities exploration, exploitation, balancing them during search process. quality obtained compared with twelve well-known algorithms. simulation presents superior competitor algorithms providing better most benchmark functions. statistical analysis Wilcoxon rank sum confirms has significant superiority over implementation 2011 four engineering proposed approach dealing real-world applications.

Язык: Английский

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Addax Optimization Algorithm: A Novel Nature-Inspired Optimizer for Solving Engineering Applications

International journal of intelligent engineering and systems, Год журнала: 2024, Номер 17(3), С. 732 - 743

Опубликована: Май 3, 2024

This paper introduces a novel nature-inspired optimization algorithm called the Addax Optimization Algorithm (AOA), which emulates natural behavior of addax in wild.The core inspiration for AOA is drawn from addax's foraging strategy and digging skills.The theoretical foundation expounded mathematically modeled two phases: (i) exploration based on modeling position change during (ii) exploitation digging.The efficiency handling realworld engineering applications evaluated four design problems.The results show that achieved effective solutions problems with its high ability exploration, exploitation, establishing balance between them search process.The outcomes derived applying are compared performance twelve well-known algorithms.The simulation provided superior to competitor algorithms, by achieving better ranking as first best optimizer.The findings proposed approach has an tasks applications.

Язык: Английский

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Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and Challenges

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер 31(8), С. 4485 - 4519

Опубликована: Авг. 21, 2024

Abstract The greatest and fastest advances in the computing world today require researchers to develop new problem-solving techniques capable of providing an optimal global solution considering a set aspects restrictions. Due superiority metaheuristic Algorithms (MAs) solving different classes problems promising results, MAs need be studied. Numerous studies algorithms fields exist, but this study, comprehensive review MAs, its nature, types, applications, open issues are introduced detail. Specifically, we introduce metaheuristics' advantages over other techniques. To obtain entire view about classifications based on (i.e., inspiration source, number search agents, updating mechanisms followed by agents their positions, primary parameters algorithms) presented detail, along with optimization including both structure types. application area occupies lot research, so most widely used applications presented. Finally, great effort research is directed discuss challenges which help upcoming know future directions active field. Overall, study helps existing understand basic information field addition directing newcomers areas that addressed future.

Язык: Английский

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Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 29, 2024

Supply chain efficiency is a major challenge in today's business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional strategies often struggle resources the complex dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring random search nature emphasizing that no algorithm best optimizer all applications, No Free Lunch (NFL) theorem encourages researchers design newer be able provide more effective solutions Motivated by NFL theorem, innovation novelty this paper designing new meta-heuristic called Bobcat Optimization Algorithm (BOA) imitates natural behavior bobcats wild. The basic inspiration BOA derived from hunting strategy during attack towards prey chase process between them. theory stated then mathematically modeled two phases (i) exploration based on simulation bobcat's position change while moving (ii) exploitation simulating catch prey. performance evaluated handle CEC 2017 test suite problem dimensions equal 10, 30, 50, 100, well address 2020. results show has high ability exploration, exploitation, balance them order achieve suitable solution obtained compared with twelve well-known algorithms. findings been successful handling 89.65, 79.31, 93.10, 89.65% functions dimension respectively. Also, 2020 suite, 100% suite. statistical analysis confirms significant superiority competition analyze dealing real world twenty-two constrained problems 2011 four engineering selected. 90.90% CEC2011 addition, SCM applications challenged ten case studies field sustainable lot size optimization. successfully provided superior competitor

Язык: Английский

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A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 31(3), С. 1749 - 1822

Опубликована: Дек. 11, 2023

Abstract Optimization is a method which used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also our daily activities. In everyone always wants to minimize or maximize something called the objective function. Traditional modern optimization techniques Meta-Heuristic (MH) are solve functions. But traditional fail complex real-world problem consisting of non-linear So many have been proposed exponentially over last few decades overcome these challenges. This paper discusses brief review different benchmark test functions (BTFs) related existing MH algorithms (OA). It classification reported literature regarding swarm-based, human-based, physics-based, evolutionary-based methods. Based on half-century literature, MH-OAs tabulated terms year, author, inspiration agent. Furthermore, this presents MATLAB python code web-link MH-OA. After reading article, readers will be able use MH-OA challenges their field.

Язык: Английский

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Attack-Leave Optimizer: A New Metaheuristic that Focuses on The Guided Search and Performs Random Search as Alternative

International journal of intelligent engineering and systems, Год журнала: 2023, Номер 16(3), С. 244 - 257

Опубликована: Май 1, 2023

This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer (ALO).As the name suggests, ALO deploys two strategies to find optimal solution.The central concept of is intensify guided searches as required method.Then, random search performed only if fails improve current solution.ALO consists four and one search, in three phases: mandatory optional.In first phase, conducted with best global solution reference.In second randomly selected reference.The third phase.Evaluating ALO, it was tested on 23 classic functions benchmarked against five existing metaheuristics known shortcomings: Mixed leader-based (MLBO), slime mould algorithm (SMA), golden (GSO), zebra optimization (ZOA), coati (COA).The results indicate that highly competitive, outperforming MLBO, SMA, GSO, COA, ZOA solving 16, 14, 10, 9 respectively, demonstrating promising metaheuristic.

Язык: Английский

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An optimal energy utilization model for precision agriculture in WSNs using multi-objective clustering and deep learning

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2023, Номер 35(10), С. 101803 - 101803

Опубликована: Окт. 18, 2023

Wireless Sensor Networks (WSNs) play a crucial role in Precision Agriculture by providing real-time data on various environmental parameters like temperature, humidity, soil moisture, etc. However, the efficient utilization of energy sensor nodes WSNs is major challenge that needs to be addressed. To address this issue, new multi-objective clustering approach introduced work for grouping WSNs. Moreover, hybrid optimisation technique called Election based Aquila Optimizer (EAO) which combination (AO) and Election-Based Optimisation Algorithm (EBOA) proposed make sure Cluster Head (CH) selection process identify best CH. In addition, method incorporates newly developed optimization with convolutional neural network (CNN) as an Optimized CNN (O-CNN) improve algorithm's precision also enhance training accuracy testing accuracy. The evaluated through experiments proved better than other approaches obtaining 99.23% classification accuracy, 76.92% throughput, 99% packet delivery ratio, 98.24% lifetime 50% maximum consumption it resolves significant difficulty agriculture.

Язык: Английский

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A multi-hybrid algorithm with shrinking population adaptation for constraint engineering design problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 421, С. 116781 - 116781

Опубликована: Янв. 19, 2024

Язык: Английский

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A hybrid Prairie INFO fission naked algorithm with stagnation mechanism for the parametric estimation of solar photovoltaic systems

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 1, 2025

Abstract This paper presents a study to enhance the performance of recently introduced naked mole-rat algorithm (NMRA), by local optima avoidance, and better exploration as well exploitation properties. A new set algorithms, namely Prairie dog optimization algorithm, INFO, Fission fusion (FuFiO) are included in fundamental framework NMRA operation. The proposed is hybrid based on four algorithms: Dog, Fusion Naked (PIFN) algorithm. Five mutation operators/inertia weights exploited make self-adaptive nature. Apart from that, stagnation phase added for avoidance. tested variable population, dimension size, efficient parameters analysed Friedman Wilcoxon rank-sum tests performed determine effectiveness PIFN On basis comparison outcomes, more effective robust than other techniques evaluated prior researchers address standard benchmark functions (classical benchmarks, CEC 2017, CEC-2019) complex engineering design challenges. Furthermore, reliability demonstrated testing using various PV modules, RTC France Solar Cell (SDM, DDM), Photowatt-PWP201, STM6- 40/36, STP6-120/36 module. results obtained compared with MH algorithms reported existing literature. achieved lowest root-mean-square error value, (SDM) 7.72E−04, (DDM) 7.59E−04, module 1.44E−02, STM6-40/36 1.723E−03, Photowatt-PWP201 2.06E−03, respectively. In order accuracy parameter estimation solar photovoltaic systems, we integrated Newton-Raphson approach Experimental statistical further prove significance respect algorithms.

Язык: Английский

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Optimized attention-enhanced U-Net for autism detection and region localization in MRI

Psychiatry Research Neuroimaging, Год журнала: 2025, Номер 349, С. 111970 - 111970

Опубликована: Март 14, 2025

Язык: Английский

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