PSO-Incorporated Hybrid Artificial Hummingbird Algorithm with Elite Opposition-Based Learning and Cauchy Mutation: A Case Study of Shape Optimization for CSGC–Ball Curves DOI Creative Commons
Kang Chen, Liuxin Chen, Gang Hu

и другие.

Biomimetics, Год журнала: 2023, Номер 8(4), С. 377 - 377

Опубликована: Авг. 18, 2023

With the rapid development of geometric modeling industry and computer technology, design shape optimization complex curve shapes have now become a very important research topic in CAGD. In this paper, Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize composite shape-adjustable generalized cubic Ball (CSGC–Ball, for short) curves. Firstly, algorithm (AHA), as newly proposed meta-heuristic algorithm, has advantages simple structure easy implementation can quickly find global optimal solution. However, there are still limitations, such low convergence accuracy tendency fall into local optimization. Therefore, paper proposes HAHA based on original AHA, combined with elite opposition-based learning strategy, PSO, Cauchy mutation, increase population diversity avoid falling optimization, thus improve rate AHA. Twenty-five benchmark test functions CEC 2022 suite evaluate overall performance HAHA, experimental results statistically analyzed using Friedman Wilkerson rank sum tests. The show that, compared other advanced algorithms, good competitiveness practicality. Secondly, order better realize curves engineering, CSGC–Ball parameters constructed SGC–Ball basis functions. By changing parameters, whole or be adjusted more flexibly. Finally, make ideal shape, curve-shape model established minimum energy value, solve model. Two representative numerical examples comprehensively verify effectiveness superiority solving problems.

Язык: Английский

Advances in Manta Ray Foraging Optimization: A Comprehensive Survey DOI
Farhad Soleimanian Gharehchopogh,

Shafi Ghafouri,

Mohammad Hasan Namazi

и другие.

Journal of Bionic Engineering, Год журнала: 2024, Номер 21(2), С. 953 - 990

Опубликована: Фев. 27, 2024

Язык: Английский

Процитировано

58
Pied kingfisher optimizer: a new bio-inspired algorithm for solving numerical optimization and industrial engineering problems DOI
Anas Bouaouda, Fatma A. Hashim, Yassine Sayouti

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Май 16, 2024

Язык: Английский

Процитировано

33
LCAHA: A hybrid artificial hummingbird algorithm with multi-strategy for engineering applications DOI
Gang Hu, Jingyu Zhong,

Congyao Zhao

и другие.

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 415, С. 116238 - 116238

Опубликована: Июль 23, 2023

Язык: Английский

Процитировано

25
Artificial Ecosystem-Based Optimization with Dwarf Mongoose Optimization for Feature Selection and Global Optimization Problems DOI Creative Commons
Ibrahim Al-Shourbaji, Pramod Kachare, Sajid Fadlelseed

и другие.

International Journal of Computational Intelligence Systems, Год журнала: 2023, Номер 16(1)

Опубликована: Июнь 16, 2023

Abstract Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because their strong capabilities picking the optimal features and removing redundant irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability exploration stage poor exploitation its stochastic nature. Dwarf Mongoose Algorithm (DMOA) is recent MH algorithm showing high capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO DMOA to develop an efficient FS with better equilibrium between exploitation. The performance investigated on seven datasets from different domains collection twenty-eight global optimization functions, eighteen CEC2017, ten CEC2019 benchmark functions. Comparative study statistical analysis demonstrate that gives competitive results statistically significant compared other popular approaches. function also indicate enhanced high-dimensional search space.

Язык: Английский

Процитировано

24
An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation DOI
Reham R. Mostafa, Essam H. Houssein, Abdelazim G. Hussien

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(15), С. 8775 - 8823

Опубликована: Март 5, 2024

Язык: Английский

Процитировано

14
Artificial Satellite Search: A New Metaheuristic Algorithm for Optimizing Truss Structure Design and Project Scheduling DOI
Min‐Yuan Cheng, Moh Nur Sholeh

Applied Mathematical Modelling, Год журнала: 2025, Номер unknown, С. 116008 - 116008

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy DOI Creative Commons
Xing Wang, Qian Liu, Li Zhang

и другие.

Biomimetics, Год журнала: 2023, Номер 8(2), С. 191 - 191

Опубликована: Май 4, 2023

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic derived from the distant sense of hearing sand cats, which shows excellent performance in some large-scale problems. However, SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, tendency to be trapped topical optimum. To escape these demerits, an adaptive based on Cauchy mutation optimal neighborhood disturbance strategy (COSCSO) are provided this study. First foremost, introduction nonlinear parameter favor scaling up global search helps retrieve optimum colossal space, preventing it being caught Secondly, operator perturbs step, accelerating speed improving efficiency. Finally, diversifies population, broadens enhances exploitation. reveal COSCSO, was compared with alternative algorithms CEC2017 CEC2020 competition suites. Furthermore, COSCSO is further deployed solve six engineering The experimental results that strongly competitive capable practical

Язык: Английский

Процитировано

23
A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization DOI Creative Commons
Zhendong Wang, Lili Huang, Shuxin Yang

и другие.

Alexandria Engineering Journal, Год журнала: 2023, Номер 81, С. 469 - 488

Опубликована: Сен. 22, 2023

There are many tricky optimization problems in real life, and metaheuristic algorithms the most effective way to solve at a lower cost. The dung beetle algorithm (DBO) is more innovative proposed 2022, which affected by action of beetles such as ball rolling, foraging, reproduction. Therefore, A based on quasi-oppositional learning Q-learning (QOLDBO). First, quantum state update idea cleverly integrated into increase randomness generated population. And best behavior pattern selected adding rolling stage improve search effect. In addition, variable spiral local domain method make up for shortage developing only around neighborhood optimum. For optimal solution each iteration, dimensional adaptive Gaussian variation retained. Experimental performance tests show that QOLDBO performs well both benchmark test functions CEC 2017. Simultaneously, validity verified several classical practical application engineering problems.

Язык: Английский

Процитировано

23
Novel hybrid of AOA-BSA with double adaptive and random spare for global optimization and engineering problems DOI Creative Commons
Fatma A. Hashim, Ruba Abu Khurma, Dheeb Albashish

и другие.

Alexandria Engineering Journal, Год журнала: 2023, Номер 73, С. 543 - 577

Опубликована: Май 11, 2023

Archimedes Optimization Algorithm (AOA) is a new physics-based optimizer that simulates principles. AOA has been used in variety of real-world applications because potential properties such as limited number control parameters, adaptability, and changing the set solutions to prevent being trapped local optima. Despite wide acceptance AOA, it some drawbacks, assumption individuals modify their locations depending on altered densities, volumes, accelerations. This causes various shortcomings stagnation into optimal regions, low diversity population, weakness exploitation phase, slow convergence curve. Thus, specific region conventional may be examined achieve balance between exploration capabilities AOA. The bird Swarm (BSA) an efficient strategy strong ability search process. In this study, hybrid called AOA-BSA proposed overcome limitations by replacing its phase with BSA one. Moreover, transition operator have high exploitation. To test examine performance, first experimental series, 29 unconstrained functions from CEC2017 whereas series second experiments use seven constrained engineering problems AOA-BSA's handling issues. performance suggested algorithm compared 10 optimizers. These are original algorithms 8 other algorithms. experiment's results show effectiveness optimizing suite. AOABSA outperforms metaheuristic across 16 functions. statically validated using Wilcoxon Rank sum. shows superior capability. due added power integration not only seen faster achieved AOABSA, but also found For further validation extensive statistical analysis performed during process recording ratios problems, achieves competitive curve reaches lowest values problem. It minimum standard deviation which indicates robustness solving these problems. Also, obtained counterparts regarding problem variables behavior best values.

Язык: Английский

Процитировано

20
Novel memetic of beluga whale optimization with self-adaptive exploration–exploitation balance for global optimization and engineering problems DOI Creative Commons
Abdelazim G. Hussien, Ruba Abu Khurma, Abdullah Alzaqebah

и другие.

Soft Computing, Год журнала: 2023, Номер 27(19), С. 13951 - 13989

Опубликована: Июнь 6, 2023

Abstract A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous deficiencies that has to be strengthened. Premature convergence a disparity between exploitation exploration are some these challenges. Furthermore, the absence transfer parameter in typical when moving from phase direct impact on algorithm’s performance. This work proposes novel modified (mBWO) incorporates an elite evolution strategy, randomization control factor, transition factor exploitation. The strategy preserves top candidates for subsequent generation so helps generate effective solutions with meaningful differences them prevent settling into local maxima. random mutation improves search offers more crucial ability prevents stagnation optimum. mBWO controlling algorithm away optima region during BWO. Gaussian (GM) acts initial position vector produce new location. Because this, majority altered operators scattered close original position, which is comparable carrying out small region. method can now depart optimal zone because this modification, also increases optimizer’s precision traverses space using placements, lead zone. Transition (TF) used make transitions agents gradually concerning amount time required. undergoes comparison 10 additional optimizers 29 CEC2017 functions. Eight engineering problems addressed by mBWO, involving design welded beams, three-bar trusses, tension/compression springs, speed reducers, best industrial refrigeration systems, pressure vessel challenges, cantilever beam designs, multi-product batch plants. In both constrained unconstrained settings, results preformed superior those other methods.

Язык: Английский

Процитировано

20