An enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based learning DOI Creative Commons
Yufei Wang, Yujun Zhang,

Yuxin Yan

и другие.

Mathematical Biosciences & Engineering, Год журнала: 2023, Номер 20(4), С. 6422 - 6467

Опубликована: Янв. 1, 2023

The aquila optimization algorithm (AO) is an efficient swarm intelligence proposed recently. However, considering that AO has better performance and slower late convergence speed in the process. For solving this effect of improving its performance, paper proposes enhanced with a velocity-aided global search mechanism adaptive opposition-based learning (VAIAO) which based on simplified Aquila (IAO). In VAIAO, velocity acceleration terms are set included update formula. Furthermore, strategy introduced to improve local optima. To verify 27 classical benchmark functions, Wilcoxon statistical sign-rank experiment, Friedman test five engineering problems tested. results experiment show VAIAO than AO, IAO other comparison algorithms. This also means introduction these two strategies enhances exploration ability algorithm.

Язык: Английский

Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization DOI
Fatma A. Hashim, Reham R. Mostafa, Abdelazim G. Hussien

и другие.

Knowledge-Based Systems, Год журнала: 2022, Номер 260, С. 110146 - 110146

Опубликована: Ноя. 29, 2022

Язык: Английский

Процитировано

178
Improved bald eagle search algorithm for global optimization and feature selection DOI Creative Commons
Amit Chhabra, Abdelazim G. Hussien, Fatma A. Hashim

и другие.

Alexandria Engineering Journal, Год журнала: 2023, Номер 68, С. 141 - 180

Опубликована: Янв. 18, 2023

The use of metaheuristics is one the most encouraging methodologies for taking care real-life problems. Bald eagle search (BES) algorithm latest swarm-intelligence metaheuristic inspired by intelligent hunting behavior bald eagles. In recent research works, BES has performed reasonably well over a wide range application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency tendency to stuck in local optima which affects final outcome. This paper introduces modified (mBES) that removes shortcomings original incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), Transition & Pharsor operators. OBL embedded different phases standard viz. initial population, selecting, space, swooping update positions individual solutions strengthen exploration, CLS used enhance position best agent will lead enhancing all individuals, operators help provide sufficient exploration–exploitation trade-off. mBES initially evaluated with 29 CEC2017 10 CEC2020 optimization benchmark functions. addition, practicality tested real-world feature selection problem five design Results are compared against number classical algorithms using statistical metrics, convergence analysis, box plots, Wilcoxon rank sum test. case composite test functions F21-F30, wins 70% cases, whereas rest functions, generates good results 65% cases. proposed produces performance 55% 45% generated competitive results. On other hand, problems, among algorithms. problem, also showed competitiveness observations problems show superiority robustness baseline metaheuristics. It can be safely concluded improvements suggested proved effective making enough solve variety

Язык: Английский

Процитировано

67
Partial reinforcement optimizer: An evolutionary optimization algorithm DOI
Ahmad Taheri, Keyvan RahimiZadeh, Amin Beheshti

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122070 - 122070

Опубликована: Окт. 19, 2023

Язык: Английский

Процитировано

55
A review of image fusion: Methods, applications and performance metrics DOI
Simrandeep Singh, Harbinder Singh, Gloria Bueno

и другие.

Digital Signal Processing, Год журнала: 2023, Номер 137, С. 104020 - 104020

Опубликована: Март 24, 2023

Язык: Английский

Процитировано

44
A Comprehensive Survey on Arithmetic Optimization Algorithm DOI Open Access
Krishna Gopal Dhal, Buddhadev Sasmal, Arunita Das

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(5), С. 3379 - 3404

Опубликована: Март 15, 2023

Язык: Английский

Процитировано

31
A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments DOI

Somayeh Yeganeh,

Amin Babazadeh Sangar, Sadoon Azizi

и другие.

Journal of Network and Computer Applications, Год журнала: 2023, Номер 214, С. 103617 - 103617

Опубликована: Март 2, 2023

Язык: Английский

Процитировано

26
Efficient Contrast Adjustment and Fusion Method for Underexposed Images in Industrial Cyber-Physical Systems DOI
Zia-ur Rahman, Muhammad Aamir, Zafar Ali

и другие.

IEEE Systems Journal, Год журнала: 2023, Номер 17(4), С. 5085 - 5096

Опубликована: Апрель 24, 2023

Owing to imaging equipment's environment and limitations, the images obtained in industrial cyber-physical systems (ICPSs) are degraded available various visual appearances. The process of highlighting hidden contents night-time contrast-distorted is complex. Earlier approaches have solved this problem from a different perspective achieved remarkable results that generally unsatisfactory for with diverse illumination distortions ICPSs. Hence, an effective visibility enhancement model proposed eliminate inconsistent color casts while more content improved inspection, safety large spaces, monitoring systems. Our has four steps: 1) removal unnatural cast via white balance technique, 2) use probability density softplus functions actual cast, 3) using optimization algorithm estimate adjusting it nonlinear function, 4) blending by multiscale fusion obtain most result. Evaluation ten benchmark datasets 14 quality metrics 22 conventional modern algorithms shows our approach robust, flexible, applicable numerous vision-based applications, such as ICPSs, autonomous vehicles, smart cameras, mobility, transportation, especially low-light environments.

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
Enhanced Aquila optimizer algorithm for global optimization and constrained engineering problems DOI Creative Commons

YU Huang-jing,

Heming Jia,

Jianping Zhou

и другие.

Mathematical Biosciences & Engineering, Год журнала: 2022, Номер 19(12), С. 14173 - 14211

Опубликована: Янв. 1, 2022

<abstract><p>The Aquila optimizer (AO) is a recently developed swarm algorithm that simulates the hunting behavior of birds. In complex optimization problems, an AO may have slow convergence or fall in sub-optimal regions, especially high ones. This paper tries to overcome these problems by using three different strategies: restart strategy, opposition-based learning and chaotic local search. The named as mAO was tested 29 CEC 2017 functions five engineering constrained problems. results prove superiority efficiency solving many issues.</p></abstract>

Язык: Английский

Процитировано

35
A multi-strategy enhanced African vultures optimization algorithm for global optimization problems DOI Creative Commons
Rong Zheng, Abdelazim G. Hussien, Raneem Qaddoura

и другие.

Journal of Computational Design and Engineering, Год журнала: 2022, Номер 10(1), С. 329 - 356

Опубликована: Дек. 14, 2022

Abstract The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the vultures’ behaviors. Though basic AVOA performs very well for most problems, it still suffers from shortcomings of slow convergence rate and local optimal stagnation when solving complex tasks. Therefore, this study introduces modified version named enhanced (EAVOA). EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight selecting accumulation mechanism, respectively, which are developed based on AVOA. strategy strikes good balance between global searches. mechanism utilized to improve quality solution. performance validated 23 classical benchmark functions with various types dimensions compared those nine other state-of-the-art methods according numerical results curves. In addition, real-world engineering design problems adopted evaluate practical applicability EAVOA. Furthermore, has been applied classify multi-layer perception using XOR cancer datasets. experimental clearly show that superiority over methods.

Язык: Английский

Процитировано

31