A New Hybrid Improved Arithmetic Optimization Algorithm for Solving Global and Engineering Optimization Problems DOI Creative Commons

Yalong Zhang,

Lining Xing

Mathematics, Journal Year: 2024, Volume and Issue: 12(20), P. 3221 - 3221

Published: Oct. 14, 2024

The Arithmetic Optimization Algorithm (AOA) is a novel metaheuristic inspired by mathematical arithmetic operators. Due to its simple structure and flexible parameter adjustment, the AOA has been applied solve various engineering problems. However, still faces challenges such as poor exploitation ability tendency fall into local optima, especially in complex, high-dimensional In this paper, we propose Hybrid Improved (HIAOA) address issues of susceptibility optima AOAs. First, grey wolf optimization incorporated AOAs, where group hunting behavior GWO allows multiple individuals perform searches at same time, enabling solution be more finely tuned avoiding over-concentration particular region, which can improve capability AOA. Second, end each run, follower mechanism Cauchy mutation operation Sparrow Search are selected with probability perturbed enhance escape from optimum. overall performance improved algorithm assessed selecting 23 benchmark functions using Wilcoxon rank-sum test. results HIAOA compared other intelligent algorithms. Furthermore, also three design problems successfully, demonstrating competitiveness. According experimental results, better test than comparator.

Language: Английский

Metaheuristic optimization algorithms: a comprehensive overview and classification of benchmark test functions DOI
Pankaj Sharma, R. Saravanakumar

Soft Computing, Journal Year: 2023, Volume and Issue: 28(4), P. 3123 - 3186

Published: Oct. 11, 2023

Language: Английский

Citations

53
Quantum particle swarm optimization algorithm based on diversity migration strategy DOI
Chen Gong, Nanrun Zhou,

Xia Shuhua

et al.

Future Generation Computer Systems, Journal Year: 2024, Volume and Issue: 157, P. 445 - 458

Published: April 9, 2024

Language: Английский

Citations

28
Enhanced marine predator algorithm for global optimization and engineering design problems DOI
Salih Berkan Aydemı̇r

Advances in Engineering Software, Journal Year: 2023, Volume and Issue: 184, P. 103517 - 103517

Published: June 28, 2023

Language: Английский

Citations

28
SEB-ChOA: an improved chimp optimization algorithm using spiral exploitation behavior DOI

Leren Qian,

Mohammad Khishe, Yiqian Huang

et al.

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 36(9), P. 4763 - 4786

Published: Dec. 19, 2023

Language: Английский

Citations

28
Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization DOI Creative Commons
Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Opeoluwa Seun Ojekemi

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 26, 2024

The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to reliance on alpha for guiding position updates of search agents, risk being trapped in a local optimal solution is notable. Furthermore, during stagnation, convergence other wolves towards this results lack diversity within population. Hence, research introduces an enhanced version GWO algorithm designed tackle numerical optimization challenges. incorporates innovative approaches such Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), it's called CMWGWO. MRS, particular, empowers certain extend their exploration range, thus enhancing global capability. By employing COL, diversification intensified, leading reduced improved precision, overall boost accuracy. integration WID fosters more information exchange between least most successful wolves, facilitating exit from optima significantly potential. To validate superiority CMWGWO, comprehensive evaluation conducted. A wide array 23 benchmark functions, spanning dimensions 30 500, ten CEC19 three engineering problems are used experimentation. empirical findings vividly demonstrate that CMWGWO surpasses original terms accuracy robust capabilities.

Language: Английский

Citations

9
Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application DOI Creative Commons
Zheng Zhang, Xiangkun Wang, Yinggao Yue

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(10), P. 595 - 595

Published: Oct. 1, 2024

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective issues in recent years. Their study has garnered lot of attention since problems hard high-dimensional goal space. The black-winged kite algorithm still suffers from the imbalance between global search and local development capabilities, it is prone even though combines Cauchy mutation enhance algorithm's ability. heuristic fused with osprey (OCBKA), which initializes population by logistic chaotic mapping fuses improve performance algorithm, proposed means enhancing ability (BKA). By using numerical comparisons CEC2005 CEC2021 benchmark functions, along other swarm solutions three engineering problems, upgraded strategy's efficacy confirmed. Based on experiment findings, revised OCBKA very competitive because can handle complicated high convergence accuracy quick time when compared comparable algorithms.

Language: Английский

Citations

7
A framework for predicting the carbonation depth of concrete incorporating fly ash based on a least squares support vector machine and metaheuristic algorithms DOI
Kai Zhang, Ke Zhang,

Rui Bao

et al.

Journal of Building Engineering, Journal Year: 2022, Volume and Issue: 65, P. 105772 - 105772

Published: Dec. 21, 2022

Language: Английский

Citations

24
An Energy-Saving and Efficient Deployment Strategy for Heterogeneous Wireless Sensor Networks Based on Improved Seagull Optimization Algorithm DOI Creative Commons
Li Cao, Zihui Wang, Zihao Wang

et al.

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 231 - 231

Published: June 2, 2023

The Internet of Things technology provides convenience for data acquisition in environmental monitoring and protection can also avoid invasive damage caused by traditional methods. An adaptive cooperative optimization seagull algorithm optimal coverage heterogeneous sensor networks is proposed order to address the issue blind zone redundancy initial random deployment network nodes sensing layer Things. Calculate individual fitness value according total number nodes, radius, area edge length, select population, aim at maximum rate determine position current solution. After continuous updating, when iterations maximum, global output output. solution node's mobile position. A scaling factor introduced dynamically adjust relative displacement between individual, which improves exploration development ability algorithm. Finally, fine-tuned opposite learning, leading whole move correct given search space, improving jump out local optimum, further increasing accuracy. experimental simulation results demonstrate that, compared with energy consumption PSO algorithm, GWO basic SOA PSO-SOA this paper 6.1%, 4.8%, 1.2% higher than them, respectively, reduced 86.8%, 68.4%, 52.6%, respectively. method based on improve reduce cost, effectively network.

Language: Английский

Citations

15
Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods DOI
Ali Mohammadi, Farid Sheikholeslam, Seyedali Mirjalili

et al.

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 30(1), P. 331 - 389

Published: Aug. 29, 2022

Language: Английский

Citations

20
Hybrid Strategies Based Seagull Optimization Algorithm for Solving Engineering Design Problems DOI Creative Commons

Pingjing Hou,

Jiang Liu, Feng Ni

et al.

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: March 27, 2024

Abstract The seagull optimization algorithm (SOA) is a meta-heuristic proposed in 2019. It has the advantages of structural simplicity, few parameters and easy implementation. However, it also some defects including three main drawbacks slow convergence speed, simple search method poor ability balancing global exploration local exploitation. Besides, most improved SOA algorithms literature have not considered comprehensively enough. This paper proposes hybrid strategies based (ISOA) to overcome SOA. Firstly, hyperbolic tangent function used adjust spiral radius. radius can change dynamically with iteration algorithm, so that converge quickly. Secondly, an adaptive weight factor improves position updating by adjusting proportion best individual balance abilities. Finally, single mode, chaotic strategy introduced for secondary search. A comprehensive comparison between ISOA other related presented, considering twelve test functions four engineering design problems. results indicate outstanding performance significant advantage solving problems, especially average improvement 14.67% welded beam problem.

Language: Английский

Citations

4