Individual disturbance and neighborhood mutation search enhanced whale optimization: performance design for engineering problems DOI

Shimeng Qiao,

Helong Yu, Ali Asghar Heidari

et al.

Journal of Computational Design and Engineering, Journal Year: 2022, Volume and Issue: 9(5), P. 1817 - 1851

Published: Aug. 16, 2022

Abstract The whale optimizer is a popular metaheuristic algorithm, which has the problems of weak global exploration, easy falling into local optimum, and low optimization accuracy when searching for optimal solution. To solve these problems, this paper proposes an enhanced algorithm (WOA) based on worst individual disturbance (WD) neighborhood mutation search (NM), named WDNMWOA, employed WD to enhance ability jump out optimum adopted NM possibility individuals approaching superiority WDNMWOA demonstrated by representative IEEE CEC2014, CEC2017, CEC2019, CEC2020 benchmark functions four engineering examples. experimental results show that thes better convergence strong than original WOA.

Language: Английский

An enhanced whale optimization algorithm for large scale optimization problems DOI
Sanjoy Chakraborty, Apu Kumar Saha, Ratul Chakraborty

et al.

Knowledge-Based Systems, Journal Year: 2021, Volume and Issue: 233, P. 107543 - 107543

Published: Sept. 30, 2021

Language: Английский

Citations

119
mLBOA: A Modified Butterfly Optimization Algorithm with Lagrange Interpolation for Global Optimization DOI

Sushmita Sharma,

Sanjoy Chakraborty, Apu Kumar Saha

et al.

Journal of Bionic Engineering, Journal Year: 2022, Volume and Issue: 19(4), P. 1161 - 1176

Published: Feb. 16, 2022

Language: Английский

Citations

77
Boosting particle swarm optimization by backtracking search algorithm for optimization problems DOI
Sukanta Nama, Apu Kumar Saha, Sanjoy Chakraborty

et al.

Swarm and Evolutionary Computation, Journal Year: 2023, Volume and Issue: 79, P. 101304 - 101304

Published: March 26, 2023

Language: Английский

Citations

67
MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems DOI Creative Commons
Mohammad H. Nadimi-Shahraki, Shokooh Taghian, Hoda Zamani

et al.

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(1), P. e0280006 - e0280006

Published: Jan. 3, 2023

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single strategy and control parameter affect convergence balance between exploration exploitation. Since strategies have considerable impact on performance of algorithms, collaborating multiple can significantly enhance abilities algorithms. This our motivation to propose multi-trial vector-based monkey named MMKE. It introduces novel best-history trial vector producer (BTVP) random (RTVP) that effectively collaborate with canonical MKE (MKE-TVP) using approach tackle various real-world optimization problems diverse challenges. expected proposed MMKE improve global search capability, strike exploitation, prevent original from converging prematurely during process. The was assessed CEC 2018 test functions, results were compared eight metaheuristic As result experiments, it demonstrated capable producing competitive superior terms accuracy rate comparison comparative Additionally, Friedman used examine gained experimental statistically, proving Furthermore, four engineering design optimal power flow (OPF) problem for IEEE 30-bus system are optimized demonstrate MMKE's real applicability. showed handle difficulties associated able solve multi-objective OPF better solutions than

Language: Английский

Citations

42
Gorilla optimization algorithm combining sine cosine and cauchy variations and its engineering applications DOI Creative Commons
Shuxin Wang, Li Cao,

Yaodan Chen

et al.

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

Published: March 30, 2024

Abstract To address the issues of lacking ability, loss population diversity, and tendency to fall into local extreme value in later stage optimization searching, resulting slow convergence lack exploration ability artificial gorilla troops optimizer algorithm (AGTO), this paper proposes a search that integrates positive cosine Cauchy's variance (SCAGTO). Firstly, is initialized using refractive reverse learning mechanism increase species diversity. A strategy nonlinearly decreasing weight factors are introduced finder position update coordinate global algorithm. The follower updated by introducing Cauchy variation perturb optimal solution, thereby improving algorithm's obtain solution. SCAGTO evaluated 30 classical test functions Test Functions 2018 terms speed, accuracy, average absolute error, other indexes, two engineering design problems, namely, pressure vessel problem welded beam problem, for verification. experimental results demonstrate improved significantly enhances speed exhibits good robustness. demonstrates certain solution advantages optimizing verifying superior practicality

Language: Английский

Citations

19
A Hybrid Moth Flame Optimization Algorithm for Global Optimization DOI
Saroj Kumar Sahoo, Apu Kumar Saha

Journal of Bionic Engineering, Journal Year: 2022, Volume and Issue: 19(5), P. 1522 - 1543

Published: July 1, 2022

Language: Английский

Citations

52
Non-dominated Sorting Advanced Butterfly Optimization Algorithm for Multi-objective Problems DOI

Sushmita Sharma,

Nima Khodadadi, Apu Kumar Saha

et al.

Journal of Bionic Engineering, Journal Year: 2022, Volume and Issue: 20(2), P. 819 - 843

Published: Nov. 22, 2022

Language: Английский

Citations

48
Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review DOI Open Access
Rebika Rai, Arunita Das, Krishna Gopal Dhal

et al.

Evolving Systems, Journal Year: 2022, Volume and Issue: 13(6), P. 889 - 945

Published: Feb. 21, 2022

Language: Английский

Citations

47
Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data DOI Creative Commons
Essam H. Houssein,

Mosa E. Hosney,

Waleed M. Mohamed

et al.

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 35(7), P. 5251 - 5275

Published: Nov. 1, 2022

Feature selection (FS) is one of the basic data preprocessing steps in mining and machine learning. It used to reduce feature size increase model generalization. In addition minimizing dimensionality, it also enhances classification accuracy reduces complexity, which are essential several applications. Traditional methods for often fail optimal global solution due large search space. Many hybrid techniques have been proposed depending on merging strategies individually as a FS problem. This study proposes modified hunger games algorithm (mHGS), solving optimization problems. The main advantages mHGS resolve following drawbacks that raised original HGS; (1) avoiding local search, (2) problem premature convergence, (3) balancing between exploitation exploration phases. has evaluated by using IEEE Congress Evolutionary Computation 2020 (CEC'20) test ten medical chemical datasets. dimensions up 20000 features or more. results compared variety well-known methods, including improved multi-operator differential evolution (IMODE), gravitational algorithm, grey wolf optimization, Harris Hawks whale slime mould search. experimental suggest can generate effective without increasing computational cost improving convergence speed. SVM performance.

Language: Английский

Citations

43
IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems DOI Creative Commons
Yaning Xiao, Yanling Guo, Hao Cui

et al.

Mathematical Biosciences & Engineering, Journal Year: 2022, Volume and Issue: 19(11), P. 10963 - 11017

Published: Jan. 1, 2022

<abstract><p>Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising capability but insufficient mechanisms. Based on characteristics both algorithms, this paper, we propose an improved hybrid optimizer called IHAOAVOA to overcome deficiencies single algorithm provide higher-quality solutions for solving optimization problems. First, combined retain valuable search competence each. Then, a new composite opposition-based learning (COBL) designed increase population diversity help escape from optima. In addition, more effectively guide process balance exploitation, fitness-distance (FDB) selection strategy introduced modify core position update formula. The performance proposed comprehensively investigated analyzed by comparing against basic AO, AVOA, six state-of-the-art 23 classical benchmark functions IEEE CEC2019 test suite. Experimental results demonstrate achieves superior solution accuracy, convergence speed, optima avoidance than comparison methods most functions. Furthermore, practicality highlighted five engineering design Our findings reveal technique also highly competitive when addressing real-world tasks. source code publicly available at <a href="https://doi.org/10.24433/CO.2373662.v1" target="_blank">https://doi.org/10.24433/CO.2373662.v1</a>.</p></abstract>

Language: Английский

Citations

41