Optimal Dung Beetle Algorithm for Fused Spiral Feeding DOI
Le Zhang, Yiwen Hu, Hong Yang

et al.

Published: Nov. 17, 2023

In order to improve the dung beetle optimization algorithm is prone fall into problem of local optimal. this paper, Manta ray spiral foraging mechanism was integrated regeneration stage rolling beetles and female breeding beetles, so as ability jump out optimal search speed. Compared with whale gray Wolf algorithm, six benchmark test functions are used improved algorithm. The experiment proves that has better global convergence speed than other comparison algorithms. feasibility practicability in practical application verified.

Language: Английский

Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm DOI
Mojtaba Ghasemi, Mohsen Zare, Pavel Trojovský

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 295, P. 111850 - 111850

Published: April 22, 2024

Language: Английский

Citations

35
An efficient bio-inspired algorithm based on humpback whale migration for constrained engineering optimization DOI Creative Commons
Mojtaba Ghasemi, Mohamed Deriche, Pavel Trojovský

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104215 - 104215

Published: Feb. 1, 2025

Language: Английский

Citations

3
A new firefly algorithm with improved global exploration and convergence with application to engineering optimization DOI Creative Commons
Mojtaba Ghasemi, Soleiman Kadkhoda Mohammadi, Mohsen Zare

et al.

Decision Analytics Journal, Journal Year: 2022, Volume and Issue: 5, P. 100125 - 100125

Published: Sept. 6, 2022

Firefly algorithm (FA) is a powerful and efficient meta-heuristic which has shown effective performance in the recent literature when applied to solving engineering optimization problems. FA imitates flashing behavior of fireflies. generates solutions randomly assumes them as However, these algorithms may suffer from premature convergence poor global exploration used optimize complex high dimension Therefore, this study proposed novel FA, called firefly 1 3 (FA1→3), via different types movements fireflies an attempt improve characteristics FA. A comprehensive been carried out on CEC2014 test functions compare FA1→3 with standard several modern improved validate its performance. The experimental results demonstrate that achieved acceptable In addition, it six real-world problems show capability, robustness, efficacy comparison As per simulations, provided suitable higher accuracy than traditional modified introduced last years. According significantly robust dealing various finds design variables straightforwardly. Note source code publicly available at https://www.optim-app.com/projects/FA.

Language: Английский

Citations

47
Improved salp swarm algorithm based on Newton interpolation and cosine opposition-based learning for feature selection DOI
Hongbo Zhang,

Xiwen Qin,

Xueliang Gao

et al.

Mathematics and Computers in Simulation, Journal Year: 2024, Volume and Issue: 219, P. 544 - 558

Published: Jan. 2, 2024

Language: Английский

Citations

13
An Efficient Multi-Objective White Shark Algorithm DOI Creative Commons
Wenyan Guo,

Yufan Qiang,

Fang Dai

et al.

Biomimetics, Journal Year: 2025, Volume and Issue: 10(2), P. 112 - 112

Published: Feb. 13, 2025

To balance the diversity and stringency of Pareto solutions in multi-objective optimization, this paper introduces a White Shark Optimization algorithm (MONSWSO) tailored for optimization. MONSWSO integrates non-dominated sorting crowding distance into framework to select optimal solution within population. The uniformity initial population is enhanced through chaotic reverse initialization learning strategy. adaptive updating individual positions facilitated by an elite-guided forgetting mechanism, which incorporates escape energy eddy aggregation behavior inspired marine organisms improve exploration key areas. evaluate effectiveness MONSWSO, it benchmarked against five state-of-the-art algorithms using four metrics: inverse generation distance, spatial homogeneity, distribution, hypervolume on 27 typical problems, including 23 functions 4 project examples. Furthermore, practical application demonstrated example optimizing design subway tunnel foundation pits. comprehensive results reveal that outperforms comparison algorithms, achieving impressive satisfactory outcomes.

Language: Английский

Citations

1
An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems DOI Open Access
Saleh Masoud Abdallah Altbawi, S.N. Khalid, Ahmad Safawi Mokhtar

et al.

Processes, Journal Year: 2023, Volume and Issue: 11(2), P. 498 - 498

Published: Feb. 7, 2023

In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving performance and accuracy algorithm for solving complex optimization engineering problems. The IGBO has added features adjusting best solution by adding inertia weight, fast convergence rate modified parameters, as well avoiding local optima using a novel functional operator (G). These make it feasible majority nonlinear problems which quite hard to achieve original version GBO. effectiveness scalability are evaluated well-known benchmark functions. Moreover, statistically analyzed ANOVA analysis, Holm–Bonferroni test. addition, was assessed real-world results functions show that very competitive, superior compared its competitors in finding optimal solutions high coverage. studied real prove superiority difficult indefinite search domains.

Language: Английский

Citations

16
Application of spiral enhanced whale optimization algorithm in solving optimization problems DOI Creative Commons

S. Q. Qu,

Huan Liu,

Yinghang Xu

et al.

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

Published: Oct. 19, 2024

The Whale Optimization Algorithm (WOA) is regarded as a classic metaheuristic algorithm, yet it suffers from limited population diversity, imbalance between exploitation and exploration, low solution accuracy. In this paper, we propose the Spiral-Enhanced (SEWOA), which incorporates nonlinear time-varying self-adaptive perturbation strategy an Archimedean spiral structure into original WOA. enhances diversity of space, aiding algorithm in escaping local optima. optimization dynamic improves algorithm's search capability effectiveness proposed validated multiple perspectives using CEC2014 test functions, CEC2017 23 benchmark functions. experimental results demonstrate that enhanced significantly balances global search, Additionally, SEWOA exhibits excellent performance solving three engineering design problems, showcasing its value wide range potential applications.

Language: Английский

Citations

5
Research on the hybrid chaos-coud salp swarm algorithm DOI

Junfeng Dai,

Lihui Fu

Communications in Nonlinear Science and Numerical Simulation, Journal Year: 2024, Volume and Issue: 138, P. 108187 - 108187

Published: Nov. 1, 2024

Language: Английский

Citations

4
Fine-Tuned Cardiovascular Risk Assessment: Locally Weighted Salp Swarm Algorithm in Global Optimization DOI Creative Commons

Shahad Ibrahim Mohammed,

Nazar K. Hussein, Outman Haddani

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(2), P. 243 - 243

Published: Jan. 11, 2024

The Salp Swarm Algorithm (SSA) is a bio-inspired metaheuristic optimization technique that mimics the collective behavior of chains hunting for food in ocean. While it demonstrates competitive performance on benchmark problems, SSA faces challenges with slow convergence and getting trapped local optima like many population-based algorithms. To address these limitations, this study proposes locally weighted (LWSSA), which combines two mechanisms into standard framework. First, approach introduced integrated to guide search toward promising regions. This heuristic iteratively probes high-quality solutions neighborhood refines current position. Second, mutation operator generates new positions followers increase randomness throughout search. In order assess its effectiveness, proposed was evaluated against state-of-the-art metaheuristics using test functions from IEEE CEC 2021 2017 competitions. methodology also applied risk assessment cardiovascular disease (CVD). Seven strategies extreme gradient boosting (XGBoost) classifier are compared LWSSA-XGBoost model. achieves superior prediction 94% F1 score, recall, 93% accuracy, area under ROC curve comparison competitors. Overall, experimental results demonstrate LWSSA enhances SSA’s ability XGBoost predictive power automated CVD assessment.

Language: Английский

Citations

1
A hybrid Improved Salp Swarm Algorithm and Harris Hawk Optimizer for energy planning in microgrids with minimum operating cost DOI

Naoual Seddaoui,

Sabri Boulouma,

Lazhar Rahmani

et al.

International Journal of Green Energy, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 18

Published: Oct. 6, 2024

Achieving optimal energy planning in Microgrids (MGs) is pivotal for addressing complex challenges associated with cost-effective and reliable supplies. This paper proposes a novel hybrid metaheuristic algorithm microgrids using an Improved Salp Swarm Algorithm Harris Hawk Foraging (ISSAHF). technique based on improved multi-leader elite leader following strategy combined Hawks foraging. A simulation study conducted low-voltage microgrid off-grid grid-connected modes. The optimization resulted daily average cost of 28.3370€ mode compared to 19.2676€ one. Furthermore, the statistical shows that proposed outperforms well-established techniques regarding search capability robustness. It yields mean 623.5248€ 404.7475€ one, other vary from 667.2141€ 959.5747€ mode, 424.5841€ 813.932€ mode. For robustness, performs well standard deviation 20.765€ best (17.024€) worst (47.2423€) cases while it 28.8771€ (21.6316€) (45.3774€) values.

Language: Английский

Citations

0