AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems DOI Creative Commons

Guoping You,

Zhong Lu, Zhipeng Qiu

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(12), P. 727 - 727

Published: Nov. 28, 2024

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy (AMBWO). The adaptive population learning strategy proposed improve global BWO. introduction roulette equilibrium selection allows have more reference points choose among during exploitation phase, which enhances flexibility algorithm. In addition, avoidance improves algorithm’s ability escape optima enriches quality. order validate performance AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on CEC2017 CEC2022 test sets. Statistical tests, convergence analysis, stability analysis show that AMBWO exhibits superior overall performance. Finally, applicability superiority was further verified several engineering problems.

Language: Английский

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine DOI Creative Commons
Wei Du,

Shitao Peng,

Peisen Wu

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(10), P. 2309 - 2309

Published: May 10, 2024

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV generation prediction. Extreme learning machine (ELM) was used as core model, enhanced improved beluga whale optimization (EIBWO) proposed optimize internal parameters ELM, thereby improving its for generation. Firstly, this introduced chaotic mapping strategy, sine dynamic adaptive factor, disturbance strategy optimization, EIBWO with high convergence strong ability. It verified through standard testing functions that performed better than comparative algorithms. Secondly, ELM establish model based on algorithm–optimization extreme (EIBWO-ELM). Finally, measured data output were verification, results show EIBWO-ELM more accurate regardless whether it cloudy or sunny. The R2 exceeded 0.99, highlighting efficient ability adapt is models. Compared existing models, significantly improves predictive reliability economic benefits not only provides technological foundation intelligent systems but also contributes sustainable development clean energy.

Language: Английский

Citations

3
AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems DOI Creative Commons

Guoping You,

Zhong Lu, Zhipeng Qiu

et al.

Biomimetics, Journal Year: 2024, Volume and Issue: 9(12), P. 727 - 727

Published: Nov. 28, 2024

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy (AMBWO). The adaptive population learning strategy proposed improve global BWO. introduction roulette equilibrium selection allows have more reference points choose among during exploitation phase, which enhances flexibility algorithm. In addition, avoidance improves algorithm’s ability escape optima enriches quality. order validate performance AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on CEC2017 CEC2022 test sets. Statistical tests, convergence analysis, stability analysis show that AMBWO exhibits superior overall performance. Finally, applicability superiority was further verified several engineering problems.

Language: Английский

Citations

0