A step-size follow-the-leader optimization algorithm with an improved step parameters DOI Creative Commons
Priyanka Singh, Rahul Kottath

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 9, P. 100360 - 100360

Published: Nov. 6, 2023

Follow the leader (FTL) algorithm is a newly developed optimization inspired by sheep's movement within flock. FTL has been successfully implemented to solve power prediction problems. However, probability of falling in local optima high due randomness step parameter. This paper proposes step-size follow-the-leader (SFTL) with decreasing and increasing combinations. The improved parameter tunes search space generating new solution improve accuracy convergence rate algorithm. Four different variants have presented this show impact dynamic improvement verified testing SFTL over thirty-two fixed unimodal, multimodal, multimodal benchmark functions. computational results indicate that significantly improves basic converges early compared other algorithms. also tested on five real engineering design problems obtained outperformed popular

Language: Английский

A hybrid moth–flame algorithm with particle swarm optimization with application in power transmission and distribution DOI Creative Commons

Muhammad Suhail Shaikh,

Saurav Raj, Rohit Babu

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 6, P. 100182 - 100182

Published: Feb. 7, 2023

The transmission lines are used for power distribution across large distances. Different parameters affect the efficiency, and quality of service. Furthermore, system parameter estimation is crucial flow analysis, electric expansion planning, stability, dispatch, economic analysis. This task created by utilizing identification techniques, with analytical method being most commonly utilized methodology acquiring line data. However, to address an issue that simplifies these techniques have significant downsides — such as non-recursive accessibility appropriately transposed line. paper presents a hybrid moth-flame optimization (MFO) particle swarm (PSO) estimating based on various scenarios mathematical validation different benchmark functions. concepts MFO PSO rationally integrated into this algorithm overcome their limitations improve global search ability. Regarding solution convergence speed results show proposed performs better than conventional original MFO.

Language: Английский

Citations

58
State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network DOI
Hao Zhang, Jingyi Gao, Le Kang

et al.

Energy, Journal Year: 2023, Volume and Issue: 283, P. 128742 - 128742

Published: Aug. 11, 2023

Language: Английский

Citations

56
Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems DOI Creative Commons

Jiaxu Huang,

Haiqing Hu

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 2, 2024

Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal multimodal problems. However, convergence speed performance still have some deficiencies when complex multidimensional Therefore, this paper proposes hybrid method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive spiral predation strategy, Nelder-Mead simplex search (NM). Firstly, initialization phase, QOBL strategy introduced. This reconstructs initial spatial position population by pairwise comparisons to obtain more prosperous higher quality population. Subsequently, an designed exploration exploitation phases. The first learns optimal individual positions dimensions through avoid loss local optimality. At same time, movement motivated cosine factor introduced maintain balance between exploitation. Finally, NM added. It corrects multiple scaling methods improve accurately efficiently. verified utilizing CEC2017 CEC2019 test functions. Meanwhile, superiority six engineering design examples. experimental results show has feasibility effectiveness practical problems than methods.

Language: Английский

Citations

24
A multi-objective thermal exchange optimization model for solving optimal power flow problems in hybrid power systems DOI Creative Commons
Sunilkumar Agrawal, Sundaram B. Pandya, Pradeep Jangir

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 8, P. 100299 - 100299

Published: Aug. 9, 2023

This study addresses the challenges associated with optimal power flow (OPF) management in hybrid systems incorporating diverse energy sources, particularly focusing on unpredictability of renewable sources (RESs). A novel analytics approach is introduced using Multi-Objective Thermal Exchange Optimization (MOTEO). MOTEO based modeling transfer grounded Newton's Law Cooling. The model integrates innovative non-dominated sorting and crowing distance strategies to effectively solve multi-objective optimization problem. proposed OPF incorporates four primary types resources: thermal, wind, solar, small-hydro, offering a holistic systems. Our model's practical applicability efficiency are validated through rigorous testing modified IEEE 30-Bus system, benchmarked against other contemporary methodologies. results demonstrate that successfully identifies solutions for (MOOPF) problem while maintaining compliance stringent system constraints. contribution enhances field by providing robust efficient handle complex systems, thereby ensuring increased reliability.

Language: Английский

Citations

27
An Adaptive Sand Cat Swarm Algorithm Based on Cauchy Mutation and Optimal Neighborhood Disturbance Strategy DOI Creative Commons
Xing Wang, Qian Liu, Li Zhang

et al.

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 191 - 191

Published: May 4, 2023

Sand cat swarm optimization algorithm (SCSO) keeps a potent and straightforward meta-heuristic derived from the distant sense of hearing sand cats, which shows excellent performance in some large-scale problems. However, SCSO still has several disadvantages, including sluggish convergence, lower convergence precision, tendency to be trapped topical optimum. To escape these demerits, an adaptive based on Cauchy mutation optimal neighborhood disturbance strategy (COSCSO) are provided this study. First foremost, introduction nonlinear parameter favor scaling up global search helps retrieve optimum colossal space, preventing it being caught Secondly, operator perturbs step, accelerating speed improving efficiency. Finally, diversifies population, broadens enhances exploitation. reveal COSCSO, was compared with alternative algorithms CEC2017 CEC2020 competition suites. Furthermore, COSCSO is further deployed solve six engineering The experimental results that strongly competitive capable practical

Language: Английский

Citations

23
A comprehensive review of dwarf mongoose optimization algorithm with emerging trends and future research directions DOI Creative Commons

Olanrewaju L. Abraham,

Md Asri Ngadi

Decision Analytics Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100551 - 100551

Published: Feb. 1, 2025

Language: Английский

Citations

1
A modified Particle Swarm Optimization algorithm with enhanced search quality and population using Hummingbird Flight patterns DOI Creative Commons
Mohsen Zare,

Mohammad‐Amin Akbari,

Rasoul Azizipanah‐Abarghooee

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 7, P. 100251 - 100251

Published: May 12, 2023

This study proposes a modified Particle Swarm Optimization (PSO) algorithm based on Hummingbird Flight (HBF) patterns to enhance the search quality and population diversity. The HBF has five concepts: (1) Smaller steps toward position updating are more likely than larger ones, (2) Position changes made step by throughout flight, (3) energy is conserved during nectar-searching process, (4) Hummingbirds do not fly in large groups confined spaces, (5) Simultaneous all directions realistic. A comprehensive two CEC-2010 CEC-2013 benchmark suites conducted verify effectiveness of proposed PSO-HBF algorithm. also evaluated compared other well-known PSO algorithms using shifted rotated CEC 2005 2014 functions. Four cases economic dispatch, 10-unit reserve constraint, 30-unit dynamic dispatch (DED) further examined. last investigate how deals with large-scale practical problems. results demonstrated that superior seven algorithms, improving eight ten functions 2010 2013 benchmarks, respectively. Furthermore, achieving third rank among nineteen improved confirms Moreover, DED problem, show significant improvement over previously published papers. algorithm's source code can be accessed publicly at http://www.optim-app.com/projects/psohbf.

Language: Английский

Citations

16
A systematic review of the literature on video assistant referees in soccer: Challenges and opportunities in sports analytics DOI Creative Commons
Maiquiel Schmidt de Oliveira, Vilmar Steffen, Flávio Trojan

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 7, P. 100232 - 100232

Published: April 20, 2023

Refereeing in sports is about fairness. The referee's job to balance what the player with ball thinks fair trying take fair. Referees are judges on those two opposite opinions. Football (soccer) has massive global appeal and fan interest. Some football championships now use Video Assistant (VAR) help referees make correct decisions. This study reviews literature VAR using Methodi Ordinatio. Ordinatio a methodology used select rank relevant scientific papers combining impact factor, number of citations, year publication. We present case distance officials cover main Brazilian championship (Brazilian Serie A). adopts p-medians method analyse opening operation rooms covered by professionals travelling officiate matches. locations were obtained, applying these first ten rounds A season 2021 would possibly reduce 70% compared total performed.

Language: Английский

Citations

15
Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study DOI Creative Commons
Amad Zafar, Shaik Javeed Hussain, Muhammad Umair Ali

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(7), P. 3714 - 3714

Published: April 3, 2023

In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce dataset's dimensionality, increase computing effectiveness, and enhance BCI's performance. Using activity-related features leads high classification rate among desired tasks. This study presents wrapper-based metaheuristic framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, temporal statistical (i.e., mean, slope, maximum, skewness, kurtosis) were computed from all available channels form training vector. Seven optimization algorithms tested their performance k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search firefly algorithm, bat flower pollination whale grey wolf (GWO). presented approach was validated based on an online dataset motor imagery (MI) mental arithmetic (MA) tasks 29 healthy subjects. results showed that accuracy significantly improved by utilizing selected relative those obtained full set features. All abovementioned reduced vector size. GWO yielded highest average rates (p < 0.01) 94.83 ± 5.5%, 92.57 6.9%, 85.66 7.3% MA, MI, four-class (left- right-hand baseline) tasks, respectively. may be helpful in phase selecting appropriate robust fNIRS-based applications.

Language: Английский

Citations

13
An improved artificial rabbits optimization for accurate and efficient infinite impulse response system identification DOI Creative Commons
Rizk M. Rizk‐Allah, Serdar Ekinci, Davut İzci

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 9, P. 100355 - 100355

Published: Nov. 3, 2023

Identifying models with Infinite Impulse Response (IIR) is crucial in signal processing and system identification. This paper addresses the challenges of IIR model identification by proposing an improved version Artificial Rabbits Optimization (ARO) algorithm called ARO (IARO). The IARO integrates adaptive local search mechanism experience-based perturbed learning strategy as two key enhancements to improve effectiveness ARO. These additions aim address loss accuracy during iterations algorithm's ability exploit promising areas. Four benchmark examples different plants are considered, performance proposed compared existing competitive methods. results consistently demonstrate that outperforms convergence for across all orders systems. Visual analysis, curves, coefficient comparison, statistical metrics comparison validate superiority algorithm. Additionally, Wilcoxon signed-rank test provide further evidence supporting superior IARO. comprehensive analysis showcases efficacy accurately identifying work represents a significant advancement identification, offering methodology accurate efficient modeling.

Language: Английский

Citations

12