Swarm robotics – a case study: bat robotics

Iglesias, Andrés; Gálvez, Antonio; Suárez, Patricia

doi:10.1016/b978-0-12-819714-1.00026-9

Cited by 10 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inspiring from echolocation behavior of microbats, the bat algorithm (BA) is proposed as a heuristic optimization algorithm (Iglesiasa et al 2020 ). Mirjalili et al ( 2014 ) proved the superiority of BA to some of the other algorithms, like GA and PSO.…”

Section: Hybrid Metaheuristic Ann For Stock Price Predictionmentioning

confidence: 99%

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Farahani

Hajiagha

2021

Soft Comput

View full text Add to dashboard Cite

Section: Hybrid Metaheuristic Ann For Stock Price Predictionmentioning

confidence: 99%

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Farahani

Hajiagha

2021

Soft Comput

View full text Add to dashboard Cite

“…Swarm Based Intelligence Techniques: Swarm intelligence (SI) refers to a subset of bioinspired techniques. The individuals in the swarm collectively organize themselves to achieve a common goal [24]. Particle Swarm Optimization (PSO) is one of the popular swarm intelligence methods [32].…”

Section: Introductionmentioning

confidence: 99%

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Reddy¹,

Kulkarni²,

Krishnasamy³

et al. 2022

Preprint

View full text Add to dashboard Cite

This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behaviour exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (Leader, Advocate, Believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. Besides benchmark problems, the LAB algorithm was applied for solving challenging engineering problems, including abrasive water jet machining, electric discharge machining, micro-machining processes, and process parameter optimization for turning titanium alloy in a minimum quantity lubrication environment. The results were superior to the other algorithms compared such as Firefly Algorithm, Variations of Co-hort Intelligence, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimisation, and Multi-Cohort Intelligence. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features of the LAB algorithm along with its limitations are also discussed.

show abstract

“…Swarm Based Intelligence Techniques: Swarm intelligence (SI) refers to a subset of bioinspired techniques. The individuals in the swarm collectively organize themselves to achieve a common goal [25]. Particle Swarm Optimization (PSO) is one of the popular swarm intelligence methods [33].…”

Section: Introductionmentioning

confidence: 99%

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Reddy

Kulkarni

Krishnasamy

et al. 2022

Preprint

View full text Add to dashboard Cite

This manuscript introduces a new socio-inspired metaheuristic technique referred to as Leader-Advocate-Believer based optimization algorithm (LAB) for engineering and global optimization problems. The proposed algorithm is inspired by the AI-based competitive behaviour exhibited by the individuals in a group while simultaneously improving themselves and establishing a role (Leader, Advocate, Believer). LAB performance in computational time and function evaluations are benchmarked using other metaheuristic algorithms. The LAB algorithm was applied for solving engineering problems, including abrasive water jet machining, electric discharge machining, micromachining processes and turning of titanium alloy in a minimum quantity lubrication environment. The results were compared with other algorithms such as Firefly Algorithm, Cohort Intelligence, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimisation and Multi-Cohort Intelligence. The results from this study highlighted that the LAB outperforms the other algorithms in terms of function evaluations and computational time. The prominent features and limitations of the LAB algorithm are also discussed.

show abstract

Swarm robotics – a case study: bat robotics

Cited by 10 publications

References 39 publications

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

Forecasting stock price using integrated artificial neural network and metaheuristic algorithms compared to time series models

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

LAB: A Leader-Advocate-Believer Based Optimization Algorithm

Contact Info

Product

Resources

About