Application of the New FAAO Metaheuristics in Modeling and Simulation of the Search for the Optimum of a Function with Many Extremes

Czerniak, Jacek M.; Ewald, Dawid; Zarzycki, Hubert; Augustyn, Piotr

doi:10.1007/978-3-030-47024-1_30

Advances in Intelligent Systems and Computing

2020

DOI: 10.1007/978-3-030-47024-1_30

|View full text |Cite

Application of the New FAAO Metaheuristics in Modeling and Simulation of the Search for the Optimum of a Function with Many Extremes

Jacek M. Czerniak

Dawid Ewald

Hubert Zarzycki

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

2021

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems

Ewald¹,

Zarzycki²,

Apiecionek³

et al. 2020

jucs

View full text Add to dashboard Cite

The paper presents an innovative OFNBee optimization method based on combining the swarm intelligence with the use of directed fuzzy numers OFN. In the introduction, the issues related to the subject of the study, including bee algorithms and OFN numbers, were reviewed. The innovative OFNBee algorithm was presented and verified against a set of known benchmarks functions such as Sphere, Rastrigin, Griewank, Rosenbrock, Schwefel and Ackley. These functions have been applied due to their reliability in the literature. In the further part of the study, the configuration of the algorithm parameters is carried out, including the launch of each mathematical function several dozen times for different data, such as different population sizes. The key part of the research and analysis was to compare OFNBee with six standard ABC, MBO, IMBO, TLBO, HBMO, BBMO bee algorithms. The article ends with a summary and an indication of the possible future works.

show abstract

Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems

Ewald¹,

Zarzycki²,

Apiecionek³

et al. 2020

jucs

View full text Add to dashboard Cite

show abstract

Análisis comparativo de la modificación del parámetro de inercia para la mejora en el desempeño del algoritmo PSO

2021

View full text Add to dashboard Cite

En este trabajo se presenta un desarrollo para mejorar el desempeño del algoritmo de optimización metaheurístico nombrado Particle Swarm Optimization (PSO). El algoritmo PSO está inspirado en el comportamiento que demuestran los grupos de individuos en la naturaleza, como ejemplo podemos mencionar las parvadas y los cardúmenes. Cada individuo o partícula, de forma análoga en un proceso matemático; es considerado como una posible solución y en ellos se contempla, como información relevante, su posición y la velocidad. La velocidad de cada partícula es modificada al multiplicarse por un parámetro nombrado factor de inercia y es este parámetro que proponemos modificar para mejorar el desempeño del algoritmo. La modificación del factor de inercia se desarrolla de dos maneras, decremento lineal y decremento caótico. Se han considerado las funciones de referencia Eggholder y Six-Hump Camelback, para determinar la mejora en el desempeño del algoritmo PSO. Los resultados presentados en este trabajo indican un mejor desempeño al aplicar el decremento de tipo caótico al factor de inercia.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Application of the New FAAO Metaheuristics in Modeling and Simulation of the Search for the Optimum of a Function with Many Extremes

Cited by 2 publications

References 22 publications

Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems

Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems

Análisis comparativo de la modificación del parámetro de inercia para la mejora en el desempeño del algoritmo PSO

Contact Info

Product

Resources

About