https://github.com/atulkamble/fruit-fly-optimization-algorithm

Performance Evaluation of Fruit Fly Optimization Algorithm on Classical Test Problem Set
https://github.com/atulkamble/fruit-fly-optimization-algorithm

algorithms evolutionary-computation optimization

Last synced: 10 months ago
JSON representation

Performance Evaluation of Fruit Fly Optimization Algorithm on Classical Test Problem Set

• Host: GitHub
• URL: https://github.com/atulkamble/fruit-fly-optimization-algorithm
• Owner: atulkamble
• License: mit
• Created: 2019-06-10T14:18:26.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2022-06-18T12:27:24.000Z (about 4 years ago)
• Last Synced: 2025-03-04T20:35:59.077Z (over 1 year ago)
• Topics: algorithms, evolutionary-computation, optimization
• Language: C++
• Homepage:
• Size: 6.64 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

README

          
# Performance-Evaluation-of-Fruit-Fly-Optimization-Algorithm-on-Classical-Test-Problem-Set

 - The fruit flies are the second smallest member among the model creatures in the narrow sense which has only hundreds of neurons and with no cerebrum. 

 - They are pulled in to matured or aging sustenance through their detecting and recognition qualities, particularly in osphresis and vision Tsao Pan (2012) proposed a new algorithm called fruit flies optimization algorithm (FFOA) motivated by the behaviour of real fruit flies. 

 

 - The Fruit Fly Optimization Algorithm is an easy evolution algorithm for maximizing global optimization based on the food searching behaviour of the fruit fly swarm. Fruit flies have better senses and perceptions than other species, particularly its olfactory and visual senses. 

 - The individual fruit fly samples the differing scents that are present in its surroundings, then uses its sensitive vision to fly in the general direction of the target. 

 

 - It transmits information to the rest of the swarm and direct the ‘flocking location' of the swarm closer to the location of the food. 

Through ‘iterative evolution,” the fly swarm comes closer and closer to the food source, until the target is reached.  

# Research Objectives 

- To implement FFOA to the classical test problem sets (CEC2013 or CEC2015). 

- To check the analysis of result of classical test problem.