.png){kind=icon}
.png){kind=icon}
Return
Honey formation optimization framework for design problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2020.125815.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, Honey Formation Optimization (HFO) Framework is proposed for design problems where the functions of the objective functions are known or under design. We applied HFO framework to three ABC variants, namely, ABC, GABC and ABC/Best/1 by adapting their local search strategies into HFO. Thus, 6 algorithms consisting of 3 ABC variants (ABC, GABC, ABC/Best/1) and 3 HFO variants (HFO-ABC, HFO-GABC, HFO-ABC/Best/1) are comparatively studied. The HFO-GABC and HFO-ABC/Best/1 algorithms are our new contributions to the literature. The HFO considers a food source consisting of multiple components, and models the honey formation inside bees as a process of mixing the components with their special enzymes according to a honey function. Each food source has its own associated honey form. Thus, HFO looks for the best honey form. HFO requires component design where component function measures the amount of component (component fitness) in a food source and the honey function maps the components to honey amount (quality) to measure the honey fitness. HFO generalises the ABC by considering worker bees that exploit only the selected components of the food sources. In this paper, the 6 algorithms are compared on the basis of 14 benchmark functions, alongside demonstration of the component design. The results show that HFO variants increase the exploitation and exploration abilities of ABC variants significantly. (C) 2020 Elsevier Inc. All rights reserved.
Keywords:
Honey formation optimisation
Component design
Honey function
Bee colony algorithm
Objective function decomposition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W