Open AccessResearch on the hyper-heuristic of Sub-domain Elimination Strategies based on Firefly Algorithm
Author(s) -
Mingquan Sun,
Bangsheng Xing,
DongYeol Yang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1966/1/012024
Subject(s) - firefly algorithm , heuristic , heuristics , computer science , hyper heuristic , domain (mathematical analysis) , mathematical optimization , algorithm , realization (probability) , artificial intelligence , mathematics , mathematical analysis , robot learning , statistics , particle swarm optimization , robot , mobile robot
In this study, a hyper-heuristic named Sub-domain Elimination Strategies based on Firefly Algorithm (SESFA) is proposed. First, a typical hyper-heuristic is usually using the high-level strategy selection or the combination of the low-level heuristics to obtain a new hyper-heuristic, each round of optimization process is carried out in the whole problem domain. However, SESFA evaluates the problem domain through the feedback information of the meta-heuristic at the lower level, eliminating the poor performance areas, and adjusting the underlying heuristic or adjusting the algorithm parameters to improve the overall optimization performance. Second, the problem domain segmentation function in SESFA can reduce the complexity of the objective function within a single sub-domain, which is conducive to improving the optimization efficiency of the underlying heuristic. Further, the problem domain segmentation function in SESFA also makes there is no direct correlation between different sub-domains, so different underlying heuristics can be adopted in different sub-domains, which is beneficial to the realization of parallel computing. Comparing SESFA with Firefly Algorithms with five standard test functions, the results show that SESFA has advantages in precision, stability and success rate.