State Flipping Based Hyper-Heuristic for Hybridization of Nature Inspired Algorithms

• Published in: Artificial Intelligence and Soft Computing Vol. 10245; pp. 337 - 346
• Main Authors: Damaševičius, Robertas, Woźniak, Marcin
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Metaheuristic; Nature inspired algorithms; Optimization
• ISBN: 9783319590622; 3319590626
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-59063-9_30

The paper presents a novel hyper-heuristic strategy for hybridization of nature inspired algorithms. The strategy is based on switching the state of agents using a logistic probability function, which depends upon the fitness rank of an agent. A case study using two nature inspired algorithms (Artificial Bee Colony (ABC) and Krill Herding (KH)) and eight optimization problems (Ackley Function, Bukin Function N.6, Griewank Function, Holder Table Function, Levy Function, Schaffer Function N.2, Schwefel Function, Shubert Function) is presented. The results show a superiority of the proposed hyper-heuristic (mean end-rank for hybrid algorithm is 1.435 vs. 2.157 for KH and 2.408 for ABC).