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

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).

[1]  Peter I. Cowling,et al.  Hyperheuristics: Recent Developments , 2008, Adaptive and Multilevel Metaheuristics.

[2]  Graham Kendall,et al.  An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

[4]  Marcin Korytkowski,et al.  Fast image classification by boosting fuzzy classifiers , 2016, Inf. Sci..

[5]  Dawid Polap.  Neuro-heuristic voice recognition , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[6]  Julian Francis Miller,et al.  Developments in Cartesian Genetic Programming: self-modifying CGP , 2010, Genetic Programming and Evolvable Machines.

[7]  Christian Napoli,et al.  Is the Colony of Ants Able to Recognize Graphic Objects? , 2015, ICIST.

[8]  Ender Özcan,et al.  A tensor-based selection hyper-heuristic for cross-domain heuristic search , 2015, Inf. Sci..

[9]  Jordan B. Pollack,et al.  Autoconstructive evolution for structural problems , 2012, GECCO '12.

[10]  Krystian Lapa,et al.  A new approach to design of control systems using genetic programming , 2015, Inf. Technol. Control..

[11]  Graham Kendall,et al.  Hyper-Heuristics: An Emerging Direction in Modern Search Technology , 2003, Handbook of Metaheuristics.

[12]  Xin-She Yang,et al.  Firefly Algorithm: Recent Advances and Applications , 2013, ArXiv.

[13]  Mihai Oltean,et al.  Evolutionary design of Evolutionary Algorithms , 2009, Genetic Programming and Evolvable Machines.

[14]  Andries Petrus Engelbrecht,et al.  Heuristic space diversity control for improved meta-hyper-heuristic performance , 2015, Inf. Sci..

[15]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[16]  Dietrich Hummel,et al.  FORMATION FLIGHT AS AN ENERGY-SAVING MECHANISM , 2013 .

[17]  Ender Özcan,et al.  A comprehensive analysis of hyper-heuristics , 2008, Intell. Data Anal..

[18]  Rafael S. Parpinelli,et al.  An eco-inspired evolutionary algorithm applied to numerical optimization , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[19]  Valery Tereshko,et al.  Reaction-Diffusion Model of a Honeybee Colony's Foraging Behaviour , 2000, PPSN.

[20]  Rafal Grycuk,et al.  Content-based image retrieval optimization by differential evolution , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[21]  Rosni Abdullah,et al.  A Comparison between Artificial Bee Colony and Particle Swarm Optimization Algorithms for Protein Structure Prediction Problem , 2013, ICONIP.

[22]  Amir Hossein Alavi,et al.  Krill herd: A new bio-inspired optimization algorithm , 2012 .

[23]  Tansel Dökeroglu,et al.  A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem , 2016, Eng. Appl. Artif. Intell..

[24]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[25]  Xin-She Yang,et al.  Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization , 2010, NICSO.

[26]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[27]  Christian Napoli,et al.  Application of Genetic Algorithms in the Construction of Invertible Substitution Boxes , 2016, ICAISC.

[28]  Eugene Semenkin,et al.  Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition , 2016, J. Artif. Intell. Soft Comput. Res..

[29]  Mehmet Fatih Tasgetiren,et al.  Dynamic multi-swarm particle swarm optimizer with harmony search , 2011, Expert Syst. Appl..

[30]  Pinar Civicioglu,et al.  A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms , 2013, Artificial Intelligence Review.

[31]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[32]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[33]  Xiaodong Li,et al.  Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale , 2009 .

[34]  Ender Özcan,et al.  A Hyper-Heuristic Based on Random Gradient, Greedy and Dominance , 2011, ISCIS.