Firefly Algorithm Hybridized with Flower Pollination Algorithm for Multimodal Functions

The successful evolutionary characteristics of biological systems have motivated the researchers to use various nature-inspired algorithms to solve various real-world problems that are complex in nature. These algorithms have the capability to find optimum solutions faster than conventional algorithms. The proposed algorithm uses two terms, exploration and exploitation, effectively from Firefly Algorithm (FA) and Flower Pollination Algorithm (FPA). The proposed algorithm (FA/FPA) is validated using various standard benchmark functions and further its comparison is done with FA and FPA. The result evaluation of the proposed algorithm compute better performance than FA and FPA on most of the benchmark functions.

[1]  Ana Carolina Olivera,et al.  A Parallel Discrete Firefly Algorithm on GPU for Permutation Combinatorial Optimization Problems , 2014, CARLA.

[2]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

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

[4]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[5]  K.M. Passino,et al.  Stability analysis of social foraging swarms , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  S. Arora,et al.  A conceptual comparison of firefly algorithm, bat algorithm and cuckoo search , 2013, 2013 International Conference on Control, Computing, Communication and Materials (ICCCCM).

[7]  Xin-She Yang,et al.  Flower pollination algorithm: A novel approach for multiobjective optimization , 2014, ArXiv.

[8]  Beverley J. Glover,et al.  Understanding flowers and flowering : an integrated approach , 2007 .

[9]  N. Waser.  Flower Constancy: Definition, Cause, and Measurement , 1986, The American Naturalist.

[10]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[11]  Xin-She Yang,et al.  Multi-Objective Flower Algorithm for Optimization , 2014, ICCS.

[12]  Satvir Singh,et al.  The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection , 2013 .

[13]  Xin-She Yang,et al.  Multiobjective firefly algorithm for continuous optimization , 2012, Engineering with Computers.

[14]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[15]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[16]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

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

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

[19]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[20]  B. Glover.  Understanding Flowers and Flowering , 2007 .

[21]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[22]  Xin-She Yang,et al.  Firefly Algorithm, Lévy Flights and Global Optimization , 2010, SGAI Conf..