Allouani Fouad
Department of Industrial Engineering, University of Khenchela, Algeria
Kai Zenger
Department of Electrical Engineering and Automation, Aalto University, Aalto, Finland
Xiao-Zhi Gao
Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Lappeenranta, Finland / School of Computing, University of Eastern Finland, Kuopio, Finland
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http://dx.doi.org/10.3384/ecp171421060Published in: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Linköping Electronic Conference Proceedings 142:156, p. 1060-1066
Published: 2018-12-19
ISBN: 978-91-7685-399-3
ISSN: 1650-3686 (print), 1650-3740 (online)
The Flower Pollination Algorithm (FPA) is a new natural bio-inspired optimization algorithm that mimics the real-life processes of the flower pollination. Thus, the latter has a quick convergence, but its population diversity and convergence precision can be limited in some applications. In order to improve its intensification (exploitation) and diversification (exploration) abilities, we have introduced a simple modification in its general structure. More precisely, we have added both Crossover and Mutation Genetic Algorithm (GA) operators respectively, just after calculating the new candidate solutions and the greedy selection operation in its basic structure. The proposed method, called FPA-GA has been tested on all the CEC2005 contest test instances. Experimental results show that FPA-GA is very competitive.
