Please use this identifier to cite or link to this item:

Title: First results from a novel multispecies predator-prey optimization algorithm
Authors: Cavaleiro Costa, Sérgio
Janeiro, Fernando M.
Malico, Isabel
Keywords: Optimization
Swarm intelligence
Genetic algorithms
Issue Date: 2021
Citation: Cavaleiro Costa, S., Janeiro, F. M., Malico, I. (2021). First results from a novel multispecies predator-prey optimization algorithm. 5th International Conference on Numerical and Symbolic Computation. Developments and Applications – SYMCOMP 2021, Évora, Portugal, 25-26 de março, pp. 173-178.
Abstract: Optimization plays a central role in today's society. Different strategies have been proposed over the years, and their application is not independent of the problem. In this work, a novel predator-prey optimization algorithm (PPA) is presented and compared with genetic algorithms (GA). In the proposed algorithm, the search is based on the behaviour of a predefined number of species, some acting as predators and others as prey. The former pursue not only the latter to prey but also members of the same species to mate. On the other hand, preys run away from predators, but are attracted to other members of the same species. The performance of the proposed algorithm is tested with the help of four benchmark test functions: Goldstein-Price, Mishra's bird, Michalewicz and Eggholder functions. Each of these functions expose both PPA and GA to different difficulties. Five hundred runs were performed for each of the four test functions. Despite the better performance of the PPA compared to GA in all benchmarks analysed, the algorithm stands out when minimizing the Eggholder function, which has many local minima and the global minimum located near the edge of the search space. In the case of this function, the success rate is 75.6% for the PPA against 29.0% for the GA, when considering a tolerance of 1.0×10-2. The algorithm presented in this work has shown a resilient convergence to the global minimum with fewer iterations than the genetic algorithms, suggesting promising results in problems with similar characteristics.
Type: article
Appears in Collections:DEM - Artigos em Livros de Actas/Proceedings
CEM - Artigos em Livros de Actas/Proceedings

Files in This Item:

File Description SizeFormat
symcomp2021_PP.pdf572.13 kBAdobe PDFView/OpenRestrict Access. You can Request a copy!
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois