Home // ADVCOMP 2016, The Tenth International Conference on Advanced Engineering Computing and Applications in Sciences // View article
Authors:
Rudolf Neydorf
Ivan Chernogorov
Victor Polyakh
Orkhan Yarakhmedov
Yulia Goncharova
Dean Vucinic
Keywords: searching optimization; multi-extremal; genetic algorithm; swarm algorithm; ant algorithm
Abstract:
The investigated objective of this paper is the search optimization task of multiextremal objects, which is considered to be more complicated than the optimization tasks of mono-extremal objects. This work postulates that in order to achieve this goal, the heuristics algorithms are the only ones able to provide suitable solutions. Therefore, 3 of the most popular and devised approaches have been considered: (1) the method of swarming particles, (2) evolutionary-genetic approach and (3) ant algorithm. The conducted research has established the common test environment for comparing the multi-extremal Rastrigin function, with the 3 investigated methods. It is clearly shown that all of these 3 methods are quite appropriate for solving the multiextremal tasks. However, in each of the addressed heuristic algorithms, we have applied their own specific characteristics to solve the problem of detection and identification of the global and local extrema. These approaches have been combined together due to the general need of data clustering. It is illustrated that, when solving an extremal task, each of these methods can provide the desired solution for a fairly wide range of imposed accuracies and available resource times.
Pages: 44 to 51
Copyright: Copyright (c) IARIA, 2016
Publication date: October 9, 2016
Published in: conference
ISSN: 2308-4499
ISBN: 978-1-61208-506-7
Location: Venice, Italy
Dates: from October 9, 2016 to October 13, 2016