The Chaos-Based Approaches for Actual Metaheuristic Algorithms
Öz
Along with rapid developments in computational technologies,
evolutionary/heuristic/metaheuristic algorithms have frequently become used in
many applications to solve optimization problems. Nowadays, new algorithms are
being developed and improvements have been made to existing algorithms. In this
study, chaos-based modifications have been proposed for recently introduced metaheuristic
algorithms: Backtracking Search (BS), Grey Wolf Optimizer (GWO) and Vortex
Search (VS), and the algorithms have been analyzed by detailed comparisons. The
proposed approaches are based on generating new values through chaos maps,
rather than some random numbers normally used in the algorithms, to improve
their solutions. In addition, some modifications are performed to the
structural operations of the algorithms used in the optimization process by
taking advantage of chaos-based values. The performances of the algorithms are
evaluated by considering two metrics: convergence rates and statistical
results. Experiments demonstrated that the performance of the algorithms with
the proposed modifications based on the chaos approach, are better than, or at least comparable to,
the original algorithms.
Anahtar Kelimeler
Kaynakça
- Alatas, B., Akin, E. and Ozer, A. B. (2009) Chaos embedded particle swarm optimization algorithms, Chaos, Solitons Fractals, 40(4), 1715-1734. doi: 10.1016/j.chaos.2007.09.063
- Civicioglu, P. (2013) Backtracking search optimization algorithm for numerical optimization problems, Applied Mathematics and Computation, 219(15), 8121-8144, 2013. doi: 10.1016/j.amc.2013.02.017
- Dogan, B. and Olmez, T. A. (2015) A new metaheuristic for numerical function optimization: vortex search algorithm, Information Sciences, 293, 125-145. doi: 10.1016/j.ins.2014.08.053
- Gandomi, A., Yang, X-S., Talatahari, S. and Alavi, A. (2013) Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation., 18(1), 89-98. doi: 10.1016/j.cnsns.2012.06.009
- Geem, Z., Kim, J. and Loganathan, G. (2001) A new heuristic optimization algorithm: harmony search, Simulation, 76(2), 60-68. doi: 10.1177/003754970107600201
- Goldberg D. E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing, USA.
- Kellert, S. (1993) In the Wake of Chaos:Unpredictable Order in Dynamical Systems, University of Chicago Press, USA.
- Kennedy J. and Eberhart R. (1995) Particle swarm optimization, IEEE International Conference on Neural Networks, 1942-1948. doi:10.1109/ICNN.1995.488968
Ayrıntılar
Birincil Dil
İngilizce
Konular
Mühendislik
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
31 Aralık 2018
Gönderilme Tarihi
2 Mayıs 2018
Kabul Tarihi
17 Ekim 2018
Yayımlandığı Sayı
Yıl 2018 Cilt: 23 Sayı: 3