Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 3 Sayı: 1, 38 - 45, 26.02.2020

Öz

Kaynakça

  • [1]. Özyön, S, Yaşar, C, Temurtaş, H. Incremental gravitational search algorithm for high-dimensional benchmark functions. Neural Computing and Applications 2019, 31(8), 3779-3803.
  • [2]. Peitgen, H, Jurgens, H, Saupe, D. Chaos and Fractals: New frontiers of science. Berlin, Springer-Verlag, 1992.
  • [3]. Durmuş, B, Özyön, S, Temurtaş, H. Gravitational search algorithm with chaotic map (GSA-CM) for solving optimization problems. International Journal of Research in Engineering and Technology 2016, 5(2), 204-212.
  • [4]. Saremi, S, Mirjalili, S, Lewis, A. Biogeography-based optimisation with chaos. Neural Computing and Applications 2014; 25(5), 1077-1097.
  • [5]. Mitic, M, Vukovic, N, Petrovic, M, Miljkovic, Z. Chaotic fruit fly optimization algorithm. Knowledge-Based Systems 2015, 89, 446-458.
  • [6]. Gandomi, AH, Yang, XS. Chaotic bat algorithm. Journal of Computational Science 2014, 5(2), 224-232.
  • [7]. Wang, GG, Guo, L, Gandomi, AH, Hao, GS, Wang, H. Chaotic krill herd algorithm. Information Sciences 2014, 274, 17-34.
  • [8]. Alataş, B. Chaotic harmony search algorithms. Applied Mathematics and Computation 2016, 216(9), 2687-2699.
  • [9]. Alataş, B, Akın, E, Bedri, O. Chaos embedded particle swarm optimization algorithms. Chaos, Solitons and Fractals 2009, 40(4), 1715-1734.
  • [10]. Chaoshun, L, Jianzhong, Z, Jian, X, Han, X. Parameters identification of chaotic system by chaotic gravitational search algorithm. Chaos, Solitons and Fractals 2012, 45(4), 539-547.
  • [11]. Ozer, AB. CIDE: Chaotically initialized differential evolution. Expert Systems with Applications 2010, 37(6), 4632-4641.
  • [12]. Jordehi, AR. Chaotic bat swarm optimisation (CBSO). Applied Soft Computing 2015, 26, 523-530.
  • [13]. Alataş, B. Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications 2010, 37(8), 5682-5687.
  • [14]. Kaveh, A, Talahatari, S. A novel heuristic optimization method: charged system search. Acta Mechanica 2010, 213(3-4), 267-289.
  • [15]. Özyön, S, Temurtaş, H, Durmuş, B, Kuvat, G. Charged system search algorithm for emission constrained economic power dispatch problem. Energy 2012, 46(1), 420-430.
  • [16]. Özyön, S, Durmuş, B, Yaşar, C, Temurtaş, H, Kuvat, G. Solution to non-convex economic power dispatch problems with generator constraints by charged system search algorithm. International Review of Electrical Engineering (IREE) 2012, 7(5), 5840-5853.
  • [17]. Mahdi, A, Jawad, AK, Hreshee, SS. Digital chaotic scrambling of voice based on Duffing Map. International Journal of Information and Communication Sciences 2016, 1(2), 16-21.
  • [18]. Bucolo, M, Caponetto, R, Fortuna, L, Frasca, M, Rizzo, A. Does chaos work better than noise?. IEEE Circuits and Systems Magazine 2002, 2(3), 4-19.
  • [19]. Henon, M. A two-dimensional mapping with a strange attractor. Communications in Mathematical Physics 1976, 50, 69-77.
  • [20]. Caponetto, R, Fortuna, L, Fazzino, S, Xibilia, MG. Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Transactions on Evolutionary Computation 2003, 7(3), 289-304.
  • [21]. Aboites, V, Liceaga, D, Kir’yanov, A, Wilson, M. Ikeda Map and phase conjugated ring resonator chaotic dynamics. Applied Mathematics & Information Sciences 2016, 10(6), 1-6.

Chaotic Charged System Search Algorithm

Yıl 2020, Cilt: 3 Sayı: 1, 38 - 45, 26.02.2020

Öz

Optimization algorithms are frequently used in literature in order to be able to obtain a suitable solution, within acceptable times, for the problems which are impossible to solve with numerical methods or which take a long time to solve. Recently, an increasing number of optimizations have been designed and contributed to the literature. Besides, many of the designed algorithms show a great similarity with each other. Therefore, researchers do studies on strong and decisive optimization algorithms with various methods. The aim of these studies is to make the algorithm stronger, faster and more decisive. One of these aforementioned methods is the use of chaotic number generators in random number generations while forming the initial populations of the algorithms with good results, which has been applied to many algorithms. In this study, five different chaotic number generators with different structures have been integrated to charged system search (CSS) algorithm which is one of the strong optimization algorithms and has been applied in the solution of many problems successfully and chaotic charged system search (CCSS) algorithms have been proposed. In order to evaluate the performances of these suggested algorithms and to define which chaotic structure is more compatible with CSS algorithm, five unimodal test functions have been solved and the obtained results have been evaluated. In the suggested algorithms, while forming the particles in the first population, the first particle has been positioned in the search space randomly. As for the other particles, they have been positioned depending on the position of the first particle by using chaotic mapping methods. Namely, the individuals have spread in the search space according to a chaotic order, not randomly. The selected five chaotic methods have been defined as Duffing, Gauss/Mouse, Henon, Icmıc ve Ikeda mapping methods. The test functions solved in the study are Schwefel’s No: 2.22, Schwefel’s No: 1.2, Schwefel’s No: 2.21 and Rosenbrock functions. All functions have been solved 30 times each for 30 dimension and the statistical results and the graphics have been given.

Kaynakça

  • [1]. Özyön, S, Yaşar, C, Temurtaş, H. Incremental gravitational search algorithm for high-dimensional benchmark functions. Neural Computing and Applications 2019, 31(8), 3779-3803.
  • [2]. Peitgen, H, Jurgens, H, Saupe, D. Chaos and Fractals: New frontiers of science. Berlin, Springer-Verlag, 1992.
  • [3]. Durmuş, B, Özyön, S, Temurtaş, H. Gravitational search algorithm with chaotic map (GSA-CM) for solving optimization problems. International Journal of Research in Engineering and Technology 2016, 5(2), 204-212.
  • [4]. Saremi, S, Mirjalili, S, Lewis, A. Biogeography-based optimisation with chaos. Neural Computing and Applications 2014; 25(5), 1077-1097.
  • [5]. Mitic, M, Vukovic, N, Petrovic, M, Miljkovic, Z. Chaotic fruit fly optimization algorithm. Knowledge-Based Systems 2015, 89, 446-458.
  • [6]. Gandomi, AH, Yang, XS. Chaotic bat algorithm. Journal of Computational Science 2014, 5(2), 224-232.
  • [7]. Wang, GG, Guo, L, Gandomi, AH, Hao, GS, Wang, H. Chaotic krill herd algorithm. Information Sciences 2014, 274, 17-34.
  • [8]. Alataş, B. Chaotic harmony search algorithms. Applied Mathematics and Computation 2016, 216(9), 2687-2699.
  • [9]. Alataş, B, Akın, E, Bedri, O. Chaos embedded particle swarm optimization algorithms. Chaos, Solitons and Fractals 2009, 40(4), 1715-1734.
  • [10]. Chaoshun, L, Jianzhong, Z, Jian, X, Han, X. Parameters identification of chaotic system by chaotic gravitational search algorithm. Chaos, Solitons and Fractals 2012, 45(4), 539-547.
  • [11]. Ozer, AB. CIDE: Chaotically initialized differential evolution. Expert Systems with Applications 2010, 37(6), 4632-4641.
  • [12]. Jordehi, AR. Chaotic bat swarm optimisation (CBSO). Applied Soft Computing 2015, 26, 523-530.
  • [13]. Alataş, B. Chaotic bee colony algorithms for global numerical optimization. Expert Systems with Applications 2010, 37(8), 5682-5687.
  • [14]. Kaveh, A, Talahatari, S. A novel heuristic optimization method: charged system search. Acta Mechanica 2010, 213(3-4), 267-289.
  • [15]. Özyön, S, Temurtaş, H, Durmuş, B, Kuvat, G. Charged system search algorithm for emission constrained economic power dispatch problem. Energy 2012, 46(1), 420-430.
  • [16]. Özyön, S, Durmuş, B, Yaşar, C, Temurtaş, H, Kuvat, G. Solution to non-convex economic power dispatch problems with generator constraints by charged system search algorithm. International Review of Electrical Engineering (IREE) 2012, 7(5), 5840-5853.
  • [17]. Mahdi, A, Jawad, AK, Hreshee, SS. Digital chaotic scrambling of voice based on Duffing Map. International Journal of Information and Communication Sciences 2016, 1(2), 16-21.
  • [18]. Bucolo, M, Caponetto, R, Fortuna, L, Frasca, M, Rizzo, A. Does chaos work better than noise?. IEEE Circuits and Systems Magazine 2002, 2(3), 4-19.
  • [19]. Henon, M. A two-dimensional mapping with a strange attractor. Communications in Mathematical Physics 1976, 50, 69-77.
  • [20]. Caponetto, R, Fortuna, L, Fazzino, S, Xibilia, MG. Chaotic sequences to improve the performance of evolutionary algorithms. IEEE Transactions on Evolutionary Computation 2003, 7(3), 289-304.
  • [21]. Aboites, V, Liceaga, D, Kir’yanov, A, Wilson, M. Ikeda Map and phase conjugated ring resonator chaotic dynamics. Applied Mathematics & Information Sciences 2016, 10(6), 1-6.
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Original Research Articles
Yazarlar

Serdar Özyön 0000-0002-4469-3908

Yayımlanma Tarihi 26 Şubat 2020
Kabul Tarihi 26 Şubat 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 3 Sayı: 1

Kaynak Göster

APA Özyön, S. (2020). Chaotic Charged System Search Algorithm. Scientific Journal of Mehmet Akif Ersoy University, 3(1), 38-45.