Abstract
Rastgele
sayı dizileri, sezgisel optimizasyon algoritmalarının stokastik yapılarından
dolayı sıkça kullanılmaktadır. Sezgisel optimizasyon algoritmaların global
optimuma yakınsamalarında rastgele üretilen sayı dizilerinin dağılımları
oldukça etkili olmaktadır. Rastgele üretilen sayıların belli bir alanda
toplanması veya aynı değerler üretilmesi ile algoritmanın lokal optimumlara
takılma riski artabilmektedir. Bu nedenle üretilen sayıların aynı olmaması,
yayılmış spektruma sahip olması sezgisel algoritmaların performansını etkileyen
durumlardır. Kaotik haritalar ayrık zamanlı sistemlerdir ve kaotik haritalarla
üretilen sayılar geniş bir spektruma sahip olup periyodik değildir. Bu nedenle,
sezgisel algoritmalarda kaotik harita kullanarak optimum noktalardan kaçmak ya
da yerel optimuma takılma riskini azaltmak mümkün olabilmektedir. Bu çalışmada
kaotik haritaların yeni optimizasyon yöntemlerinden olan Altın Sinüs
Algoritması üzerindeki etkisi incelenmiştir.
References
- [1] Alatas B, Akin E, Ozer AB. Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals 2009. 40(4):1715-1734. [2] Alatas B. Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 2010. 37(8): 5682-5687. [3] Jun-feng Y, Chi M, Xiao-qi P, Zhi-kun H, Jun H. A new optimization approach-chaosgenetic algorithm, Syst Eng 2001. 1:1-5. [4] Zhenyu G, Bo C, Min Y, Binggang C. Self-adaptive chaos differential evolution. Adv Nat Comput 2006. 972–975. [5] Mingjun J, Huanwen T. Application of chaos in simulated annealing. Chaos Solitons Fractals 2004. 21: 933–941. [6] Gandomi AH, Yang XS, Talatahari S, Alavi AH. Firefly algorithm with chaos. Commun Nonlinear Sci 2012; 18(1):89-98 [7] Wang GG, Guo L, Gandomi AH, Hao GS, Wang H. Chaotic krill herd algorithm. Inform Sciences 2014; 274:17–34. [8] Tanyıldızı E, Cigal T. Kaotik Haritalı Balina Optimizasyon Algoritması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 2017; 29(1):309-319. [9] Saremi S, Mirjalili S, Lewis A. Biogeography-based optimisation with chaos. Neural Comput Appl 2014; 25:1077–1097. [10] Tanyıldızı E, Demir G. Golden Sine Algorithm: A Novel Math-Inspired Algorithm. Adv Electr Comput En 2017; 17: 71-78 [11] Mirjalili S, Gandomi AH. Chaotic gravitational constants for the gravitational search algorithm. Appl Soft Comput 2017. 53:407-419. [12] Yao X, Liu Y, Lin G. Evolutionary programming made faster. IEEE T Evolut Comput 1999; 3(2): 82-102.
Abstract
Random number sequences are frequently used for
stochastic structures of metaheuristic optimization algorithms. The
distributions of randomly generated sequences of numbers are quite effective in
the convergence of heuristic optimization algorithms to the global optimum. By
gathering randomly generated numbers in a certain area or by generating the
same values, the risk of the algorithm to be localized may be increased.
Therefore, the numbers produced are not the same, the spread spectrum has the
effect of the performance of heuristic algorithms. Chaotic maps are
discrete-time systems, and the numbers produced by chaotic maps have a broad
spectrum and are not periodic. Therefore, it is possible to avoid the optimum
points by using the chaotic map in heuristic algorithms or to reduce the risk
of local optimization. In this study, the effect of chaotic maps on the Golden
Sine Algorithm, which is one of the new optimization methods, has been
investigated.
References
- [1] Alatas B, Akin E, Ozer AB. Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals 2009. 40(4):1715-1734. [2] Alatas B. Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl 2010. 37(8): 5682-5687. [3] Jun-feng Y, Chi M, Xiao-qi P, Zhi-kun H, Jun H. A new optimization approach-chaosgenetic algorithm, Syst Eng 2001. 1:1-5. [4] Zhenyu G, Bo C, Min Y, Binggang C. Self-adaptive chaos differential evolution. Adv Nat Comput 2006. 972–975. [5] Mingjun J, Huanwen T. Application of chaos in simulated annealing. Chaos Solitons Fractals 2004. 21: 933–941. [6] Gandomi AH, Yang XS, Talatahari S, Alavi AH. Firefly algorithm with chaos. Commun Nonlinear Sci 2012; 18(1):89-98 [7] Wang GG, Guo L, Gandomi AH, Hao GS, Wang H. Chaotic krill herd algorithm. Inform Sciences 2014; 274:17–34. [8] Tanyıldızı E, Cigal T. Kaotik Haritalı Balina Optimizasyon Algoritması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 2017; 29(1):309-319. [9] Saremi S, Mirjalili S, Lewis A. Biogeography-based optimisation with chaos. Neural Comput Appl 2014; 25:1077–1097. [10] Tanyıldızı E, Demir G. Golden Sine Algorithm: A Novel Math-Inspired Algorithm. Adv Electr Comput En 2017; 17: 71-78 [11] Mirjalili S, Gandomi AH. Chaotic gravitational constants for the gravitational search algorithm. Appl Soft Comput 2017. 53:407-419. [12] Yao X, Liu Y, Lin G. Evolutionary programming made faster. IEEE T Evolut Comput 1999; 3(2): 82-102.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | MBD |
| Authors | |
| Publication Date | March 15, 2019 |
| Submission Date | July 6, 2018 |
| Published in Issue | Year 2019 |