Year 2021,
Volume: 16 Issue: 1, 145 - 153, 15.03.2021
Eyüp Eröz
,
Erkan Tanyıldızı
References
- Mirjalili S, Lewis A. The Whale Optimization Algorithm, ADV ENG SOFTW 2016; 95: 51-67
- Tanyıldızı E, Cigal T. Kaotik Haritalı Balina Optimizasyon Algoritmaları, FÜMD 2017; 29(1): 307 – 317.
- Tanyıldızı E, Demir G. Nümerik Optimizasyon için Kaotik Altın Sinüs Algoritması, FUMBD 2019; 31(1): 91 – 97.
- S. M. P. J. S. S., “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems,” Appl Intell, DOI 10.1007/s10489-016-0825-8, 2016.
- Wei G. Shoubin W. Chaos Ant Colony Optimization and Application. Fourth International Conference on Internet Computing for Science and Engineering;2009; Harbin, China.
- Ayan K. and Kilic U. Solution of multi-objective optimal power flow with chaotic artificial bee colony algorithm, INT REV ELECTR ENG-I; 2011; England, 6(3):1365–1371.
- Ying S, Yuelin G,Xudong S. Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity. Mathematics 2019, 7, 146;
- Dunia S. Ramzy A. A Chaotic Crow Search Algorithm for High-Dimensional Optimization Problems. Basrah Journal for Engineering Sciences January 2018; 17(1):15-25
- Danqing G, Junping W, Jun H. Renmin H and Maoqiang S. Chaotic-NSGA-II: An effective algorithm to solve multi-objective optimization problems. ICISS; 2010; Guilin, China.
- Zhang H. Zhou J. Zhang Y. Fang N. and Zhang R. Short term hydrothermal scheduling using multi-objective differential evolution with three chaotic sequences, INT J ELEC POWER; 2013;England; 47: 85–99.
- Pei Y. and Hao J, "Non-dominated sorting and crowding distance based multi-objective chaotic evolution", ICSI 2017; Japan, pp. 15-22,
- Tanyıldızı E, Demir G. Golden Sine Algorithm: A Novel Math-Inspired Algorithm, ADV ELECTR COMPUT EN 2017.
- Eröz E. Yeni Çok Amaçlı Optimizasyon Algoritması: MOGoldSA, Fırat Üniversitesi Teknoloji Fakültesi Yazılım Mühendisliği Yüksek Lisans Tezi, 2020
- Mirjalili S, Gandomi AH. Chaotic gravitational constants for the gravitational search algorithm. APPL SOFT COMPUT 2017. 53: 407-419.
- Zawbaa HM, Emary E, Grosan C (2016) Feature Selection via Chaotic Antlion Optimization. PLoS ONE 11(3): e0150652. doi:10.1371/journal. pone.0150652
- P. J. S. S. Mirjalili, “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems,” Springer Science+Business Media, 10.1007/s10489-016-0825-8, 2016.
- Vohra R, Patel B. An Efficient Chaos-Based Optimization Algorithm Approach for Cryptography. Communication Network Security. 2012;1(4):75–79.
- Ren B, Zhong W. Multi-objective optimization using chaos based PSO. Information Technology. 2011;10(10):1908–1916.
- Eröz, E., Tanyıldızı, E., (2020). Kaotik Haritalı Çok Amaçlı Altın Sinüs Algoritmasının Performans Analizi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2),391-402.
- Deb K. Multi-objective optimization using evolutionary algorithms. New York: John Wiley&Sons, 2001.
- Van Veldhuizen, D. A. and Lamont, G. B. Multiobjective evolutionary algorithm research: A history and analysis. Technical Report TR-98-03, Department of Electrical and
Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, WrightPatterson AFB, Ohio, 1998
-Performance of Chaotic Mapping Multi-Objective Optimization Algorithms
Year 2021,
Volume: 16 Issue: 1, 145 - 153, 15.03.2021
Eyüp Eröz
,
Erkan Tanyıldızı
Abstract
Multi-objective optimization is defined as the process of producing suitable solutions to problems with multiple objectives. The randomly generated string of numbers is of great importance in achieving solutions close to the global optimum in intuitive multi- objective optimization. Collecting the randomly generated string of numbers in a certain area increases the risk of moving away from the global optimum. Chaotic maps are used to reduce this risk it is not periodic as the variety of numbers produced in chaotic maps is high. For this reason, chaotic maps are used in the random number generation part of optimization algorithms. Chaos-based algorithms have become an important field of study because they are flexible and can escape from local minimums. In this study, the effects of chaotic maps on the new and successful Multipurpose Gold Sine Algorithm (MOGoldSA) were compared with the Multi-Objectıve Ant Lion Optimization (MOALO) algorithm.
References
- Mirjalili S, Lewis A. The Whale Optimization Algorithm, ADV ENG SOFTW 2016; 95: 51-67
- Tanyıldızı E, Cigal T. Kaotik Haritalı Balina Optimizasyon Algoritmaları, FÜMD 2017; 29(1): 307 – 317.
- Tanyıldızı E, Demir G. Nümerik Optimizasyon için Kaotik Altın Sinüs Algoritması, FUMBD 2019; 31(1): 91 – 97.
- S. M. P. J. S. S., “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems,” Appl Intell, DOI 10.1007/s10489-016-0825-8, 2016.
- Wei G. Shoubin W. Chaos Ant Colony Optimization and Application. Fourth International Conference on Internet Computing for Science and Engineering;2009; Harbin, China.
- Ayan K. and Kilic U. Solution of multi-objective optimal power flow with chaotic artificial bee colony algorithm, INT REV ELECTR ENG-I; 2011; England, 6(3):1365–1371.
- Ying S, Yuelin G,Xudong S. Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity. Mathematics 2019, 7, 146;
- Dunia S. Ramzy A. A Chaotic Crow Search Algorithm for High-Dimensional Optimization Problems. Basrah Journal for Engineering Sciences January 2018; 17(1):15-25
- Danqing G, Junping W, Jun H. Renmin H and Maoqiang S. Chaotic-NSGA-II: An effective algorithm to solve multi-objective optimization problems. ICISS; 2010; Guilin, China.
- Zhang H. Zhou J. Zhang Y. Fang N. and Zhang R. Short term hydrothermal scheduling using multi-objective differential evolution with three chaotic sequences, INT J ELEC POWER; 2013;England; 47: 85–99.
- Pei Y. and Hao J, "Non-dominated sorting and crowding distance based multi-objective chaotic evolution", ICSI 2017; Japan, pp. 15-22,
- Tanyıldızı E, Demir G. Golden Sine Algorithm: A Novel Math-Inspired Algorithm, ADV ELECTR COMPUT EN 2017.
- Eröz E. Yeni Çok Amaçlı Optimizasyon Algoritması: MOGoldSA, Fırat Üniversitesi Teknoloji Fakültesi Yazılım Mühendisliği Yüksek Lisans Tezi, 2020
- Mirjalili S, Gandomi AH. Chaotic gravitational constants for the gravitational search algorithm. APPL SOFT COMPUT 2017. 53: 407-419.
- Zawbaa HM, Emary E, Grosan C (2016) Feature Selection via Chaotic Antlion Optimization. PLoS ONE 11(3): e0150652. doi:10.1371/journal. pone.0150652
- P. J. S. S. Mirjalili, “Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems,” Springer Science+Business Media, 10.1007/s10489-016-0825-8, 2016.
- Vohra R, Patel B. An Efficient Chaos-Based Optimization Algorithm Approach for Cryptography. Communication Network Security. 2012;1(4):75–79.
- Ren B, Zhong W. Multi-objective optimization using chaos based PSO. Information Technology. 2011;10(10):1908–1916.
- Eröz, E., Tanyıldızı, E., (2020). Kaotik Haritalı Çok Amaçlı Altın Sinüs Algoritmasının Performans Analizi. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(2),391-402.
- Deb K. Multi-objective optimization using evolutionary algorithms. New York: John Wiley&Sons, 2001.
- Van Veldhuizen, D. A. and Lamont, G. B. Multiobjective evolutionary algorithm research: A history and analysis. Technical Report TR-98-03, Department of Electrical and
Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, WrightPatterson AFB, Ohio, 1998