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Son Üç Yılda Geliştirilen Metasezgisel Algoritmalar Hakkında Kısa Bir İnceleme

Yıl 2019, Özel Sayı 2019, 463 - 477, 31.10.2019
https://doi.org/10.31590/ejosat.638431

Öz

Optimizasyon
bir problemin olabilecek farklı çözümleri arasından en iyi sonucu verenin bulunmasıdır.
Optimizasyon problemlerinin çözümünde birçok algoritma kullanılmaktadır. Optimizasyon
algoritmaları genel olarak sezgisel optimizasyon algoritmaları ve matematiksel
optimizasyon algoritmaları olarak ikiye ayrılmaktadır. Matematiksel
optimizasyon algoritmaları, tüm çözüm kümesini tarayarak çözüme ulaşmayı
amaçlarken, sezgisel optimizasyon algoritmaları ise, çözüm kümesine sezgisel
olarak yaklaşmakta ve en iyi çözüme yada en iyiye yakın bir çözüme ulaşmayı
amaçlamaktadır. Çözüm kümesi geniş olan problemlerde matematiksel optimizasyon
algoritmalarının kullanımı maliyetlidir. Bu tip problemlerin çözümünde, sezgisel
optimizasyon algoritmaları daha avantajlı olup daha fazla tercih edilmektedir. Bir
optimizasyon algoritmasının her tür problem veya test fonksiyonu üzerinde
başarılı olması beklenemez. Bu nedenle hangi tür problemin hangi algoritma ile daha
iyi çözüldüğünün belirlenmesi gerekmektedir. Günümüzde temel sezgisel
metotların birleşerek etkili kullanımı sonucunda Metasezgisel isimli
algoritmalar geliştirilmiştir. Bu algoritmalar, yüksek seviyeli çalışma
ortamında, verimli arama işlemleri kullanarak çözüm uzayındaki optimum çözüme
daha hızlı şekilde ulaşmaktadır. Metasezgisel optimizasyon tekniklerinin kullanımının
yaygın olması nedeniyle, günümüzde birçok yeni metasezgisel optimizasyon algoritmaları
önerilmektedir. Önerilen bu algoritmalar üzerinde geliştirmeler ve hibrit
çalışmalar da yapılmaktadır. Bu çalışmada, literatürde son üç yılda (2017-2019)
önerilmiş olan, Harris Hawks Optimizasyon Algoritması (HHO), Satin Bowerbird Optimizasyon
Algoritması (SBO), Optimal Foraging Algoritması (OFA), Butterfly Optimizasyon Algoritması
(BOA), Pity Beetle Algoritması (PBA) ve Collective Decision Optimizasyon Algoritması
(CDOA) ele alınmıştır. Geliştirilen bu yeni optimizasyon algoritmalarının
esinlendikleri alan, çalışma mantıkları ve arama stratejileri incelenerek sunulmuştur.
Gerçekleştirilen bu derlemenin metasezgizel optimizasyon problemleri alanında yapılacak
olan çalışmalara ışık tutacağı düşünülmektedir.

Kaynakça

  • A Saha, A. B. (2018). CDO - A New Metaheuristic Algorithm Towards the Solution of Transient Stability Constrained Optimal Power Flow. International IEEE.
  • Arora S, S. S. (2018). Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm. COMPUTER ENGINEERING AND COMPUTER SCIENCE.
  • Asghar Heidari, S. M. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 849–872.
  • Bednarz, J. (1988). Cooperative Hunting Harris' Hawks. American Association for the Advancement of Science., 1525-1527.
  • Blair RB, L. A. (1997). Butterfly Diversity And Human Land Use:Species Assemblages Along An Urban Grandient. Biol Conserv, 113–125.
  • C Tian, Y. H. (2018). A Novel Wind Speed Forecasting System Based On Hybrid Data Preprocessing And Multi-Objective Optimization. Applied energy.
  • Coleman SW, P. G. (2004). Variable Female Preferences Drive Complex Male Displays. Research Gate.
  • Çelik, Y. (2013). Optimizasyon Problemlerinde Bal Arılarının Evlilik Optimizasyonu Algoritmasının Performansının Geliştirilmesi. Doktora Tezi. Konya, Türkiye: Selçuk Üniversitesi Fen Bilimleri Enstitüsü.
  • D Karaboga, B. G. (2019). Solving Traveling Salesman Problem by Using Combinatorial Artificial Bee Colony Algorithms. International Journal on Artificial Intelligence Tools.
  • Fatma Hashim, E. H. (2019). Henry Gas Solubility Optimization: A Novel Physics-Based Algorithm. Future Generation Computer Systems, 646–667.
  • G Pyke, H. P. (1977). A Selective Review Of Theory And Tests. Quarterly Review of Biology , 137-154.
  • GI Sayed, M. S. (2018). Modified Optimal Foraging Algorithm For Parameters Optimization Of Support Vector Machine. International Conference on Advanced .
  • Guang-Yu Zhu, W.-B. Z. (2017). Optimal Foraging Algorithm For Global Optimization. Applied Soft Computing, 294–313.
  • Hart, W. E. (1994). Adaptive Global Optimization With Local Search. A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science & Engineering. San Diego: University of California, San Diego.
  • Heming Jia, C. L. (2019). Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation. Remote sensing .
  • Holland, J. (1992). Genetic Algorithms. Scientific American, 66-72.
  • J Chintam, M. D. (2018). Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm. Energies.
  • J Digalakis, K. M. (2000). On Benchmarking Functions For Genetic Algorithms. International Journal of Computer Mathematics, 1-27.
  • Jagadeeswar Chintam, M. D. (2017). Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm. Energies.
  • K Senthilkumar, R. R. (2019). Optimized Scheduling Of Multicore ECU Architecture With Bio-Security CAN network using AUTOSAR. Future Generation Computer Systems.
  • L. Lefebvre, P. W. (1997). Feeding İnnovations And Forebrain Size İn Birds. Animal Behaviour, 549-560.
  • Laporte, G. (2006). Classical And Modern Heuristics For The Vehicle Routing Problem. International transactions in operation research , 285-300.
  • M Dabbaghjamanesh, A. K.-F. (2018). Effective Scheduling of Reconfigurable Microgrids With Dynamic Thermal Line Rating. IEEE Transactions.
  • M Dorigo, T. S. (2019). Ant Colony Optimization: Overview And Recent Advances. Springer.
  • M Tolba, H. R. (2018). Impact of Optimal Allocation of Renewable Distributed Generation in Radial Distribution Systems Based on Different Optimization Algorithms. Energies.
  • NA Golilarz, H. G. (2019). Satellite Image De-Noising With Harris Hawks Meta Heuristic Optimization Algorithm and Improved Adaptive Generalized Gaussian Distribution Threshold Function. IEEE Access.
  • Nahavandi, A. (2006 ). The Art and Science of Leadership. Prentice Hall.
  • Nikos Kallioras, N. L. (2018). A New Metaheuristic İnspired By Bark Beetles For Solving Engineering Problems. Advances in Engineering Software .
  • P Du, J. W. (2019). A Novel Hybrid Model Based On Multi-Objective Harris Hawks Optimization Algorithm For Daily PM2. 5 And PM10 Forecasting. arXiv.org.
  • P Gupta, A. S. (2019). Clustering-Based Optimized HEED Protocols For Wsns Using Bacterial Foraging Optimization And Fuzzy Logic System. Springer.
  • Q Zhang, R. W. (2018). Modified Collective Decision Optimization Algorithm With Application İn Trajectory Planning Of UAV. Applied Intelligence.
  • Qingyang Zhang, R. W. (2017). COLLECTİVE DECİSİON OPTİMİZATİON ALGORİTHM: A NEW HEURİSTİC OPTİMİZATİON METHOD. Neurocomputing, 123–137.
  • S Arora, P. A. (2019). Binary Butterfly Optimization Approaches For Feature Selection. Expert Systems with Applications.
  • S Arora, S. S. (2017). An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization. International Journal of Interactive Multimedia.
  • S Arora, S. S. (2018). A Modified Butterfly Optimization Algorithm For Mechanical Design Optimization Problems. Journal of the Brazilian Society of Mechanical Sciences and Engineering.
  • S Shadravan, H. N. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial.
  • Sankalap Arora, S. S. (2019). Butterfly Optimization Algorithm: A Novel Approach For Global Optimization. Soft Computing, 715–734.
  • Seyyed Moosavi, V. B. (2017). Satin Bowerbird Optimizer: A New Optimization Algorithm To Optimize ANFIS For Software Development Effort Estimation. Engineering Applications of Artificial Intelligence, 1-15.
  • SK Chandrinos, N. L. (2018). Construction Of Currency Portfolios By Means Of An Optimized İnvestment Strategy. Operations Research Perspectives.
  • Spyros Chandrinos, N. L. (2018). Construction of Currency Portfolios by means of an Optimized Investment Strategy. Operations Research Perspectives, 32–44.
  • WB Zhang, G. Z. (2017). Drilling Path Optimization By Optimal Foraging Algorithm. IEEE Transactions on Industrial.
  • wikipedia. (2019, Temmuz). wikipedia. June 20, 2019 tarihinde wikipedia: http://en.wikipedia.org adresinden alındı
  • X Bao, H. J. (2019). A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation. IEEE Access.
  • X Xu, Z. H. (2018). Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem. Complexity.
  • Yang, X.-S. (2010). Nature-inspired Metaheuristic Algorithms. United Kindom: Luniver Press.
  • Zahra Beheshti, S. M. (2013). A Review of Population-based Meta-Heuristic Algorithm. International journal of advances in soft computing and its applications.

A Brief Review of Metaheuristic Algorithms Improved in the Last Three Years

Yıl 2019, Özel Sayı 2019, 463 - 477, 31.10.2019
https://doi.org/10.31590/ejosat.638431

Öz

Optimization is to find the best solution among the different solutions of a problem. Many algorithms are used to solve optimization problems. Optimization algorithms are generally divided into heuristic optimization algorithms and mathematical optimization algorithms. While mathematical optimization algorithms aim to reach the solution by scanning the whole solution set, heuristic optimization algorithms approach the solution set intuitively and aim to reach the best solution or the best solution. Mathematical optimization algorithms are costly to solve problems with a large set of solutions. In solving such problems, heuristic optimization algorithms are more advantageous and more preferred. An optimization algorithm cannot be expected to succeed on any problem or test function. Therefore, it is necessary to determine which kind of problem is best solved by which algorithm. Nowadays, as a result of the effective use of basic heuristic methods combined, Metaheuristic algorithms have been developed. These algorithms reach the optimum solution in the solution space faster by using efficient search operations in a high level working environment. Due to the widespread use of metaheuristic optimization techniques, many new metaheuristic optimization algorithms have been proposed today. Improved and hybrid studies are also performed on these proposed algorithms. In this study, Harris Hawks Optimization Algorithm (HHO), Satin Bowerbird Optimization Algorithm (SBO), Optimal Foraging Algorithm (OFA), Butterfly Optimization Algorithm (BOA) and Pity Beetle Algorithm (PBA) and Collective Decision Optimization Algorithm (CDOA) have been proposed in the literature in the last three years (2017-2019). The field of inspiration of these new optimization algorithms, study logic and search strategies are presented. It is thought that this review will shed light on the studies to be conducted in the field of metaheuristic optimization problems.

Kaynakça

  • A Saha, A. B. (2018). CDO - A New Metaheuristic Algorithm Towards the Solution of Transient Stability Constrained Optimal Power Flow. International IEEE.
  • Arora S, S. S. (2018). Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm. COMPUTER ENGINEERING AND COMPUTER SCIENCE.
  • Asghar Heidari, S. M. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 849–872.
  • Bednarz, J. (1988). Cooperative Hunting Harris' Hawks. American Association for the Advancement of Science., 1525-1527.
  • Blair RB, L. A. (1997). Butterfly Diversity And Human Land Use:Species Assemblages Along An Urban Grandient. Biol Conserv, 113–125.
  • C Tian, Y. H. (2018). A Novel Wind Speed Forecasting System Based On Hybrid Data Preprocessing And Multi-Objective Optimization. Applied energy.
  • Coleman SW, P. G. (2004). Variable Female Preferences Drive Complex Male Displays. Research Gate.
  • Çelik, Y. (2013). Optimizasyon Problemlerinde Bal Arılarının Evlilik Optimizasyonu Algoritmasının Performansının Geliştirilmesi. Doktora Tezi. Konya, Türkiye: Selçuk Üniversitesi Fen Bilimleri Enstitüsü.
  • D Karaboga, B. G. (2019). Solving Traveling Salesman Problem by Using Combinatorial Artificial Bee Colony Algorithms. International Journal on Artificial Intelligence Tools.
  • Fatma Hashim, E. H. (2019). Henry Gas Solubility Optimization: A Novel Physics-Based Algorithm. Future Generation Computer Systems, 646–667.
  • G Pyke, H. P. (1977). A Selective Review Of Theory And Tests. Quarterly Review of Biology , 137-154.
  • GI Sayed, M. S. (2018). Modified Optimal Foraging Algorithm For Parameters Optimization Of Support Vector Machine. International Conference on Advanced .
  • Guang-Yu Zhu, W.-B. Z. (2017). Optimal Foraging Algorithm For Global Optimization. Applied Soft Computing, 294–313.
  • Hart, W. E. (1994). Adaptive Global Optimization With Local Search. A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science & Engineering. San Diego: University of California, San Diego.
  • Heming Jia, C. L. (2019). Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation. Remote sensing .
  • Holland, J. (1992). Genetic Algorithms. Scientific American, 66-72.
  • J Chintam, M. D. (2018). Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm. Energies.
  • J Digalakis, K. M. (2000). On Benchmarking Functions For Genetic Algorithms. International Journal of Computer Mathematics, 1-27.
  • Jagadeeswar Chintam, M. D. (2017). Real-Power Rescheduling of Generators for Congestion Management Using a Novel Satin Bowerbird Optimization Algorithm. Energies.
  • K Senthilkumar, R. R. (2019). Optimized Scheduling Of Multicore ECU Architecture With Bio-Security CAN network using AUTOSAR. Future Generation Computer Systems.
  • L. Lefebvre, P. W. (1997). Feeding İnnovations And Forebrain Size İn Birds. Animal Behaviour, 549-560.
  • Laporte, G. (2006). Classical And Modern Heuristics For The Vehicle Routing Problem. International transactions in operation research , 285-300.
  • M Dabbaghjamanesh, A. K.-F. (2018). Effective Scheduling of Reconfigurable Microgrids With Dynamic Thermal Line Rating. IEEE Transactions.
  • M Dorigo, T. S. (2019). Ant Colony Optimization: Overview And Recent Advances. Springer.
  • M Tolba, H. R. (2018). Impact of Optimal Allocation of Renewable Distributed Generation in Radial Distribution Systems Based on Different Optimization Algorithms. Energies.
  • NA Golilarz, H. G. (2019). Satellite Image De-Noising With Harris Hawks Meta Heuristic Optimization Algorithm and Improved Adaptive Generalized Gaussian Distribution Threshold Function. IEEE Access.
  • Nahavandi, A. (2006 ). The Art and Science of Leadership. Prentice Hall.
  • Nikos Kallioras, N. L. (2018). A New Metaheuristic İnspired By Bark Beetles For Solving Engineering Problems. Advances in Engineering Software .
  • P Du, J. W. (2019). A Novel Hybrid Model Based On Multi-Objective Harris Hawks Optimization Algorithm For Daily PM2. 5 And PM10 Forecasting. arXiv.org.
  • P Gupta, A. S. (2019). Clustering-Based Optimized HEED Protocols For Wsns Using Bacterial Foraging Optimization And Fuzzy Logic System. Springer.
  • Q Zhang, R. W. (2018). Modified Collective Decision Optimization Algorithm With Application İn Trajectory Planning Of UAV. Applied Intelligence.
  • Qingyang Zhang, R. W. (2017). COLLECTİVE DECİSİON OPTİMİZATİON ALGORİTHM: A NEW HEURİSTİC OPTİMİZATİON METHOD. Neurocomputing, 123–137.
  • S Arora, P. A. (2019). Binary Butterfly Optimization Approaches For Feature Selection. Expert Systems with Applications.
  • S Arora, S. S. (2017). An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization. International Journal of Interactive Multimedia.
  • S Arora, S. S. (2018). A Modified Butterfly Optimization Algorithm For Mechanical Design Optimization Problems. Journal of the Brazilian Society of Mechanical Sciences and Engineering.
  • S Shadravan, H. N. (2019). The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Engineering Applications of Artificial.
  • Sankalap Arora, S. S. (2019). Butterfly Optimization Algorithm: A Novel Approach For Global Optimization. Soft Computing, 715–734.
  • Seyyed Moosavi, V. B. (2017). Satin Bowerbird Optimizer: A New Optimization Algorithm To Optimize ANFIS For Software Development Effort Estimation. Engineering Applications of Artificial Intelligence, 1-15.
  • SK Chandrinos, N. L. (2018). Construction Of Currency Portfolios By Means Of An Optimized İnvestment Strategy. Operations Research Perspectives.
  • Spyros Chandrinos, N. L. (2018). Construction of Currency Portfolios by means of an Optimized Investment Strategy. Operations Research Perspectives, 32–44.
  • WB Zhang, G. Z. (2017). Drilling Path Optimization By Optimal Foraging Algorithm. IEEE Transactions on Industrial.
  • wikipedia. (2019, Temmuz). wikipedia. June 20, 2019 tarihinde wikipedia: http://en.wikipedia.org adresinden alındı
  • X Bao, H. J. (2019). A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation. IEEE Access.
  • X Xu, Z. H. (2018). Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem. Complexity.
  • Yang, X.-S. (2010). Nature-inspired Metaheuristic Algorithms. United Kindom: Luniver Press.
  • Zahra Beheshti, S. M. (2013). A Review of Population-based Meta-Heuristic Algorithm. International journal of advances in soft computing and its applications.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Yüksel Çelik Bu kişi benim 0000-0002-7117-9736

İlker Yıldız Bu kişi benim 0000-0002-1575-2673

Alper Talha Karadeniz Bu kişi benim 0000-0003-4165-3932

Yayımlanma Tarihi 31 Ekim 2019
Yayımlandığı Sayı Yıl 2019 Özel Sayı 2019

Kaynak Göster

APA Çelik, Y., Yıldız, İ., & Karadeniz, A. T. (2019). Son Üç Yılda Geliştirilen Metasezgisel Algoritmalar Hakkında Kısa Bir İnceleme. Avrupa Bilim Ve Teknoloji Dergisi463-477. https://doi.org/10.31590/ejosat.638431

Cited By