Title: CONTOPT-JS: Metaheuristic Algorithms based JavaScript Software Library for Continuous Optimization Problems

Abstract: In this study, a software library called CONTOPT-JS has been developed for solving continuous optimization problems. By using this JavaScript language based library, fully client-side web applications can be developed. In the library, Artificial Bee Colony, Differential Evolution, Particle Swarm Optimization and Evolution Strategies metaheuristics exist and new algorithms and new problems can be added because of its modular design. Using the CONTOPT-JS library, experimental works have been conducted on some standard optimization benchmark functions and Sensor Deployment application area and the obtained results have been presented.

Keywords: continuous optimization; metaheuristic algorithms; software library; javascript; sensor deployment problemContinuous optimization; metaheuristic algorithms; software library; Javascript; sensor deployment problem

Başlık: CONTOPT-JS: Sürekli Eniyileme Problemleri için Metasezgisel Algoritmalar tabanlı bir JavaScript Yazılım Kütüphanesi

Özet: Bu çalışmada sürekli eniyileme problemlerinin çözümü için CONTOPT-JS isimli bir yazılım kütüphanesi tasarlanarak gerçekleştirimi yapılmıştır. JavaScript dili tabanlı bu kütüphane ile tamamen istemci taraflı çalışabilen web uygulamaları geliştirilebilmektedir. Kütüphanede Yapay Arı Kolonisi, Diferansiyel Gelişim, Parçacık Sürü Eniyilemesi ve Evrim Stratejileri metasezgiselleri yer almakla birlikte modüler bir yapıya sahip olduğundan dolayı yeni algoritma ve problemlerin de eklenebilmesine olanak tanımaktadır. CONTOPT-JS kütüphanesi ile, bazı standart eniyileme test fonksiyonları üzerinde ve ayrıca Sensör Yerleştirme problemi uygulama alanında deneysel çalışmalar yapılarak elde edilen sonuçlar sunulmuştur.

Anahtar kelimeler: Sürekli eniyileme; metasezgisel algoritmalar; yazılım kütüphanesi; Javascript; sensör yerleştirme problemi

• Holland JH. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Michigan Press, Oxford, England, 1975.
• Glover F. Tabu search-part I. ORSA Journal on Computing 1989; 1(3): 190-206.
• Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science 1983; 220(4598): 671-680.
• Dorigo M. Optimization, Learning and Natural Algorithms. PhD Thesis, Politecnico di Milano, Italy, 1992.
• Kennedy J, Eberhart R. Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks, November 27-December 1, 1995, Perth, WA, Australia, pp. 1942-1948.
• Boussaid I, Lepagnot J, Siarry P. A survey on optimization metaheuristics. Information Sciences 2013; 237: 82-117.
• Ugur A. Path planning on a cuboid using genetic algorithms. Information Sciences 2008; 178(16): 3275-3287.
• Eberhart R, Shi Y. Particle swarm optimization: Developments, applications and resources. In Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), May 27-30, 2001, Seoul, South Korea, pp. 81-86.
• Das S, Suganthan PN. Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical Report, 2010.
• Dyer DW. The Watchmaker Framework for Evolutionary Computation. Retrieved from http://watchmaker.uncommons.org/index.php on June 28, 2017.
• Lukasiewycz M, Glab M, Reimann F, Teich J. Opt4J: A modular framework for meta-heuristic optimization. In GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation, July 2011, pp. 1723-1730.
• De Beukelaer H, Davenport GF, De Meyer G, Fack V. JAMES: An object-oriented Java framework for discrete optimization using local search metaheuristics. Journal of Software: Practice and Experience 2017; 47(6): 921-938.
• Pohlheim H. GEATbx-Genetic and evolutionary algorithms toolbox in Matlab. Retrieved from http://www.geatbx.com/ at June 28, 2017.
• Galisteo JC. MetaHeuristics ToolBox for MATLAB. Retrieved from http://neo.lcc.uma.es/software/mhtb/ at June 28, 2017.
• Cahon S, Melab N, Talbi EG. Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 2004; 10(3): 357-380.
• Keijzer M, Merelo J, Romero G, Schoenauer M Evolving objects: A general purpose evolutionary computation library. In Artificial Evolution, 2002, pp. 829-888.
• Wagner S, Affenzeller M. HeuristicLab: A generic and extensible optimization environment. Adaptive and Natural Computing Algorithms 2005; 538-541.
• GitHub Repository. js-metaheuristics: Metaheuristic algorithms for JavaScript. Retrieved from https://github.com/aureooms/jsmetaheuristics at June 28, 2017.
• GitHub Repository. genetic-js: Advanced genetic and evolutionary algorithm library written in Javascript. Retrieved from https://github.com/subprotocol/genetic-js at June 28, 2017.
• Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Kayseri, Turkey, 2005.
• Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 1997; 11(4): 341-359.
• Rechenberg I. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Publisher, Stuttgart-Bad Cannstatt, German edition, 1973.
• Engelschall RS. ECMAScript 6: New Features: Overview and Comparison. Retrieved from http://es6-features.org/#Constants at June 28, 2017.