A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques
Keywords:Optimizations Techniques (OT), Artificial Intelligence, Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FFA), Fitness functions.
From few decadesâ€™ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle Swarm Optimization (PSO) is presented and also the most suitable fitness functions and its numerical expressions have discussed.
 Jung-Min Yang, Jong-Hwan Kim, â€œSliding Mode Control for Trajectory Tracking of Nonholonomic Wheeled Mobile Robotsâ€, IEEE Transactions on Robotics and Automation, Vol. 15, No. 3, June 1999, 578-587.
 Yogeswaran Mohan, S. G. Ponnambalam, â€œAn Extensive Review of Research in Swarm Roboticsâ€, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), 140â€“145.
 ManueleBrambilla, Eliseo Ferrante, Mauro Birattari, Marco Dorigo. Swarm Intell Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, Springer, 2013, 7 (1), 1-41. <10.1007/s11721-012-0075-2>. <hal-01405919>
 M. R. AlRashidi, M. E. El-Hawary, â€œA Survey of Particle Swarm Optimization Applications in Electric Power Systemsâ€, IEEE Transactions on Evolutionary Computation, Sep. 2009, DOI: 10.1109/TEVC.2006.880326.
 Kwang Y Lee, Mohamed A El-Sharkawi, â€œModern Heuristic Optimization Techniques: Theory and Applications to Power Systemsâ€, IEEE Press, Wiley Interscience, A John Wiley & Sons Inc. Publication, 2008, ISBN 978-0471-45711-4.
 Ahmed M Hasan, KhairulmizamSamsudin, Abd Rahman Ramli, Raja SyamsulAzmir, and Salam A Ismaeel, â€œA Review of NavigationSystems (Integrationand Algorithms)â€, Australian Journal of Basic and Applied Sciences, 3(2), 943-959, Jan 2009.
 Oren La'adan, Amnon Barak, â€œInter Process Communication Optimization In A Scalable Computing Clusterâ€, Annual Review of Scalable Computing. September 2000, 121-173.
 Minoru Mukuda, Yasuhiro Tsujimura, â€œA Multiobjective Genetic Algorithm For Solving Reliability Optimization Problem Of A Communication Network Systemâ€, Advanced Reliability Modeling II. July 2006, 166-175.
 M. Egmont-Petersena, D. de Ridder b, H. Handels, â€œImage processing with neural networksâ€”a reviewâ€, Pattern Recognition Society, Elsevier, 2002, 35 (10), 2279-2301, DOI: 10.1016/s0031-3203(01)00178-9.
 Mahamed G. Omran, Andries P. Engelbrecht, Ayed Salman, â€œImage Classification Using Particle Swarm Optimizationâ€, Recent Advances in Simulated Evolution and Learning. August 2004, 347-365.
 A. Ghosh and A. Gorai, â€œHue-Preserving Color Image Enhancement Using Particle Swarm Optimizationâ€, Integration of Swarm Intelligence and Artificial Neural Network. June 2011, 157-178.
 JoÃ£o Manuel, R. S. Tavares,LuÃsa Ferreira Bastos, â€œImprovement of Modal Matching Image Objects in Dynamic Pedobarography Using Optimization Techniquesâ€, Progress in Computer Vision and Image Analysis. August 2009, 339-368.
 HUA-MEI CHEN and PRAMOD K. VARSHNEY, â€œTechniques for Mutual Information-Based Brain Image Registration and Their Applicationsâ€, Medical Imaging Systems Technology. August 2005, 325-350.
 A.Colorni, M.Dorigo, V.Maniezzo, "Distributed Optimization by Ant Colonies," Proceedings of the First European Conference on Artificial Life, Paris, France, F.Varela and P.Bourgine (Eds.), Elsevier Publishing, 134â€“142, 1991.
 A.Colorni, M.Dorigo, V.Maniezzo, "An Investigation of some Properties of an Ant Algorithm," Proceedings of the Parallel Problem Solving from Nature Conference (PPSN 92), Brussels, Belgium, R.MÃ¤nner and B.Manderick (Eds.), Elsevier Publishing, 509-520, 1992.
 Dorigo M, Optimization, â€œlearning and natural algorithmsâ€ PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992 [in Italian].
 Dorigo M, Gambardella LM, â€œAnt colony system: A cooperative learning approach to the traveling salesman problemâ€, IEEE Transactions on Evolutionary Computing, 1997, 1(1), 53â€“66.
 Dorigo M, StÃ¼tzle T, â€œAnt Colony optimization. Cambridge, MA: MIT Press; 2004.
 Bullnheimer B, Hartl R, Strauss C, â€œA new rank-based version of the Ant System: A computational studyâ€, Central European Journal of Operations Research, 1999;7(1):25â€“38.
 StÃ¼tzle T, Hoos HH, â€œMAXâ€“MIN Ant systemâ€, Future Generation Computer Systems 2000, 16(8), 889â€“914.
 Blum C, Dorigo M, â€œThe hyper-cube framework for ant colony optimizationâ€, IEEE Transactions on System Management Cybernetics Part B 2004, 34(2), 1161â€“72.
 Gambardella LM, Taillard Ã‰D, Agazzi G, â€œMACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windowsâ€, â€œNew ideas in optimizationâ€, London: McGraw-Hill; 1999. p. 63â€“76.
 Reimann M, Doerner K, Hartl RF, â€œD-ants: Savings based ants divide and conquer the vehicle routing problemsâ€, Computers & Operations Research, 2004, 31(4), 563â€“91.
 Kennedy, J. and Eberhart, R. C, â€œParticle swarm optimizationâ€, Proceedings IEEE International conference on neural networks Vol. 4, pp. 1942-1948. IEEE service center, Piscataway, NJ, 1995.
 Eberhart, R. C. and Kennedy J, â€œA new optimizer using particle swarm theoryâ€, Proceedings of the sixth international symposium on micro machine and human science pp. 39-43. IEEE service center, Piscataway, NJ, Nagoya, Japan, 1995.
 Eberhart, R. C. and Shi, â€œParticle swarm optimization: developments, applications and resourcesâ€, Proc. congress on evolutionary computation 2001 IEEE service center, Piscataway, NJ., Seoul, Korea., 2001.
 Y amilledel V alle, Ganesh Kumar Venayagamoorthy, Salman Mohagheghi, Jean-Carlos Hernandez, Ronald G. Harley, â€œParticle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systemsâ€, IEEE Transactions on Evolutionary Computation, 12(2), April 2008.
 D. Karaboga, â€œAn idea based on honey bee swarm for numerical optimizationâ€, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
 Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, and Shu-Chuan Chu, â€œEnhanced Artificial Bee Colony Optimizationâ€, International Journal of Innovative Computing, Information and Control Volume 5, Issue 12, December 2009.
 Karaboga, Dervis, and BahriyeAkay, "A modified artificial bee colony (ABC) algorithm for constrained optimization problems." Applied soft computing, 11(3), April 2011, 3021-3031.
 Karaboga, D., Gorkemli, B., Ozturk, C., &Karaboga, N. (2014). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57.
 Xin-She Yang, "Firefly algorithms for multimodal optimization", International symposium on stochastic algorithms. Springer, Berlin, Heidelberg, Oct 2009, 168-178.
 S. Palit, S. Sinha, M. Molla, A. Khanra, M. Kule, A cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm, in: Computer and Communication Technology (ICCCT), 2011 2nd International Conference on, IEEE, 2011, pp. 428â€“432.
 R. Falcon, M. Almeida, A. Nayak, Fault identification with binary adaptive fireflies in parallel and distributed systems, in: Evolutionary Computation (CEC), 2011 IEEE Congress on, IEEE, 2011, pp. 1359â€“1366.
 S. M. Farahani, A. Abshouri, B. Nasiri, M. Meybodi, A gaussian firefly algorithm, International Journal of Machine Learning and Computing 1 (5) (2011) 448â€“454.
 X. S. Yang, Metaheuristic optimization: algorithm analysis and open problems, in: P. Pardalos, S. Rebennack (Eds.), Experimental Algorithms, Lecture notes in computer science, Vol. 6630, Springer Verlag, Berlin, 2011, pp. 21â€“32.
 L. dos Santos Coelho, D. L. de Andrade Bernert, V. C. Mariani, A chaotic firefly algorithm applied to reliability-redundancy optimization, in: Evolutionary Computation (CEC), 2011 IEEE Congress on, Vol. 18, IEEE, 2013, pp. 89â€“98.
 A. Gandomi, X.-S. Yang, S. Talatahari, A. Alavi, Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation 18 (1) (2013) 89 â€“ 98. doi:10.1016/j.cnsns.2012.06.009.
 M. Subutic, M. Tuba, N. Stanarevic, Parallelization of the firefly algorithm for unconstrained optimization problems, in: Latest Advances in Information Science and Applications, 2012, pp. 264â€“269.
 A. Husselmann, K. Hawick, Parallel parametric optimisation with firefly algorithms on graphical processing units, Technical Report CSTN-141.
 T. Apostolopoulos, A. Vlachos, Application of the firefly algorithm for solving the economic emissions load dispatch problem, International Journal of Combinatorics 2011 (2011).
 X. S. Yang, S. S. S. Hosseini, A. H. Gandomi, Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect, Applied Soft Computing 12 (3) (2011) 1180â€“1186
 B. Rampriya, K. Mahadevan, S. Kannan, Unit commitment in deregulated power system using lagrangian firefly algorithm, in: Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on, IEEE, 2010, pp. 389â€“393.
 Y. Zhang, L. Wu, A novel method for rigid image registration based on firefly algorithm, International Journal of Research and Reviews in Soft and Intelligent Computing (IJRRSIC) 2 (2) (2012) 141â€“146.
 M. Horng, Vector quantization using the firefly algorithm for image compression, Expert Systems with Applications 39 (1) (2012) 1078â€“1091.
 T. Hassanzadeh, H. Vojodi, F. Mahmoudi, Non-linear grayscale image enhancement based on firefly algorithm, in: Swarm, Evolutionary, and Memetic Computing, Springer, 2011, pp. 174â€“181.
 B. Basu, G. Mahanti, Fire-fly and artificial bees colony algorithm for synthesis of scanned and broadside linear array antenna, Progress In Electromagnetics Research B 32 (2011) 169â€“190.
 B. Jakimovski, B. Meyer, E. Maehle, Firefly flashing synchronization as inspiration for self-synchronization of walking robot gait patterns using a decentralized robot control architecture, Architecture of Computing Systems (ARCS) 2010 (2010) 61â€“72.
 S. Severin, J. Rossmann, A comparison of different metaheuristic algorithms for optimizing blended ptp movements for industrial robots, Intelligent Robotics and Applications (2012) 321â€“330.Fister, Iztok, Xin-She Yang, and Janez Brest. "A comprehensive review of firefly algorithms." Swarm and Evolutionary Computation 13 (2013): 34-46.
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