A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    From few decades’ 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.

     


  • Keywords


    Optimizations Techniques (OT), Artificial Intelligence, Artificial Bee Colony (ABC) Algorithm, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Firefly Algorithm (FFA), Fitness functions.

  • References


      [1] https://en.wikipedia.org/wiki/Mathematical_optimization

      [2] "The Nature of Mathematical Programming Archived2014-03-05 at the Wayback Machine.," Mathematical Programming Glossary, INFORMS Computing Society.

      [3] Jung-Min Yang, Jong-Hwan Kim, “Sliding Mode Control for Trajectory Tracking of Nonholonomic Wheeled Mobile Robots”, IEEE Transactions on Robotics and Automation, Vol. 15, No. 3, June 1999, 578-587.

      [4] Yogeswaran Mohan, S. G. Ponnambalam, “An Extensive Review of Research in Swarm Robotics”, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), 140–145.

      [5] 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>

      [6] M. R. AlRashidi, M. E. El-Hawary, “A Survey of Particle Swarm Optimization Applications in Electric Power Systems”, IEEE Transactions on Evolutionary Computation, Sep. 2009, DOI: 10.1109/TEVC.2006.880326.

      [7] Kwang Y Lee, Mohamed A El-Sharkawi, “Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems”, IEEE Press, Wiley Interscience, A John Wiley & Sons Inc. Publication, 2008, ISBN 978-0471-45711-4.

      [8] Ahmed M Hasan, KhairulmizamSamsudin, Abd Rahman Ramli, Raja SyamsulAzmir, and Salam A Ismaeel, “A Review of NavigationSystems (Integrationand Algorithms)”, Australian Journal of Basic and Applied Sciences, 3(2), 943-959, Jan 2009.

      [9] Oren La'adan, Amnon Barak, “Inter Process Communication Optimization In A Scalable Computing Cluster”, Annual Review of Scalable Computing. September 2000, 121-173.

      [10] Minoru Mukuda, Yasuhiro Tsujimura, “A Multiobjective Genetic Algorithm For Solving Reliability Optimization Problem Of A Communication Network System”, Advanced Reliability Modeling II. July 2006, 166-175.

      [11] M. Egmont-Petersena, D. de Ridder b, H. Handels, “Image processing with neural networks—a review”, Pattern Recognition Society, Elsevier, 2002, 35 (10), 2279-2301, DOI: 10.1016/s0031-3203(01)00178-9.

      [12] Mahamed G. Omran, Andries P. Engelbrecht, Ayed Salman, “Image Classification Using Particle Swarm Optimization”, Recent Advances in Simulated Evolution and Learning. August 2004, 347-365.

      [13] A. Ghosh and A. Gorai, “Hue-Preserving Color Image Enhancement Using Particle Swarm Optimization”, Integration of Swarm Intelligence and Artificial Neural Network. June 2011, 157-178.

      [14] João Manuel, R. S. Tavares,Luísa Ferreira Bastos, “Improvement of Modal Matching Image Objects in Dynamic Pedobarography Using Optimization Techniques”, Progress in Computer Vision and Image Analysis. August 2009, 339-368.

      [15] HUA-MEI CHEN and PRAMOD K. VARSHNEY, “Techniques for Mutual Information-Based Brain Image Registration and Their Applications”, Medical Imaging Systems Technology. August 2005, 325-350.

      [16] 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, 134–142, 1991.

      [17] 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.Männer and B.Manderick (Eds.), Elsevier Publishing, 509-520, 1992.

      [18] Dorigo M, Optimization, “learning and natural algorithms” PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992 [in Italian].

      [19] Dorigo M, Gambardella LM, “Ant colony system: A cooperative learning approach to the traveling salesman problem”, IEEE Transactions on Evolutionary Computing, 1997, 1(1), 53–66.

      [20] Dorigo M, Stützle T, “Ant Colony optimization. Cambridge, MA: MIT Press; 2004.

      [21] Bullnheimer B, Hartl R, Strauss C, “A new rank-based version of the Ant System: A computational study”, Central European Journal of Operations Research, 1999;7(1):25–38.

      [22] Stützle T, Hoos HH, “MAX–MIN Ant system”, Future Generation Computer Systems 2000, 16(8), 889–914.

      [23] Blum C, Dorigo M, “The hyper-cube framework for ant colony optimization”, IEEE Transactions on System Management Cybernetics Part B 2004, 34(2), 1161–72.

      [24] Gambardella LM, Taillard ÉD, Agazzi G, “MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows”, “New ideas in optimization”, London: McGraw-Hill; 1999. p. 63–76.

      [25] Reimann M, Doerner K, Hartl RF, “D-ants: Savings based ants divide and conquer the vehicle routing problems”, Computers & Operations Research, 2004, 31(4), 563–91.

      [26] Kennedy, J. and Eberhart, R. C, “Particle swarm optimization”, Proceedings IEEE International conference on neural networks Vol. 4, pp. 1942-1948. IEEE service center, Piscataway, NJ, 1995.

      [27] Eberhart, R. C. and Kennedy J, “A new optimizer using particle swarm theory”, Proceedings of the sixth international symposium on micro machine and human science pp. 39-43. IEEE service center, Piscataway, NJ, Nagoya, Japan, 1995.

      [28] Eberhart, R. C. and Shi, “Particle swarm optimization: developments, applications and resources”, Proc. congress on evolutionary computation 2001 IEEE service center, Piscataway, NJ., Seoul, Korea., 2001.

      [29] Y amilledel V alle, Ganesh Kumar Venayagamoorthy, Salman Mohagheghi, Jean-Carlos Hernandez, Ronald G. Harley, “Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems”, IEEE Transactions on Evolutionary Computation, 12(2), April 2008.

      [30] D. Karaboga, “An idea based on honey bee swarm for numerical optimization”, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.

      [31] Pei-Wei Tsai, Jeng-Shyang Pan, Bin-Yih Liao, and Shu-Chuan Chu, “Enhanced Artificial Bee Colony Optimization”, International Journal of Innovative Computing, Information and Control Volume 5, Issue 12, December 2009.

      [32] Karaboga, Dervis, and BahriyeAkay, "A modified artificial bee colony (ABC) algorithm for constrained optimization problems." Applied soft computing, 11(3), April 2011, 3021-3031.

      [33] 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.

      [34] Xin-She Yang, "Firefly algorithms for multimodal optimization", International symposium on stochastic algorithms. Springer, Berlin, Heidelberg, Oct 2009, 168-178.

      [35] 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. 428–432.

      [36] 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. 1359–1366.

      [37] S. M. Farahani, A. Abshouri, B. Nasiri, M. Meybodi, A gaussian firefly algorithm, International Journal of Machine Learning and Computing 1 (5) (2011) 448–454.

      [38] 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. 21–32.

      [39] 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. 89–98.

      [40] A. Gandomi, X.-S. Yang, S. Talatahari, A. Alavi, Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation 18 (1) (2013) 89 – 98. doi:10.1016/j.cnsns.2012.06.009.

      [41] 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. 264–269.

      [42] A. Husselmann, K. Hawick, Parallel parametric optimisation with firefly algorithms on graphical processing units, Technical Report CSTN-141.

      [43] T. Apostolopoulos, A. Vlachos, Application of the firefly algorithm for solving the economic emissions load dispatch problem, International Journal of Combinatorics 2011 (2011).

      [44] 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) 1180–1186

      [45] 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. 389–393.

      [46] 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) 141–146.

      [47] M. Horng, Vector quantization using the firefly algorithm for image compression, Expert Systems with Applications 39 (1) (2012) 1078–1091.

      [48] T. Hassanzadeh, H. Vojodi, F. Mahmoudi, Non-linear grayscale image enhancement based on firefly algorithm, in: Swarm, Evolutionary, and Memetic Computing, Springer, 2011, pp. 174–181.

      [49] 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) 169–190.

      [50] 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) 61–72.

      [51] S. Severin, J. Rossmann, A comparison of different metaheuristic algorithms for optimizing blended ptp movements for industrial robots, Intelligent Robotics and Applications (2012) 321–330.

      Fister, Iztok, Xin-She Yang, and Janez Brest. "A comprehensive review of firefly algorithms." Swarm and Evolutionary Computation 13 (2013): 34-46.

 

View

Download

Article ID: 20205
 
DOI: 10.14419/ijet.v7i4.5.20205




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.