INFORMATION CHANGE THE WORLD

International Journal of Mathematical Sciences and Computing(IJMSC)

ISSN: 2310-9025 (Print), ISSN: 2310-9033 (Online)

Published By: MECS Press

IJMSC Vol.3, No.4, Nov. 2017

A Multi-view Comparison of Various Metaheuristic and Soft Computing Algorithms

Full Text (PDF, 288KB), PP.8-19


Views:69   Downloads:4

Author(s)

Abdulrahman Ahmed Bobakr Baqais

Index Terms

Metaheuristics;Review;Comparison

Abstract

AI algorithms have been applied in a wide spectrum of articles across different domains with great success in finding solutions. There is an increasing trend of applying these techniques on newer problems. However, the numerous numbers of algorithms that are classified as AI algorithm hinder the ability of any researcher to select which algorithm is suitable for his problem. The invention of new algorithms increases the difficulty for researchers to be updated about AI algorithms. This paper is intended to provide a multi-facet comparison between various AI algorithms in order to aid researchers in understanding the differences between some of the popular algorithms and select the suitable candidate for their problems.

Cite This Paper

Abdulrahman Ahmed Bobakr Baqais,"A Multi-view Comparison of Various Metaheuristic and Soft Computing Algorithms", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.3, No.4, pp.8-19, 2017.DOI: 10.5815/ijmsc.2017.04.02

Reference

[1]Glover, F., Future Paths for Integer Programming and Links to Artificial Intelligence. Comput. Oper. Res., 1986. 13: p. 533–549.

[2]Zäpfel, G., R. Braune, and M. Bögl, Metaheuristics in General, in Metaheuristic Search Concepts. 2010, Springer Berlin Heidelberg. p. 67-73.

[3]Gendreau, M. and J.-Y. Potvin, Handbook of metaheuristics. Vol. 2. 2010: Springer.

[4]Talbi, E.-G., Metaheuristics: from design to implementation. 2009, Hoboken, N.J.: John Wiley & Sons.

[5]Yang, X.-S. and W.I.O. service), Engineering optimization an introduction with metaheuristic applications. 2010, Hoboken, N.J.: John Wiley.

[6]Hare, W., J. Nutini, and S. Tesfamariam, A Survey of Non-gradient Optimization Methods in Structural Engineering. Adv. Eng. Softw., 2013. 59: p. 19–28.

[7]Jin, Y., A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 2005. 9: p. 3-12.

[8]Davidor, Y. Epistasis Variance: A Viewpoint on GA-Hardness. in FOGA. 1990.

[9]Zäpfel, G., R. Braune, and M. Bögl, Search Heuristics, in Metaheuristic Search Concepts. 2010, Springer Berlin Heidelberg. p. 31-64.

[10]Tikk, D., L.T. Kóczy, and T.D. Gedeon, A survey on universal approximation and its limits in soft computing techniques. International Journal of Approximate Reasoning, 2003. 33: p. 185-202.

[11]Dorigo, M., Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano, Italy, 1992.

[12]Kennedy, J. and R. Eberhart. Particle swarm optimization. in Neural Networks, 1995. Proceedings., IEEE International Conference on. 1995.

[13]Alander, J.T., Population size, building blocks, fitness landscape and genetic algorithm search efficiency in combinatorial optimization: an empirical study. 1999.

[14]Kicinger, R., T. Arciszewski, and K.D. Jong, Evolutionary computation and structural design: A survey of the state-of-the-art. Computers & Structures, 2005. 83: p. 1943-1978.

[15]Deng, W., et al., A novel parallel hybrid intelligence optimization algorithm for a function approximation problem. Computers & Mathematics with Applications, 2012. 63: p. 325-336.

[16]GULSEN, M., A.E. SMITH, and D.M. TATE, A genetic algorithm approach to curve fitting. International Journal of Production Research, 1995. 33: p. 1911-1923.

[17]Roy, R., S. Hinduja, and R. Teti, Recent advances in engineering design optimisation: Challenges and future trends. CIRP Annals - Manufacturing Technology, 2008. 57: p. 697-715.

[18]Pedrycz, W. and F. Gomide, An introduction to fuzzy sets: analysis and design. 1998: Mit Press.

[19]Oduguwa, V., R. Roy, and D. Farrugia, Development of a soft computing-based framework for engineering design optimisation with quantitative and qualitative search spaces. Appl. Soft Comput., 2007. 7: p. 166–188.

[20]Mizukami, Y., Y. Wakasa, and K. Tanaka. A proposal of neural network architecture for non-linear function approximation. in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. 2004.

[21]Carpenter, W.C. and J.F. Barthelemy, Common Misconceptions about Neural Networks as Approximators. Journal of Computing in Civil Engineering, 1994. 8: p. 345-358.

[22]Crossley, M., A. Nisbet, and M. Amos, Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms. 2013.

[23]Aarts, E., J. Korst, and W. Michiels, Simulated annealing, in Search methodologies. 2005, Springer. p. 187-210.

[24]Granville, V., M. Krivanek, and J.P. Rasson, Simulated annealing: a proof of convergence. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1994. 16(6): p. 652-656.

[25]Dowsland, K.A. and J.M. Thompson, Simulated annealing, in Handbook of Natural Computing. 2012, Springer. p. 1623-1655.

[26]Berkhin, P., A survey of clustering data mining techniques, in Grouping multidimensional data. 2006, Springer. p. 25-71.

[27]Rui, X. and D. Wunsch, II, Survey of clustering algorithms. Neural Networks, IEEE Transactions on, 2005. 16(3): p. 645-678.

[28]Jain, A.K., Data clustering: 50 years beyond K-means. Pattern recognition letters, 2010. 31(8): p. 651-666.

[29]Digalakis, J.G. and K.G. Margaritis, on benchmarking functions for genetic algorithms. International Journal of Computer Mathematics, 2001. 77: p. 481-506.

[30]Yang, X.-S., 1 - Optimization and Metaheuristic Algorithms in Engineering, in Metaheuristics in Water, Geotechnical and Transport Engineering. 2013, Elsevier: Oxford. p. 1-23.

[31]Ying, K.-C., et al., A novel function approximation based on robust fuzzy regression algorithm model and particle swarm optimization. Applied Soft Computing, 2011. 11: p. 1820-1826.

[32]EL-Naggar, K.M., M.R. AlRashidi, and A.K. Al-Othman, Estimating the input–output parameters of thermal power plants using PSO. Energy Conversion and Management, 2009. 50: p. 1767-1772.

[33]Gutjahr, W.J., ACO algorithms with guaranteed convergence to the optimal solution. Information Processing Letters, 2002. 82(3): p. 145-153.

[34]Glover, F. and S.d. Hanafi, Tabu search and finite convergence. Discrete Applied Mathematics, 2002. 119(1–2): p. 3-36.

[35]NING Ai-ping, Z.X.-y., Convergence analysis of artificial bee colony algorithm. Control and Decision, 2013. 28(10): p. 1554-1558.

[36]Quan, Y. and G. Yin, Analyzing Convergence and Rates of Convergence of Particle Swarm Optimization Algorithms Using Stochastic Approximation Methods. Automatic Control, IEEE Transactions on, 2015. 60(7): p. 1760-1773.

[37]Real Life Applications of Soft Computing - CRC Press Book.

[38]Lee, K.S. and Z.W. Geem, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, 2005. 194: p. 3902-3933.