Meta-heuristics Algorithms: A survey

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2019 by IJETT Journal
Volume-67 Issue-5
Year of Publication : 2019
Authors : Shereen Zaki , and Abd El-Nasser H. Zaied
  10.14445/22315381/IJETT-V67I5P210

MLA 

MLA Style: Shereen Zaki , and Abd El-Nasser H. Zaied "Meta-heuristics Algorithms: A survey" International Journal of Engineering Trends and Technology 67.5 (2019): 67-74.

APA Style: Shereen Zaki , and Abd El-Nasser H. Zaied (2019). Meta-heuristics Algorithms: A survey International Journal of Engineering Trends and Technology, 67(5), 67-74.

Abstract
Because of effective applications and high power, meta-heuristic research has been widely conveyed in literature, which covers algorithms, applications, comparisons, and analysis. However, slight has been evidenced on insightful analysis of meta-heuristic performance issues, and it is still a “black box” that why certain meta-heuristics perform better on specific optimization problems and not as good on others. Meta-heuristics have been revealed as the best effective scheme for solving many hard optimization problems as it has the ability to deal with NP-hard problems Mainly, meta-heuristic algorithms are classified to different classes to discriminate between them in searching schemes and explain how the algorithms mimic a particular phenomenon behaviour in the search area, diverse classification explored, This paper targets to review of all meta-heuristics related issues also hybridized meta-heuristics are discussed

Reference
[1] BLUM, Christian; ROLI, Andrea. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 2003, 35.3: 268-308.
[2] ČREPINŠEK, Matej; LIU, Shih-Hsi; MERNIK, Marjan. Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 2013, 45.3: 35.
[3] KHAJEHZADEH, Mohammad, et al. A survey on meta-heuristic global optimization algorithms. Research Journal of Applied Sciences, Engineering and Technology, 2011, 3.6: 569-578.
[4] YANG, Xin-She, et al. Attraction and diffusion in nature-inspired optimization algorithms. Neural Computing and Applications, 2015, 1-8.
[5] HOLLAND, John H. Genetic algorithms. Scientific american, 1992, 267.1: 66-73.
[6] Hussain, Kashif, et al. "Metaheuristic research: a comprehensive survey." Artificial Intelligence Review (2018): 1-43.
[7] DORIGO, Marco; SOCHA, Krzysztof. An introduction to ant colony optimization. Universit de Libre de Bruxelles, CP, 2006, 194.6.
[8] EBERHART, Russell; KENNEDY, James. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks. 1995. p. 1942-1948.
[9] FESTA, Paola. Greedy randomized adaptive search procedures. AIROnews, 2003, 7.4: 7-11.
[10] LAGUNA, Manuel; TAILLARD, E.; DE WERRA, Dominique. Tabu search. Basel: Baltzer, 1993.
[11] CHEN, Zhiqiang; WANG, Ronglong. GA and ACO-based Hybrid Approach for Continuous Optimization. In: 2015 International Conference on Modeling, Simulation and Applied Mathematics. Atlantis Press, 2015.
[12] BOUSSAÏD, Ilhem; LEPAGNOT, Julien; SIARRY, Patrick. A survey on optimization metaheuristics. Information sciences, 2013, 237: 82-117.
[13] BLUM, Christian; ROLI, Andrea. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 2003, 35.3: 268-308.
[14] HUSSAIN, Kashif, et al. Metaheuristic research: a comprehensive survey. Artificial Intelligence Review, 2018, 1-43.
[15] SÖRENSEN, Kenneth. Metaheuristics—the metaphor exposed. International Transactions in Operational Research, 2015, 22.1: 3-18.
[16] GANDOMI, Amir H. Interior search algorithm (ISA): a novel approach for global optimization. ISA transactions, 2014, 53.4: 1168-1183.
[17] SAMSUDDIN, Sherylaidah; OTHMAN, Mohd Shahizan; YUSUF, Lizawati Mi. A REVIEW OF SINGLE AND POPULATION-BASED METAHEURISTIC ALGORITHMS SOLVING MULTI DEPOT VEHICLE ROUTING PROBLEM. International Journal of Software Engineering and Computer Systems, 2018, 4.2: 80-93.
[18] BIRATTARI, Mauro, et al. Classification of Metaheuristics and Design of Experiments for the Analysis of Components Tech. Rep. AIDA-01-05. 2001.
[19] FISTER JR, Iztok, et al. A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186, 2013.
[20] RAIDL, Günther R.; PUCHINGER, Jakob; BLUM, Christian. Metaheuristic Hybrids. In: Handbook of Metaheuristics. Springer, Cham, 2019. p. 385-417.
[21] ALBA, Enrique. Parallel metaheuristics: a new class of algorithms. John Wiley & Sons, 2005.
[22] DI GASPERO, Luca. Integration of metaheuristics and constraint programming. In: Springer Handbook of Computational Intelligence. Springer, Berlin, Heidelberg, 2015. p. 1225-1237.
[23] YANG, Xin-She. Nature-inspired metaheuristic algorithms. Luniver press, 2010.
[24] WOLPERT, David H., et al. No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1997, 1.1: 67-82.
[25] HO, Yu-Chi; PEPYNE, David L. Simple explanation of the no free lunch theorem of optimization. In: Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No. 01CH37228). IEEE, 2001. p. 4409-4414.
[26] BLUM, Christian; ROLI, Andrea. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 2003, 35.3: 268-308.
[27] FISTER JR, Iztok, et al. A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186, 2013.
[28] BINITHA, S., et al. A survey of bio inspired optimization algorithms. International Journal of Soft Computing and Engineering, 2012, 2.2: 137-151.
[29] YANG, Xin-She. Nature-inspired algorithms: success and challenges. In: Engineering and Applied Sciences Optimization. Springer, Cham, 2015. p. 129-143.
[30] KAR, Arpan Kumar. Bio inspired computing–A review of algorithms and scope of applications. Expert Systems with Applications, 2016, 59: 20-32.
[31] KHAJEHZADEH, Mohammad, et al. A survey on meta-heuristic global optimization algorithms. Research Journal of Applied Sciences, Engineering and Technology, 2011, 3.6: 569-578.
[32] SÖRENSEN, K.; GLOVER, F. W. Metaheuristics Encyclopedia of operations research and management science. 2013.
[33] GENDREAU, Michel; POTVIN, Jean-Yves. Metaheuristics in combinatorial optimization. Annals of Operations Research, 2005, 140.1: 189-213.
[34] Abdel-Basset, M., El-Shahat, D., Faris, H., & Mirjalili, S. (2019). A binary multi-verse optimizer for 0-1 multidimensional knapsack problems with application in interactive multimedia systems. Computers & Industrial Engineering.
[35] Abdel-Basset, M., El-Shahat, D., & Sangaiah, A. K. (2019). A modified nature inspired meta-heuristic whale optimization algorithm for solving 0–1 knapsack problem. International Journal of Machine Learning and Cybernetics, 10(3), 495-514
[36] Abdel‐Basset, M., El‐Shahat, D., & El‐henawy, I. A modified hybrid whale optimization algorithm for the scheduling problem in multimedia data objects. Concurrency and Computation: Practice and Experience, e5137.
[37] Abdel-Basset, M., Wang, G. G., Sangaiah, A. K., & Rushdy, E. (2019). Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimedia Tools and Applications, 78(4), 3861-3884.
[38] Liu, X., Luo, Q., Wang, D., Abdel-Baset, M., & Jiang, S. (2018, August). An Improved Most Valuable Player Algorithm with Twice Training Mechanism. In International Conference on Intelligent Computing (pp. 854-865). Springer, Cham.
[39] Zhang, S., Zhou, Y., Luo, Q., & Abdel-Baset, M. (2018, August). A Complex-Valued Encoding Satin Bowerbird Optimization Algorithm for Global Optimization. In International Conference on Intelligent Computing (pp. 834-839). Springer, Cham.
[40] Abdel-Basset, M., Manogaran, G., El-Shahat, D., & Mirjalili, S. (2018). A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Generation Computer Systems, 85, 129-145.
[41] Abdel-Basset, M., El-Shahat, D., El-Henawy, I., Sangaiah, A. K., & Ahmed, S. H. (2018). A novel whale optimization algorithm for cryptanalysis in Merkle-Hellman cryptosystem. Mobile Networks and Applications, 1-11.
[42] Abdel-Basset, M., El-Shahat, D., El-Henawy, I., & Sangaiah, A. K. (2018). A modified flower pollination algorithm for the multidimensional knapsack problem: human-centric decision making. Soft Computing, 22(13), 4221-4239.
[43] Abdel-Basset, M., & Shawky, L. A. (2018). Flower pollination algorithm: a comprehensive review. Artificial Intelligence Review, 1-25.
[44] Hezam, I. M., Abdel-Baset, M., & Hassan, B. M. (2016). A hybrid flower pollination algorithm with tabu search for unconstrained optimization problems. Inf. Sci. Lett, 5(1), 29-34.
[45] ABDEL-BASET, M. O. H. A. M. E. D., & HEZAM, I. M. (2015). An improved flower pollination algorithm based on simulated annealing for solving engineering optimization problems. Asian Journal of Mathematics and Computer Research, 149-170.

Keywords
meta-heuristics, Global optimization, optimization algorithms, Meta-heuristic Hybrids