Abstract

This paper is meant to present a meta-heuristic algorithms and their application to combinatorial optimization problems. This report contains an assessment of the rapid development of meta-heuristic thoughts, their convergence towards a unified fabric and the richness of potential application in optimization problems. The paper presents a brief survey of different meta-heuristic algorithms aiming to solve optimization problems. The meta-heuristic is divided into four broad categories Evolutionary, Physics-based, Swarm-based and Human-based algorithms.

References

• Greco, Salvatore, J. Figueira, and M. Ehrgott. "Multiple criteria decision analysis." Springer's International series (2005).
• Lee, Kang Seok, and Zong Woo Geem. "A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice." Computer methods in applied mechanics and engineering 194.36 (2005): 3902-3933.‏
• Mirjalili, Seyedali, and Andrew Lewis. "The whale optimization algorithm." Approaches in Engineering Software 95 (2016): 51-67.‏
• Xie, Liping, et al. "Artificial physics optimisation: a brief survey." International Journal of Bio-Inspired Computation 2.5 (2010): 291-302.‏
• Binitha, S., and S. Siva Sathya. "A survey of bio inspired optimization algorithms." International Journal of Soft Computing and Engineering 2.2 (2012): 137-151.‏
• Haupt, Randy L., and Sue Ellen Haupt. Practical genetic algorithms. John Wiley & Sons, 2004.
• Nordin, Peter, Wolfgang Banzhaf, and Frank D. Francone. "Efficient evolution of machine code for CISC architectures using instruction blocks and homologous crossover." Advances in genetic programming 3 (1999): 275-299.
• Goldberg, David E., and Jon Richardson. "Genetic algorithms with sharing for multimodal function optimization." Genetic algorithms and their applications: Proceedings of the Second International Conference on Genetic Algorithms. Hillsdale, NJ: Lawrence Erlbaum, 1987.
• Bäck, Thomas, David B. Fogel, and Zbigniew Michalewicz, eds. Evolutionary computation 1: Basic algorithms and operators. Vol. 1. CRC press, 2000.‏
• Beheshti, Zahra, and Siti Mariyam Hj Shamsuddin. "A review of population-based meta-heuristic algorithms." Int. J. Adv. Soft Comput. Appl 5.1 (2013): 1-35.‏
• Rashedi, Esmat, Hossein Nezamabadi-Pour, and Saeid Saryazdi. "GSA: a gravitational search algorithm." Information sciences 179.13 (2009): 2232-2248.‏
• Ghasemi, Mojtaba, et al. "Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm." Engineering Applications of Artificial Intelligence 39 (2015): 100-108.
• Xing, Bo, and Wen-Jing Gao. Innovative computational intelligence: a rough guide to 134 clever algorithms. Switzerland: Springer International Publishing, 2014.‏
• Wiener, Norbert. Cybernetics or Control and Communication in the Animal and the Machine. Vol. 25. MIT press, 1961.
• Labbi, Y., and D. Attous. "BIG BANG-BIG CRUNCH OPTIMIZATION ALGORITHM FOR ECONOMIC DISPATCH WITH VALVE-POINT EFFECT."Journal of Theoretical & Applied Information Technology 16 (2010).
• Genc, H. M., and A. K. Hocaoglu. "Bearing-only target tracking based on big bang–big crunch algorithm." Computing in the Global Information Technology, 2008. ICCGI'08. The Third International Multi-Conference on. IEEE, 2008.
• Can, Umit, and Bilal Alatas. "Physics based metaheuristic algorithms for global optimization." American Journal of Information Science and Computer Engineering 1.3 (2015): 94-106.‏
• Kaveh, Ali, and Siamak Talatahari. "Optimal design of skeletal structures via the charged system search algorithm." Structural and Multidisciplinary Optimization 41.6 (2010): 893-911
• Kaveh, A., and S. Talatahari. "Hybrid charged system search and particle swarm optimization for engineering design problems." Engineering Computations 28.4 (2011): 423-440
• Can, Umit, and Bilal Alatas. "Physics based metaheuristic algorithms for global optimization." American Journal of Information Science and Computer Engineering 1.3 (2015): 94-106.
• Kaveh, A., and S. Talatahari. "Hybrid charged system search and particle swarm optimization for engineering design problems." Engineering Computations 28.4 (2011): 423-440.‏
• Kaveh, A., and S. Talatahari. "Hybrid charged system search and particle swarm optimization for engineering design problems." Engineering Computations 28.4 (2011): 423-440.‏
• Juang, Chia-Feng. "A hybrid of genetic algorithm and particle swarm optimization for recurrent network design." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34.2 (2004): 997-1006.‏
• combinatorial optimization problems.it is a technique for optimization that was introduced in the early 1990’s[Dorigo, Marco, Mauro Birattari, and Thomas Stutzle. "Ant colony optimization."IEEE computational intelligence magazine 1.4 (2006): 28-39.‏
• combinatorial optimization problems.it is a technique for optimization that was introduced in the early 1990’s[Dorigo, Marco, Mauro Birattari, and Thomas Stutzle. "Ant colony optimization."IEEE computational intelligence magazine 1.4 (2006): 28-39.‏
• Yang, Xin-She. "Cuckoo search and firefly algorithm: overview and analysis."Cuckoo Search and Firefly Algorithm. Springer International Publishing, 2014. 1-26
• Gandomi, Amir Hossein, Xin-She Yang, and Amir Hossein Alavi. "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems." Engineering with computers 29.1 (2013): 17-35.‏
• Pham, D. T., et al. "The bees algorithm-A novel tool for complex optimisation."Intelligent Production Machines and Systems-2nd I* PROMS Virtual International Conference (3-14 July 2006). sn, 2011.
• Agazadeh, F., and M. R. Meybodi. "Cooperative Bees Algorithm." International Conference on Measurement and Control Engineering 2nd (ICMCE 2011). ASME Press, 2011.‏
• Mirjalili, Seyedali, and Andrew Lewis. "The whale optimization algorithm."Advances in Engineering Software 95 (2016): 51-67.
• Rao, R. Venkata, and V. D. Kalyankar. "Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm."Engineering Applications of Artificial Intelligence 26.1 (2013): 524-531.
• Dueck, Gunter, and Tobias Scheuer. "Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing." Journal of computational physics 90.1 (1990): 161-175.
• Rao, R. Venkata, and Vivek Patel. "An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems."Scientia Iranica 20.3 (2013): 710-720.‏
• Geem, Zong Woo, Joong Hoon Kim, and Gobichettipalayam Vasudevan Loganathan. "A new heuristic optimization algorithm: harmony search."simulation 76.2 (2001): 60-68.‏
• He, Shan, Q. Henry Wu, and J. R. Saunders. "Group search optimizer: an optimization algorithm inspired by animal searching behavior." IEEE transactions on evolutionary computation 13.5 (2009): 973-990.‏
• Gates, Kathleen M., and Peter CM Molenaar. "Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples." Neuroimage 63.1 (2012): 310-319.
• Glover, Fred, James P. Kelly, and Manuel Laguna. "Genetic algorithms and tabu search: hybrids for optimization." Computers & Operations Research 22.1 (1995): 111-134.‏
• Voß, Stefan, et al., eds. Meta-heuristics: Advances and trends in local search paradigms for optimization. Springer Science & Business Media, 2012.‏
• Gallego, Ramon A., Rubén Romero, and Alcir J. Monticelli. "Tabu search algorithm for network synthesis." IEEE Transactions on Power Systems 15.2 (2000): 490-495.
• Abdel-Raouf, O., and Abdel-Baset, M. (2014). A new hybrid flower pollination algorithm for solving constrained global optimization problems. International Journal of Applied Operational Research-An Open Access Journal, 4(2), 1-13.
• Abdel-Raouf, Osama, Ibrahim El-Henawy, and Mohamed Abdel-Baset. "A novel hybrid flower pollination algorithm with chaotic harmony search for solving sudoku puzzles." International Journal of Modern Education and Computer Science 6.3 (2014): 38.
• Abdel-Baset, M., and Hezam, I. M. (2015). An improved flower pollination algorithm for ratios optimization problems. Applied Mathematics & Information Sciences Letters An International Journal, 3(2), 83-91.
• Abdel-Baset, M., and Hezam, I. M. (2015). An Effective Hybrid Flower Pollination and Genetic Algorithm for Constrained Optimization Problems. Advanced Engineering Technology and Application An International Journal, 4, 27-27.
• Abdel-Baset, M., and Hezam, I. (2016). Cuckoo Search and Genetic Algorithm Hybrid Schemes for Optimization Problems. Appl. Math, 10(3), 1185-1192.
• Abdel-Basset, M., Hessin, A. N., and Abdel-Fatah, L. (2016). A comprehensive study of cuckoo-inspired algorithms. Neural Computing and Applications, 1-17.
• Abdel-Baset, M., and Hezam, I. (2016). A Hybrid Flower Pollination Algorithm for Engineering Optimization Problems. International Journal of Computer Applications, 140(12).
• Abdel-Baset, M., and Hezam, I. M. (2016). A hybrid flower pollination algorithm for solving ill-conditioned set of equations. International Journal of Bio-Inspired Computation, 8(4), 215-220.
• Abdel-Baset, M., and Hezam, I. M. (2016). Solving Linear Least Squares Problems Based on Improved Cuckoo Search Algorithm.
• Metwalli, M. A. B., & hezam, I. (2015). A Modified Flower Pollination Algorithm for Fractional Programming Problems. International Journal of Intelligent Systems and Applications in Engineering, 3(3).
• Hezam, I. M., Abd-ElBaset, M., & Selem, I. (2015). Cuckoo Search Algorithm for Stellar Population Analysis of Galaxies. International Journal of Information Technology and Computer Science, 7, 29-33.
• Metwalli, M. A. B., & hezam, I. (2015). A Modified Flower Pollination Algorithm for Fractional Programming Problems. International Journal of Intelligent Systems and Applications in Engineering, 3(3).
• Baset, M. A., Satar, M. M. A., Abdel-Raouf, O., & El-Henawy, I. (2016). Non-Dominated Sorting Genetic Algorithm Based on Altruism for Solving Multi-Objective Optimization Problems. Journal of Computational and Theoretical Nanoscience, 13(8), 5060-5071.
• Abdel-Raouf, O., and Metwally, M. A. B. (2013). A survey of harmony search algorithm. International Journal of Computer Applications, 70(28).
• Abdel-Raouf, O., Abdel-Baset, M., and El-Henawy, I. (2014). An improved chaotic bat algorithm for solving integer programming problems. International Journal of Modern Education and Computer Science, 6(8), 18.
• Abdel-Raouf, O., El-henawy, I., and Abdel-Baset, M. (2014). chaotic Harmony Search Algorithm with Different Chaotic Maps for Solving Assignment Problems. International Journal of Computer Applications, 86(10).
• Abdel-Raouf, O., Abdel-Baset, M., and El-henawy, I. (2014). Chaotic firefly algorithm for solving definite integral. International Journal of Information Technology and Computer Science (IJITCS), 6(6), 19.
• Abdel-Raouf, O., Abdel-Baset, M., and El-henawy, I. (2014). Improved Harmony Search with Chaos for Solving Linear Assignment Problems. International Journal of Intelligent Systems and Applications, 6(5), 55.
• El-henawy, I., Abdel-Raouf, O., and Abdelbaset, M. (2014). Improved harmony search algorithm with chaos for solving definite integral. International Journal of Operational Research, 21(2), 252-261.
• Wu, H., Zhou, Y., Luo, Q., and Basset, M. A. (2016). Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm. Computational Intelligence and Neuroscience, 2016.
• Abdel-Baset, Mohamed, et al. "Stellar population analysis of galaxies based on improved flower pollination algorithm." International Journal of Mathematical Modelling and Numerical Optimisation 8.3 (2018): 183-196.
• Abdel-Basset, Mohamed, et al. "Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization." Journal of medical systems 41.12 (2017): 197.
• Abdel-Basset, Mohamed, et al. "A modified flower pollination algorithm for the multidimensional knapsack problem: human-centric decision making." Soft Computing (2017): 1-19.
• Zhou, Yongquan, et al. "Discrete greedy flower pollination algorithm for spherical traveling salesman problem." Neural Computing and Applications (2017): 1-16.
• Zhou, Yongquan, et al. "A simplex method-based social spider optimization algorithm for clustering analysis." Engineering Applications of Artificial Intelligence 64 (2017): 67-82.
• Abdel-Basset, Mohamed, et al. "A comparative study of cuckoo search and flower pollination algorithm on solving global optimization problems." Library Hi Tech 35.4 (2017): 595-608.
• Abdel-Basset, Mohamed, et al. "Solving 0–1 knapsack problems by binary dragonfly algorithm." International Conference on Intelligent Computing. Springer, Cham, 2017.
• Abdel-Basset, Mohamed, et al. "Krill herd algorithm based on cuckoo search for solving engineering optimization problems." Multimedia Tools and Applications (2017): 1-24.
• Abdel-Baset, Mohamed, et al. "Elite opposition-flower pollination algorithm for quadratic assignment problem." Journal of Intelligent & Fuzzy Systems 33.2 (2017): 901-911.
• Abdel-Baset, Mohamed, Haizhou Wu, and Yongquan Zhou. "A complex encoding flower pollination algorithm for constrained engineering optimisation problems." International Journal of Mathematical Modelling and Numerical Optimisation 8.2 (2017): 108-126.