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Review of Hybrid Combinations of Metaheuristics for Problem Solving Optimization

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Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 940))

Abstract

This article describes a review of the state of the art of some of the different metaheuristic combinations that exist for solving problems, using two or more methods in combination or hybrid form. There are different nature-inspired or metaheuristic algorithms, which have been classified to solve certain types of problems, although currently, trying to solve problems with a single method has been somewhat neglected, and modifications, hybridizations, among others, have been carried out. Combinations to solve a common problem, provides an opportunity to exploit the methods in different scenarios to be solved, focusing not only on the solution but also on the use of algorithms.

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References

  • Angira, R., and B.V. Babu. 2006. Optimization of process synthesis and design problems: A modified differential evolution approach. Chemical Engineering Science 61 (14): 4707–4721.

    Article  Google Scholar 

  • Arnaout, J.P., G. Rabadi, and R. Musa. 2010. A two-stage Ant Colony optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times. Journal of Intelligent Manufacturing 21 (6): 693–701.

    Article  Google Scholar 

  • Babaie-Kafaki, S., R. Ghanbari, and N. Mahdavi-Amiri. 2016. Hybridizations of genetic algorithms and neighborhood search metaheuristics for fuzzy bus terminal location problems. Applied Soft Computing 46: 220–229.

    Article  Google Scholar 

  • Behnamian, J., M. Zandieh, and S. M. T. Fatemi Ghomi. 2009. Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm. Expert Systems with Applications 36 (6): 9637–9644.

    Google Scholar 

  • Brito, J., F.J. Martínez, J.A. Moreno, and J.L. Verdegay. 2015. An ACO hybrid metaheuristic for close-open vehicle routing problems with time windows and fuzzy constraints. Applied Soft Computing 32: 154–163.

    Article  Google Scholar 

  • Chaimatanan, S., D. Delahaye, M. Mongeau. 2014. Hybrid metaheuristic optimization algorithm for strategic planning of 4D aircraft trajectories at the continent scale A hybrid metaheuristic optimization algorithm for strategic planning of 4D aircraft trajectories at the continental scale. 9 (4): 46–61. ieeexplore.ieee.org.

    Google Scholar 

  • Drezner, Z. 2008. Extensive experiments with hybrid genetic algorithms for the solution of the quadratic assignment problem. Computers & Operations Research 35 (3): 717–736.

    Article  MathSciNet  Google Scholar 

  • F. Neri, C. Cotta, and P. Moscato, Handbook of Memetic Algorithms. 2012.

    Google Scholar 

  • Feo, T.A., and M.G.C. Resende. 1995. Greedy randomized adaptive search procedures. Journal of Global Optimization 6 (2): 109–133.

    Article  MathSciNet  Google Scholar 

  • Galvão Costa, B. L., C. Luiz Graciola, B. A. Angélico, A. Goedtel, and M. F. Castoldi. 2018. Metaheuristics optimization applied to PI controllers tuning of a DTC-SVM drive for three-phase induction motors. Applied Soft Computing 62: 776–788.

    Google Scholar 

  • Gao, J., L. Sun, and M. Gen. 2008. A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research 35 (9): 2892–2907.

    Article  MathSciNet  Google Scholar 

  • Ghanbari, R., and N. Mahdavi-Amiri. 2011. Solving bus terminal location problems using evolutionary algorithms. Applied Soft Computing Journal 11 (1): 991–999.

    Article  Google Scholar 

  • Glover, F., Z. Lü, and J. K. Hao. 2010. Diversification-driven tabu search for unconstrained binary quadratic problems. 4OR, 8 (3): 239–253.

    Google Scholar 

  • Han, S., W. Pedrycz, and C. Han. 2005. Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing. Mathematical and Computer Modelling 41 (6–7): 697–709.

    Article  MathSciNet  Google Scholar 

  • Hansen, P., N. Mladenović, and J. A. Moreno Pérez. 2010. Variable neighbourhood search: Methods and applications. Annals of Operations Research 175 (1): 367–407.

    Google Scholar 

  • Hari Krishna, C., J. Amarnath, and S. Kamakshaiah. 2012. Simplified SVPWM algorithm for neutral point clamped 3-level inverter fed DTC-IM drive. In 2012 International conference on advances in power conversion and energy technologies, APCET (2012).

    Google Scholar 

  • Holland, J.H. 1992. Genetic algorithms understand genetic algorithms. Scientific American 267 (1): 66–73.

    Article  Google Scholar 

  • Jayaprakasam, S., S. Rahim, and C. Yen Leow. 2015. PSOGSA-Explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming. Applied Soft Computing 30, 229–237.

    Google Scholar 

  • Karaboga, D., and B. Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization 39 (3): 459–471.

    Article  MathSciNet  Google Scholar 

  • Kaveh, A., T. Bakhshpoori, and E. Afshari. 2014. An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm. Computers & Structures 143: 40–59.

    Article  Google Scholar 

  • Keskinturk, T., M.B. Yildirim, and M. Barut. 2012. An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times. Computers & Operations Research 39 (6): 1225–1235.

    Article  MathSciNet  Google Scholar 

  • Kim, J.-S., and S.-K. Sul. 1996. A novel voltage modulation technique of the space vector PWM. IEEJ Journal of Industry Applications 116 (8): 820–825.

    Google Scholar 

  • Knowles, J., and D. Corne. 2006. Memetic algorithms for multiobjective optimization: Issues, methods and prospects. In Recent advances in memetic algorithms. Springer-Verlag. 313–352.

    Google Scholar 

  • Kuo, R.J., T.C. Lin, F.E. Zulvia, and C.Y. Tsai. 2018. A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis. Applied Soft Computing 67: 299–308.

    Article  Google Scholar 

  • Lanza-Gutierrez, J.M., and J.A. Gomez-Pulido. 2015. Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for relay node deployment in wireless sensor networks. Applied Soft Computing 30: 675–687.

    Article  Google Scholar 

  • Liao, T.W., P.C. Chang, R.J. Kuo, and C.-J. Liao. 2014. A comparison of five hybrid metaheuristic algorithms for unrelated parallel-machine scheduling and inbound trucks sequencing in multi-door cross docking systems. Applied Soft Computing 21: 180–193.

    Article  Google Scholar 

  • Liefooghe, A., S. Verel, and J.-K. Hao. 2014. A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Applied Soft Computing 16: 10–19.

    Article  Google Scholar 

  • Lin, K.P. 2014. A novel evolutionary kernel intuitionistic fuzzy C-means clustering algorithm. IEEE Transactions on Fuzzy Systems 22 (5): 1074–1087.

    Article  Google Scholar 

  • López-Camacho, E., M. Jesús, G. Godoy, J. García-Nieto, A.J. Nebro, and J.F. Aldana-Montes. 2015. Solving molecular flexible docking problems with metaheuristics: A comparative study. Applied Soft Computing 28: 379–393.

    Article  Google Scholar 

  • Mafarja, M.M., and S. Mirjalili. 2017. Hybrid Whale Optimization Algorithm with simulated annealing for feature selection. Neurocomputing 260: 302–312.

    Article  Google Scholar 

  • Mehrabian, A.R., and C. Lucas. 2006. A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 1 (4): 355–366.

    Article  Google Scholar 

  • Mirjalili, S. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based System 89: 228–249.

    Article  Google Scholar 

  • Mirjalili, S. 2016. SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based System 96: 120–133.

    Article  Google Scholar 

  • Mirjalili, S., S.M. Mirjalili, and A. Lewis. 2014. Grey Wolf optimizer. Advances in Engineering Software 69: 46–61.

    Article  Google Scholar 

  • Mortazavi, A., V. ToÄŸan, and A. NuhoÄŸlu. 2018. Interactive search algorithm: A new hybrid metaheuristic optimization algorithm. Engineering Applications of Artificial Intelligence 71: 275–292.

    Article  Google Scholar 

  • Niknam, T., and B. Amiri. 2010. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing Journal 10 (1): 183–197.

    Article  Google Scholar 

  • Ochiai, H., P. Mitran, H.V. Poor, and V. Tarokh. 2005. Collaborative beamforming for distributed wireless ad hoc sensor networks. IEEE Transactions on Signal Processing 53 (11): 4110–4124.

    Article  MathSciNet  Google Scholar 

  • Parlett, S. David. The Oxford History of Board Games,… - Google Académico. https://scholar.google.com.mx/scholar?hl=es&as_sdt=0%2C5&q=Parlett%2C+S.+David%2C+The+Oxford+History+of+Board+Games%2C+Oxford+University+Press%2C+USA%2C+1999.&btnG. Accessed 29 May 2020.

  • Potluri, A., and A. Singh. 2013. Hybrid metaheuristic algorithms for minimum weight dominating set. Applied Soft Computing 13: 76–88.

    Article  Google Scholar 

  • Rashedi, E., H. Nezamabadi-pour, and S. Saryazdi. 2009. GSA: A gravitational search algorithm. Information Sciences (Ny) 179 (13): 2232–2248.

    Article  Google Scholar 

  • Restrepo, J., J.M. Aller, A. Bueno, V.M. Guzmán, and M.I. Giménez. 2011. Generalized algorithm for pulse width modulation using a two-vectors based technique. EPE Journal 21 (2): 30–39.

    Article  Google Scholar 

  • Sadollah, A., Y. Choi, G. Yoo, and J.H. Kim. 2015. Metaheuristic algorithms for approximate solution to ordinary differential equations of longitudinal fins having various profiles. Applied Soft Computing 33: 360–379.

    Article  Google Scholar 

  • Saremi, S., S. Mirjalili, and A. Lewis. 2017. Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software 105: 30–47.

    Article  Google Scholar 

  • Singh, P. R., M. Abd Elaziz, and S. Xiong. 2019. Ludo game-based metaheuristics for global and engineering optimization. Applied Soft Computing, 84, 105723.

    Google Scholar 

  • Tang, L., J. Liu, A. Rong, and Z. Yang. 2000. A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. European Journal of Operational Research 124 (2): 267–282.

    Article  Google Scholar 

  • Venkatesh, P., and A. Singh. 2015. Two metaheuristic approaches for the multiple traveling salesperson problem. Applied Soft Computing 26: 74–89.

    Article  Google Scholar 

  • Woo Geem, Z., J. Hoon Kim, and G. V Loganathan. 2001. A new heuristic optimization agorithm: Harmony search.

    Google Scholar 

  • Yi, H., Q. Duan, and T.W. Liao. 2013. Three improved hybrid metaheuristic algorithms for engineering design optimization. Applied Soft Computing 13: 2433–2444.

    Article  Google Scholar 

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Marylu L. Lagunes, Oscar Castillo, Fevrier Valdez & Jose Soria

Authors
  1. Marylu L. Lagunes
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  2. Oscar Castillo
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  3. Fevrier Valdez
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  4. Jose Soria
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Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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Lagunes, M.L., Castillo, O., Valdez, F., Soria, J. (2021). Review of Hybrid Combinations of Metaheuristics for Problem Solving Optimization. In: Castillo, O., Melin, P. (eds) Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-68776-2_12

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