Abstract
Various metaheuristics have been proposed recently and each of them has its inherent evolutionary, physical-based, and/or swarm intelligent mechanisms. This paper does not focus on any subbranch, but on the metaheuristics research from a unified view. The population of decision vectors is looked on as an abstract matrix and three novel basic solution generation operations, E[p(i,j)], E[p(c·i)] and E[i, p(c·i+j)], are proposed in this paper. They are inspired by the elementary matrix transformations, all of which have none latent meanings. Experiments with real-coded genetic algorithm, particle swarm optimization and differential evolution illustrate its promising performance and potential.
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References
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)
Das, S., Suganthan, P.N.: Differential Evolution: A Survey of the State-of-the-Art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. The MIT Press, Cambridge (2004)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Norwell (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks IV, pp. 1942–1948. IEEE Press, Piscataway (1995)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Leonora, B., Dorigo, M., Gambardella, L.M., Gutjahr, W.J.: A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing 8(2), 239–287 (2009)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf Optimizer. Advances in Engineering Software 69, 46–61 (2014)
Ju, Y.M.: Linear algebra, 2nd edn. Tsinghua University Press, Beijing (2002)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: A gravitational search algorithm. Information Sciences 179(13), 2232–2248 (2009)
Storn, R., Price, K.: Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization 11(4), 341–359 (1997)
Yao, X., Liu, Y., Lin, G.M.: Evolutionary Programming Made Faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)
Zhan, Z.H., Zhang, J., Li, Y., et al.: Orthogonal learning particle swarm optimization. IEEE Trans. Evolut. Comput. 15(6), 832–847 (2011)
Zhao, X.C., Lin, W.Q., Zhang, Q.F.: Enhanced Particle Swarm Optimization based on Principal Component Analysis and Line Search. Applied Mathematics and Computation 229(25), 440–456 (2014)
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Zhao, X., Hao, J. (2014). A Unified Matrix-Based Stochastic Optimization Algorithm. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_8
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DOI: https://doi.org/10.1007/978-3-319-11857-4_8
Publisher Name: Springer, Cham
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