Abstract
This paper describes a new approach to deal with dynamic optimization that uses a multi-population. Its main features include the use of a modified wind driven optimization algorithm that aims to foster impact of pressure on velocities of particles. Moreover, a concept of multi-region inspired from meteorology has been introduced along with a new collision avoidance technique to maintain good diversity while preventing collision between sub-populations. The method has been assessed using Moving Peaks Benchmark and compared to state of the art methods. Preliminary results are very encouraging and show viability of the method.
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References
CalderÃn, J.F., Masegosa, A.D., Suárez, A.R., Pelta, D.A.: Adaptation Schemes and Dynamic Optimization problems: A Basic Study on the Adaptive Hill Climbing Memetic Algorithm. In: Terrazas, G., Otero, F.E.B., Masegosa, A.D. (eds.) NICSO 2013. SCI, vol. 512, pp. 85–97. Springer, Heidelberg (2014)
Yang, S., Yao, X. (eds.): Evolutionary Computation for Dynamic Optimization Problems. SCI, vol. 490. Springer, Heidelberg (2013)
Bayraktar, Z., Komurcu, M., Bossard, J.A., Werner, D.H.: The Wind Driven Optimization Technique and its Application in Electromagnetics. IEEE Transactions on Antennas and Propagation 61(5), 2745–2757 (2013)
James, R.H.: An Introduction to Dynamic Meteorology, 4th edn., USA, vol. 88 (2004)
Chao, C.W., Fang, S.C., Liao, C.J.: A Tropical Cyclone-Based Method For Global Optimization. Journal of Industrial And Management Optimization 8, 103–115 (2012)
Nguyen, T.T.: Continuous Dynamic Optimisation Using Evolutionary Algorithms. PhD thesis, School of Computer Science, University of Birmingham (2011)
Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans. Evol. Comput. 10(4), 459–472 (2006)
Branke, J.: The moving peaks benchmark , http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/ (viewed November 8, 2008)
Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems. In: Advances in Evolutionary Computing: Theory and Applications, pp. 239–262 (2003)
Kamosi, M., Hashemi, A.B., Meybodi, M.R.: A hibernating multi-swarm optimization algorithm for dynamic environments. In: Proc. World Congr. on Nature and Biologically Inspired Computing, NaBIC 2010, pp. 363–369 (2010)
Yang, S., Li, C.: A clustering particle swarm optimizer for locating and tracking multi-ple optima in dynamic environments. IEEE Trans. Evol. Comput., 959–974 (2010)
Parrott, D., Li, X.: Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput. 10(4), 440–458 (2006)
Li, C., Yang, S.: A general framework of multipopulation methods with clustering in undetectable dynamic environments. IEEE Trans. Evol. Comput. 16(4), 556–577 (2012)
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Boulesnane, A., Meshoul, S. (2014). A New Multi-region Modified Wind Driven Optimization Algorithm with Collision Avoidance for Dynamic Environments. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_47
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DOI: https://doi.org/10.1007/978-3-319-11897-0_47
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