Abstract
Evolutionary algorithms have been extensively used to solve static and dynamic single objective optimization problems, and static multiobjective optimization problems. However, there has only been tepid interest to solve multiobjective optimization problems in dynamic environments. It is only in the past few years that evolutionary algorithms have been used to solve dynamic multiobjective optimization problems and comprehensive benchmark suites have been proposed for testing the performance of algorithms. Prediction based algorithms may be able to provide information about the location of the changed optima and thereby assisting the evolutionary algorithm in the non-trivial task of tracking the changing Pareto Optimal Front or Set. Kalman filter is one of the widely used techniques in prediction scenarios for state estimation. A Dynamic Multi-objective Evolutionary algorithm was proposed in which the Kalman Filter was applied to the whole population to direct the search for Pareto Optimal Solutions in the decision space after a change in the problem has occurred. In this work, the Kalman Filter assisted Evolutionary Algorithm is tested on the IEEE CEC 2015 Benchmark problems set and the results are presented. It is observed that while the proposed algorithm performs well on some problems, more efficient strategies are required to supplement the algorithm in cases of high change severity, isolated and deceptive fronts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Zhang, Q., Li, H.: Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evolutionary Computation 11(6), 712–731 (2007)
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii. Trans. Evol. Comp 13(2), 284–302 (2009)
Cheong, C., Tan, K., Liu, D., Lin, C.: Multi-objective and prioritized berth allocation incontainer ports. Annals of Operations Research 180(1), 63–103 (2010)
Tan, W., Lu, F., Loh, A., Tan, K.: Modeling and control of a pilot ph plant using genetic algorithm. Engineering Applications of Artificial Intelligence 18(4), 485–494 (2005)
Ang, J., Tan, K., Mamun, A.: An evolutionary memetic algorithm for rule extraction. Expert Systems with Applications 37(2), 1302–1315 (2010)
Tan, K., Tang, H., Ge, S.: On parameter settings of hopfield networks applied to traveling salesman problems. IEEE Transactions on Circuits and Systems I: Regular Papers 52(5), 994–1002 (2005)
Tan, K., Tang, H., Yi, Z.: Global exponential stability of discrete-time neural networks for constrained quadratic optimization. Neurocomputing 56, 399–406 (2004)
Tan, K., Li, Y.: Grey-box model identification via evolutionary computing. Control Engineering Practice 10(7), 673–684 (2002). Developments in High Precision Servo Systems
Muruganantham, A., Zhao, Y., Gee, S.B., Qiu, X., Tan, K.C.: Dynamic multiobjective optimization using evolutionary algorithm with kalman filter. Procedia Computer Science 24, 66–75 (2013). 17th Asia Pacific Symposium on Intelligent and Evolutionary Systems, (IES2013)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons Inc, New York, NY, USA (2001)
Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans. Evolutionary Computation 8(5), 425–442 (2004)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell (2001)
Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME Journal of Basic Engineering 82(D), 35–45 (1960)
Welch, G., Bishop, G.: An introduction to the kalman filter. Technical report, Chapel Hill, NC, USA (1995)
Helbig, M., Engelbrecht, A.: Benchmark functions for cec 2015 special session and competition on dynamic multi-objective optimization. Technical report
Goh, C.K.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation 13(1), 103–127 (2009)
Helbig, M., Engelbrecht, A.: Archive management for dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 2047–2054 (June 2011)
Helbig, M., Engelbrecht, A.P.: Benchmarks for dynamic multi-objective optimisation. In: 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 84–91. IEEE (2013)
Helbig, M., Engelbrecht, A.P.: Benchmarks for dynamic multi-objective optimisation algorithms. ACM Comput. Surv. 46(3), 37:1–37:39 (2014)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C., da Fonseca, V.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Muruganantham, A., Tan, K.C., Vadakkepat, P. (2016). Solving the IEEE CEC 2015 Dynamic Benchmark Problems Using Kalman Filter Based Dynamic Multiobjective Evolutionary Algorithm. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-27000-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26999-3
Online ISBN: 978-3-319-27000-5
eBook Packages: EngineeringEngineering (R0)