Abstract
Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. However, to avoid complications, such problems are usually treated as static optimization problems demanding the knowledge of the pattern of change a priori. If the problem is optimized in its totality for the entire duration of application, the procedure can be computationally expensive, involving a large number of variables. Despite some studies on the use of evolutionary algorithms in solving single-objective dynamic optimization problems, there has been a lukewarm interest in solving dynamic multi-objective optimization problems. In this paper, we discuss two different approaches to dynamic optimization for single as well as multi-objective problems. Both methods are discussed and their working principles are illustrated by applying them to different practical optimization problems. The off-line optimization approach in arriving at a knowledge base which can then be used for on-line applications is applicable when the change in the problem is significant. On the other hand, an off-line approach to arrive at a minimal time window for treating the problem in a static manner is more appropriate for problems having a slow change. Further approaches and applications of these two techniques remain as important future work in making on-line optimization task a reality in the coming years.
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References
Basu, M.: A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems. Electric Power and Energy Systems 27(2), 147–153 (2005)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Springer, Heidelberg (2001)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Pratihar, D.K., Ghosh, A.: Learning to Avoid Moving Obstacles Optimally for Mobile Robots Using a Genetic-Fuzzy Approach. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 583–592. Springer, Heidelberg (1998)
Deb, K., Udaya Bhaskara Rao, N., Karthik, S.: Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007)
Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: Test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation 8(5), 425–442 (2000)
Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: A forward-looking approach. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1201–1208 (2006)
Jin, Y., Sendhoff, B.: Constructing Dynamic Optimization Test Problems Using the Multi-objective Optimization Concept. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 525–536. Springer, Heidelberg (2004)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)
Pratihar, D., Deb, K., Ghosh, A.: Fuzzy-genetic algorithms and time-optimal obstacle-free path generation for mobile robots. Engineering Optimization 32, 117–142 (1999)
Pratihar, D.K., Deb, K., Ghosh, A.: Optimal path and gait generations simultaneously of a six-legged robot using a ga-fuzzy approach. Robotics and Autonomous Systems 41, 1–21 (2002)
Wood, A.J., Woolenberg, B.F.: Power Generation, Operation and Control. John-Wiley & Sons (1986)
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Deb, K. (2013). Two Approaches for Single and Multi-Objective Dynamic Optimization. In: Alba, E., Nakib, A., Siarry, P. (eds) Metaheuristics for Dynamic Optimization. Studies in Computational Intelligence, vol 433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30665-5_6
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DOI: https://doi.org/10.1007/978-3-642-30665-5_6
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