Dynamic Multi-objective Optimization and Decision-Making Using Modified NSGA-II: A Case Study on Hydro-thermal Power Scheduling

Most real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Treating such problems as a stationary optimization problem demand the knowledge of the pattern of change a priori and even then the procedure can be computationally expensive. Although dynamic consideration using evolutionary algorithms has been made for single-objective optimization problems, there has been a lukewarm interest in formulating and solving dynamic multi-objective optimization problems. In this paper, we modify the commonly-used NSGA-II procedure in tracking a new Pareto-optimal front, as soon as there is a change in the problem. Introduction of a few random solutions or a few mutated solutions are investigated in detail. The approaches are tested and compared on a test problem and a real-world optimization of a hydro-thermal power scheduling problem. This systematic study is able to find a minimum frequency of change allowed in a problem for two dynamic EMO procedures to adequately track Pareto-optimal frontiers on-line. Based on these results, this paper also suggests an automatic decision-making procedure for arriving at a dynamic single optimal solution on-line.

[1]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[2]  Kalyanmoy Deb,et al.  Towards estimating nadir objective vector using evolutionary approaches , 2006, GECCO.

[3]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[4]  F. Trutt,et al.  Efficient methods for optimal scheduling of fixed head hydrothermal power systems , 1988 .

[5]  Y. W. Wong,et al.  Short-term hydrothermal scheduling part. I. Simulated annealing approach , 1994 .

[6]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[7]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[8]  K. M. Nor,et al.  An efficient method for optimal scheduling of fixed head hydro and thermal plants , 1991 .

[9]  David Wallace,et al.  Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach , 2006, GECCO '06.

[10]  Bernhard Sendhoff,et al.  Constructing Dynamic Optimization Test Problems Using the Multi-objective Optimization Concept , 2004, EvoWorkshops.

[11]  D. K. Pratihar,et al.  FUZZY-GENETIC ALGORITHMS AND TIME-OPTIMAL OBSTACLE-FREE PATH GENERATION FOR MOBILE ROBOTS , 1999 .

[12]  Malcolm Irving,et al.  A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling , 1998 .

[13]  P. K. Chattopadhyay,et al.  Fast Evolutionary Progranuning Techniques for Short-Term Hydrothermal Scheduling , 2002, IEEE Power Engineering Review.

[14]  Malabika Basu,et al.  A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems , 2005 .

[15]  P. K. Chattopadhyay,et al.  Fast evolutionary programming techniques for short-term hydrothermal scheduling , 2003 .

[16]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.

[17]  Kalyanmoy Deb,et al.  Dynamic multiobjective optimization problems: test cases, approximations, and applications , 2004, IEEE Transactions on Evolutionary Computation.

[18]  Yang Jin-Shyr,et al.  Short term hydrothermal coordination using multi-pass dynamic programming , 1989 .