Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Towards estimating nadir objective vector using evolutionary approaches
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Multiobjective Optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An interactive evolutionary multi-objective optimization and decision making procedure
Applied Soft Computing
An integrated method of multi-objective optimization for complex mechanical structure
Advances in Engineering Software
Memetic algorithm for dynamic bi-objective optimization problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
High performance computing for dynamic multi-objective optimisation
International Journal of High Performance Systems Architecture
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Simplex model based evolutionary algorithm for dynamic multi-objective optimization
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
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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.