Name
Title | ||
|---|---|---|
A New Elitist Multi-Objective Stochastic Search Technique And Its Application To Economic-Emission Dispatch Problem In Power Systems |
Abstract | ||
|---|---|---|
In this paper, a new Multi-Objective Hybrid Evolutionary Algorithm (MOHEA) dubbed as Elitist Multi-Objective Stochastic Search Technique - II (EMOSST-II) which is capable of finding multiple Pareto-optimal solutions with good diversity in a single run is presented. It is applied for the solution of two-objective Economic- Emission Dispatch Problem (EED) in Power Systems. The features of EMOSST-II ensure better diversity and prevent premature convergence to ensure better non-dominated solutions and faster convergence. The computational performance of EMOSST-II for EED is investigated on the IEEE 30 bus 6 generator system, IEEE 57 bus 13 generator system. The results indicate that the performance of EMOSST-II is competitive when compared to the other state-of-the-art elitist Multi-objective Evolutionary Algorithms in terms of convergence to true Pareto-optimal front, maintenance of good spread in Pareto solutions, speed of convergence and scalability. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/CEC.2007.4424852 | 2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS |
Keywords | Field | DocType |
evolutionary computation,premature convergence,power system | Convergence (routing),Mathematical optimization,Evolutionary algorithm,Premature convergence,Computer science,Economic emission dispatch,Electric power system,Evolutionary computation,Artificial intelligence,Machine learning,Pareto principle,Scalability | Conference |
Citations | PageRank | References |
1 | 0.38 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Srinivas | 1 | 1 | 0.38 |
| C. Patvardhan | 2 | 78 | 12.28 |
| D. Bhagwan Das | 3 | 4 | 1.44 |