.png){kind=icon}
.png){kind=icon}
Return
DPD: An intelligent parallel hybrid algorithm for economic load dispatch problems with various practical constraints
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2016.07.012.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The Economic Load Dispatch (ELD) problem has attracted much attention in the field of electric power system. This paper proposes a novel parallel hybrid optimization methodology aimed at solving ELD problem with various generator constraints. The proposed approach combines the Differential Evolution (DE) and Particle Swarm Optimization (PSO). Initially the whole population (in increasing order of fitness) is divided into three groups - Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. The proposed method is called DPD as it uses DE-PSO-DE on a population in parallel manner. Two strategies namely Elitism (to retain the best obtained values so far) and Non-redundant search (to improve the solution quality) have been employed in DPD cycle. Moreover, the suitable mutation strategy for both DEs used in DPD is investigated over a set of 8 popular mutation strategies. Combination of 8 mutation strategies generated 64 different variants of DPD. Top 4 DPDs are investigated through IEEE CEC 2006 functions. Based on the performance analysis, best DPD is reported and further used in solving four different typical test systems of ELD problem. Numerical and graphical results indicate the efficiency, convergence characteristic and robustness of proposed DPD. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Differential Evolution
Particle Swarm Optimization
Non-redundant search
Elitism
IEEE CEC 2006 Problem
Economic Load Dispatch problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.