Bacterial foraging optimisation: Nelder–Mead hybrid algorithm for economic load dispatch

2008 ◽  
Vol 2 (4) ◽  
pp. 556 ◽  
Author(s):  
B.K. Panigrahi ◽  
V. Ravikumar Pandi
Keyword(s):  
Hybrid Algorithm ◽  
Economic Load Dispatch ◽  
Bacterial Foraging

scholarly journals Hybrid Bacteria Foraging Particle Swarm Optimization based Optimal Reactive Power Dispatch for the Alleviation of Voltage Deviations

2019 ◽  
Vol 9 (2) ◽  
pp. 5350-5354
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Bacterial Foraging Algorithm ◽  
Reactive Power Dispatch

This paper presents a hybrid algorithm for optimal reactive power dispatch by combining two popular evolutionary computation algorithms; Bacterial Foraging algorithm and Particle Swarm Optimization. The Hybrid algorithm combines velocity and position updating strategy of Particle swarm optimization algorithm and reproduction and elimination dispersal of Bacterial foraging algorithm. The proposed algorithm is applied to solve optimal power flow with the objective of minimization of Sum of squares of voltage deviations of all load buses. The proposed approach has been evaluated on a standard IEEE 30 bus test system and 24 bus EHV southern region equivalent Indian power system. The results obtained by the proposed Hybrid algorithm are compared with their basic counter parts and superiority of the proposed hybrid algorithm is demonstrated

A Novel DE-PSO-DE (DPD) Algorithm for Economic Load Dispatch Problem

2014 ◽  
Vol 5 (4) ◽  
pp. 59-88 ◽  
Author(s):  
Kedar Nath Das ◽  
Raghav Prasad Parouha
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Analysis ◽  
Engineering Design ◽  
Hybrid Algorithm ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Information Sharing Mechanism

This paper presents a hybrid algorithm of two popular heuristics namely Differential Evolution (DE) and Particle Swarm Optimization (PSO) on a tri-population environment. 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. It is based on the information sharing mechanism of their inherent property to overcome the shortcomings of each other. The proposed method is called DPD as it uses DE-PSO-DE on a population. 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. Out of a total of 64 DPDs, Top 4 DPDs are investigated through CEC2006 constrained benchmark functions. Based on the ‘performance' analysis, best DPD is reported and further used in solving 5 engineering design problems along with economic load dispatch problem in order to confirm further the efficiency of the proposed DPD.

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