Bacterial Foraging Algorithm Based on Quantum-Behaved Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 948-954 ◽  
Author(s):  
Ling Li ◽  
Xiong Fa Mai

Bacterial Foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization,but the E.coli algorithm depends on random search directions which may lead to delay in reaching the global solution.The quantum-behaved particle swarm optimization (QPSO) algorithm may lead to possible entrapment in local minimum solutions. In order to overcome the delay in optimization and to further enhance the performance of BFA,a bacterial foraging algorithm based on QPSO(QPSO-BFA) is presented.The new algorithm is proposed to combines both algorithms’ advantages in order to get better optimization values. Simulation results on eight benchmark functions show that the proposed algorithm is superior to the BFA,QPSO and BF-PSO.

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


2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Sushree Sangita Patnaik ◽  
Anup Kumar Panda

Conventional mathematical modeling-based approaches are incompetent to solve the electrical power quality problems, as the power system network represents highly nonlinear, nonstationary, complex system that involves large number of inequality constraints. In order to overcome the various difficulties encountered in power system such as harmonic current, unbalanced source current, reactive power burden, active power filter (APF) emerged as a potential solution. This paper proposes the implementation of particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithms which are intended for optimal harmonic compensation by minimizing the undesirable losses occurring inside the APF itself. The efficiency and effectiveness of the implementation of two approaches are compared for two different conditions of supply. The total harmonic distortion (THD) in the source current which is a measure of APF performance is reduced drastically to nearly 1% by employing BFO. The results demonstrate that BFO outperforms the conventional and PSO-based approaches by ensuring excellent functionality of APF and quick prevail over harmonics in the source current even under unbalanced supply.


Sign in / Sign up

Export Citation Format

Share Document