Cooperative bacterial foraging algorithm for global optimization

Authors:
Hanning Chen;Yunlong Zhu;Kunyuan Hu
Affiliations:
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 8
Cited 1

Swarm intelligence

Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab

Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab
Transmission loss reduction based on FACTS and bacteria foraging algorithm

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Adaptive tuning of PID controller for multivariable system using bacterial foraging based optimization

AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
A taxonomy of cooperative search algorithms

HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
Optimization based on bacterial chemotaxis

IEEE Transactions on Evolutionary Computation
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation

IEEE Transactions on Evolutionary Computation

Solution to economic load dispatch problem based on FIREFLY algorithm and its comparison with BFO, CBFO-S and CBFO-Hybrid

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. However, the original BFO algorithm possesses a poor convergence behavior compared to the other successful nature-inspired algorithms. In order to accelerate the convergence speed of the bacterial colony near global optima, two cooperative approaches have been applied to BFO that resulted in a significant improvement in the performance of the original algorithm in terms of convergence speed, accuracy and robustness. The performance of the proposed cooperative variants are compared to the original BFO, the standard PSO, and a real-coded GA on a set of 4 widely-used benchmark functions, demonstrating their superiority.