A genetic algorithm for the vehicle routing problem
Computers and Operations Research
Recent Developments In Biologically Inspired Computing
Recent Developments In Biologically Inspired Computing
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Bacterial foraging oriented by particle swarm optimization strategy for PID tuning
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Adaptive Computational Chemotaxis in Bacterial Foraging Algorithm
CISIS '08 Proceedings of the 2008 International Conference on Complex, Intelligent and Software Intensive Systems
Stability of the chemotactic dynamics in bacterial foraging optimization algorithm
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Transmission loss reduction based on FACTS and bacteria foraging algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
IEEE Computational Intelligence Magazine
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation
IEEE Transactions on Evolutionary Computation
Bio-inspired algorithms for autonomous deployment and localization of sensor nodes
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
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In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) for distributed optimization and control. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. However, during the process of chemotaxis, the BFOA depends on random search directions which may lead to delay in reaching the global solution. Recently, a new algorithm BFOA oriented by PSO termed BF-PSO has shown superior in proportional integral derivative controller tuning application. In order to examine the global search capability of BF-PSO, we evaluate the performance of BFOA and BF-PSO on 23 numerical benchmark functions. In BF-PSO, the search directions of tumble behavior for each bacterium oriented by the individual's best location and the global best location. The experimental results show that BF-PSO performs much better than BFOA for almost all test functions. That's approved that the BFOA oriented by PSO strategy improve its global optimization capability.