An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms.

[1]  Yichuan Shao,et al.  Cooperative Bacterial Foraging Optimization , 2009, 2009 International Conference on Future BioMedical Information Engineering (FBIE).

[2]  Dong Hwa Kim,et al.  A hybrid genetic algorithm and bacterial foraging approach for global optimization , 2007, Inf. Sci..

[3]  Haifeng Guo,et al.  Lifecycle-based Swarm Optimization Method for Constrained Optimization , 2011, J. Comput..

[4]  Amit Konar,et al.  On Stability of the Chemotactic Dynamics in Bacterial-Foraging Optimization Algorithm , 2009, IEEE Trans. Syst. Man Cybern. Part A.

[5]  Xiaohui Yan,et al.  A new approach for data clustering using hybrid artificial bee colony algorithm , 2012, Neurocomputing.

[6]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[7]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[8]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[9]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[10]  Francisco Herrera,et al.  A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability , 2009, Soft Comput..

[11]  Yunlong Zhu,et al.  Cooperative Bacterial Foraging algorithm for global Optimization , 2009, 2009 Chinese Control and Decision Conference.

[12]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[13]  Ganapati Panda,et al.  Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques , 2009, Expert Syst. Appl..

[14]  Q. H. Wu,et al.  A lifecycle model for simulating bacterial evolution , 2008, Neurocomputing.

[15]  D J DeRosier,et al.  The Turn of the Screw: The Bacterial Flagellar Motor , 1998, Cell.

[16]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[17]  Bin Zhou,et al.  Gradient Methods with Adaptive Step-Sizes , 2006, Comput. Optim. Appl..

[18]  Ajith Abraham,et al.  Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis , 2009, IEEE Transactions on Evolutionary Computation.

[19]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[20]  H. Berg,et al.  Chemotaxis in Escherichia coli analysed by Three-dimensional Tracking , 1972, Nature.

[21]  Chunguo Wu,et al.  Improved Bacterial Foraging Algorithms and Their Applications to Job Shop Scheduling Problems , 2007, ICANNGA.

[22]  Ajith Abraham,et al.  Stability analysis of the reproduction operator in bacterial foraging optimization , 2008, CSTST.

[23]  Ajith Abraham,et al.  Stability analysis of the reproduction operator in bacterial foraging optimization , 2010, Theor. Comput. Sci..

[24]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[25]  Ajith Abraham,et al.  Automatic circle detection on digital images with an adaptive bacterial foraging algorithm , 2010, Soft Comput..

[26]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[27]  Hassan M. Emara,et al.  Bacterial foraging oriented by Particle Swarm Optimization strategy for PID tuning , 2009, CIRA.

[28]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[29]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[30]  Xiaohui Yan,et al.  A Hybrid Artificial Bee Colony algorithm for numerical function optimization , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[31]  陈瀚宁,et al.  Self-Adaptation in Bacterial Foraging Optimization Algorithm , 2008 .

[32]  K. Passino,et al.  Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors , 2002 .

[33]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[34]  Yunlong Zhu,et al.  A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm , 2010 .

[35]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.