An Effective Bacterial Foraging Optimization Based on Conjugation and Novel Step-Size Strategies

Bacterial Foraging Optimization (BFO) is an effective metaheuristic algorithm that has been widely applied to the real world. Despite outstanding computing functionality, BFO algorithms can barely avoid premature convergence induced by easy trapping in local optima. To improve the computing functionality of BFO algorithm, this paper presents an improved BFO algorithm that employs a novel step-length evolution strategy. Also, the improved BFO algorithm adopts Lévy flight strategy proposed in LPBFO and the conjugation strategy proposed in BFO-CC. By combining the three strategies associatedlly, the proposed Conjugated Novel Step-size BFO algorithm(CNSBFO) strikes an outstanding balance between exploitation and exploration, effectively mitigating the problem of premature convergence in BFO algorithm. Experimental results comparing with several similar algorithms on 8 benchmark functions are conducted to demonstrate the efficiency of the proposed CNSBFO algorithm. Keywords—BFO; Lévy flight; conjugation; adaptive step size

[1]  Mouayad A. Sahib,et al.  Improving bacterial foraging algorithm using non-uniform elimination-dispersal probability distribution , 2018, Alexandria Engineering Journal.

[2]  Chengjin Zhang,et al.  Bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy , 2018, Applied Intelligence.

[3]  Efrén Mezura-Montes,et al.  Improved Modified Bacterial Foraging Optimization Algorithm to Solve Constrained Numerical Optimization Problems , 2016 .

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

[5]  Baocai Yin,et al.  Bacterial foraging optimization using novel chemotaxis and conjugation strategies , 2016, Inf. Sci..

[6]  Ajith Abraham,et al.  A SYNERGY OF DIFFERENTIAL EVOLUTION AND BACTERIAL FORAGING OPTIMIZATION FOR GLOBAL OPTIMIZATION , 2007 .

[7]  Ali Kaveh,et al.  A NOVEL META-HEURISTIC ALGORITHM: TUG OF WAR OPTIMIZATION , 2016 .

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

[9]  Edgar Alfredo Portilla-Flores,et al.  Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid , 2019 .

[10]  Z. Beheshti.  A review of population-based meta-heuristic algorithm , 2013, SOCO 2013.

[11]  Xin-She Yang,et al.  Nature-Inspired Optimization Algorithms: Challenges and Open Problems , 2020, J. Comput. Sci..

[12]  D. Rekha,et al.  Bacterial Foraging Optimization-Based Clustering in Wireless Sensor Network by Preventing Left-Out Nodes , 2020, Intelligent Computing Paradigm.

[13]  Ben Niu,et al.  An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning , 2012 .

[14]  Liying Wang,et al.  An effective bacterial foraging optimizer for global optimization , 2016, Inf. Sci..

[15]  Ben Niu,et al.  Novel Bacterial Foraging Optimization with Time-varying Chemotaxis Step , 2011 .

[16]  R. Mantegna,et al.  Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[17]  Hong Wang,et al.  Bacterial Colony Optimization , 2012 .

[18]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[19]  Ben Niu,et al.  Structure-Redesign-Based Bacterial Foraging Optimization for Portfolio Selection , 2014, ICIC.

[20]  Emile H. L. Aarts,et al.  Performance of the simulated annealing algorithm , 1987 .

[21]  Hong Wang,et al.  Adaptive comprehensive learning bacterial foraging optimization and its application on vehicle routing problem with time windows , 2015, Neurocomputing.

[22]  M. Dorigo,et al.  The Ant Colony Optimization MetaHeuristic 1 , 1999 .

[23]  Ponnuthurai N. Suganthan,et al.  Bacterial foraging optimization algorithm in robotic cells with sequence-dependent setup times , 2019, Knowl. Based Syst..

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

[25]  Ben Niu,et al.  Coevolutionary Structure-Redesigned-Based Bacterial Foraging Optimization , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  S. Fong,et al.  Metaheuristic Algorithms: Optimal Balance of Intensification and Diversification , 2014 .

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

[28]  Ahmed Yousuf Saber,et al.  Economic dispatch using particle swarm optimization with bacterial foraging effect , 2012 .

[29]  M. Azzam,et al.  Design of PID Controller for Power System Stabilization Using Hybrid Particle Swarm-Bacteria Foraging Optimization , 2013 .

[30]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..