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

EasyChair Preprint no. 1370

Abstract

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 L´evy 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.

Keyphrases: adaptive step size, Bacterial Foraging, Bacterial Foraging Optimization, Bacterial Foraging Optimization Algorithm, BFO, BFO Algorithm, conjugation, conjugation strategy, improved bfo algorithm, L´evy flight

@Booklet{EasyChair:1370,
  author = {Ming Chen and Xiaojun Qiu and Hong Wang},
  title = {An Effective Bacterial Foraging Optimization Based on Conjugation and Novel Step-Size Strategies},
  howpublished = {EasyChair Preprint no. 1370},

  year = {EasyChair, 2019}}