An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning

Abstract

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.

A hybrid approach to artificial bee colony algorithm

“…• Adaptive bacterial foraging optimization algorithm with life cycle and social learning (BFOLS) [41].…”

“…PABC uses the similar local search strategy with HABCPowell's method to improve the exploitation ability [18]. BFOLS is an enhanced BFO variant using similar lifecycle strategy with HABC [41]. EGA is the classical genetic algorithm with elitist selection scheme [5].…”

“…For the other specific parameters for involved algorithms, we can follow parameters settings of the original literatures of ABC [8], PABC [18], BFOLC [41], PSO [5], EGA [5], CMA-ES [6], BIPOP-CMA-ES [42], MPSO [43] and CCEA [44], as summarized in Table 3. For the proposed HABC, corresponding parameters can be set as following: The inertia coefficient g = 0.6.…”

A restructured artificial bee colony optimizer combining life-cycle, local search and crossover operations for droplet property prediction in printable electronics fabrication

“…All the control parameters for the involved algorithms are set to be default of their original literatures: the limit parameter of ABC and PABC is set to be S N × D, where D is the dimension of the problem and SN is the number of employed bees while Tp factor for PABC is equal to D ). For BFOLS, as referred in Yan et al (2012), we take Ns = 4 and Pe = 0.25 as recommended in original BFO, started step Cs = 0.1 (Ub-Lb) and ended step Ce = 0.00001 (Ub-Lb), where Lb and Ub refer to the lower bound and upper bound of the variables of the problems;…”

“…Moreover, we investigate an interesting realworld application of the HABC scheme to solve the optimal droplet property prediction (ODPP) problem, which focusing on minimizing the error between the desired droplet volume/velocity and simulated droplet volume/velocity. The proposed HABC has been compared with its two counterparts, namely the classical ABC algorithm ) and ABC algorithm with Powell's method (PABC) , and the very popular evolutionary and swarm intelligence algorithms known as genetic algorithm with elitism (EGA) (Sumathi et al 2008), covariance matrix adaptation evolution strategy (CMA-ES) (Hansen and Ostermeier 2001), particle swarm optimization with constriction factor (PSO) (Clerc and Kennedy 2002), and adaptive bacterial foraging optimization with lifecycle and social learning (BFOLS) (Yan et al 2012), over both benchmarks and the real-world ODPP problem with respect to the statistical performance measures of solution quality and convergence speed.…”