Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria, but it is difficult to optimize to get a high precision due to the randomness of the bacterial behavior, which belongs to intelligence algorithm. This paper presents an extended BFO algorithm, namely the Cooperative Bacterial Foraging Optimization (CBFO), which sign...

The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimization method that is implemented in this research to obtain the best kinetic parameter value. The proposed algorithm is then used to model tyrosine production in mus musculus (mouse) by using a dataset, JAK/STAT (Janus Kinase Signal Transducer and Activator of Transcrip...

The Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a bio-inspired algorithm that emulates the foraging behavior of E. Coli bacteria to solve optimization problems. JMetaBFOP (Bacterial foraging-based METAheuristics For Problems) framework implementing TS-MBFOA processes as library problems with preloaded constraints or defined by end user. This paper presents framewor...

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this paper, a computationally effective approach of combining bacterial foraging strategy with particle swarm optimization for solving the minimum makespan problem of job shop scheduling is proposed. In the artificial bacterial foraging system, a novel chemotactic model is designe...

The bacterial foraging optimization (BFO) algorithm is a popular stochastic, population-based optimization technique that can be applied to a wide range of problems. Two are the major issues the BFO algorithm is confronted with: first, the foraging mechanism of BFO might in some cases induce the attraction of bacteria gathered near the global optimum by bacteria gathered to local optima, thus s...

According to the fact that parameter selection of support vector machine(SVM) for fault diagnosis is difficult, a new method based on bacterial foraging algorithm(BAF) for support vector machine parameter optimization was proposed , then the faster optimization of the parameters C and RBF kernel parameter γ was performed. The crack rotor as the experiment object, firstly, AE signal of rotors wi...