A hybrid genetically-bacterial foraging algorithm converged by particle swarm optimisation for global optimisation

The social foraging behaviour of Escherichia coli bacteria and the effectiveness of genetic operators have recently been combined to develop a hybridised algorithm for distributed optimisation and control. The classical algorithms have their importance in solving real-world optimisation problems. Hybridisation of two algorithms is gaining popularity among researchers to explore the area of optimisation. This paper proposes a novel algorithm which hybridises the best features of three basic algorithms, i.e., genetic algorithm (GA), bacterial foraging (BF) and particle swarm optimisation (PSO) as genetically bacterial swarm optimisation (GBSO). The hybridisation is carried out in two phases; first, the diversity in searching the optimal solution is increased using selection, crossover and mutation operators. Secondly, the search direction vector is optimised using PSO to enhance the convergence rate of the fitness function in achieving the optimality. The proposed algorithm is tested on a set of functions which are then compared with the basic algorithms. Simulation results were reported and the proposed algorithm indeed has established superiority over the basic algorithms with respect to the set of functions considered and it can easily be extended for other global optimisation problems.