TEAM problem 22 approached by a hybrid artificial life method

Purpose - The purpose of this paper is to apply a hybrid algorithm based on the combination of two heuristics inspired by artificial life to the solution of optimization problems. Design/methodology/approach - The flock-of-starlings optimization (FSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the BCA has been used to refine the FSO-found solutions, thanks to its better performances in local search. Findings - A good solution of the 8-th parameters version of the TEAM problem 22 is obtained by using a maximum 200 FSO steps combined with 20 BCA steps. Tests on an analytical function are presented in order to compare FSO, PSO and FSO + BCA algorithms. Practical implications - The development of an efficient method for the solution of optimization problems, exploiting the different characteristic of the two heuristic approaches. Originality/value - The paper shows the combination and the interaction of stochastic methods having different exploration properties, which allows new algorithms able to produce effective solutions of multimodal optimization problems, with an acceptable computational cost, to be defined.