An Adaptive Bumble Bees Mating Optimization algorithm

Abstract

The finding of the suitable parameters of an evolutionary algorithm, as the Bumble Bees Mating Optimization (BBMO) algorithm, is one of the most challenging tasks that a researcher has to deal with. One of the most common used ways to solve the problem is the trial and error procedure. In the recent few years, a number of adaptive versions of every evolutionary and nature inspired algorithm have been presented in order to avoid the use of a predefined set of parameters for all instances of the studied problem. In this paper, an adaptive version of the BBMO algorithm is proposed, where initially random values are given to each one of the parameters and, then, these parameters are adapted during the optimization process. The proposed Adaptive BBMO algorithm is used for the solution of the Multicast Routing Problem (MRP). As we would like to prove that the proposed algorithm is suitable for solving different kinds of combinatorial optimization problems we test the algorithm, also, in the Probabilistic Traveling Salesman Problem (PTSP) and in the Hierarchical Permutation Flowshop Scheduling Problem (HPFSP). Finally, the algorithm is tested in four classic benchmark functions for global optimization problems (Rosenbrock, Sphere, Rastrigin and Griewank) in order to prove the generality of the procedure. A number of benchmark instances for all problems are tested using the proposed algorithm in order to prove its effectiveness.