Barnacle Growth Algorithm (BGA): A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Metaheuristic algorithms are an important area of artificial intelligence research and a popular method for solving complex optimization problems. In this paper, we propose a new metaheuristic algorithm called the Barnacle Growth Algorithm (BGA). BGA simulates three stages of barnacle growth, namely planktonic stage, exploratory stage, and mature stage. In three stages, particles will have different behaviors and parameters. In the first process, BGA will find as many local optimal solutions as possible in the solution space. In the second process, it finds the global optimal solution around. In the third stage, BGA will re-explore the surrounding area for some search individuals to ensure that they will not fall into local optimum. The convergence speed performance of BGA is tested using the benchmark function of CEC 2013 by experiments. Experiment results are shown that BGA has better convergence speed and search ability on unimodal functions and multimodal functions, compared with the same type optimization algorithms. Applying the BGA algorithm to the optimization problem of city power transmission network, it is verified that the BGA algorithm can be applied to real problems. At the same time, the experimental results are shown that transmission route generated by the BGA algorithm can achieve the lowest power loss.