A global-best guided phase based optimization algorithm for scalable optimization problems and its application

Large scale optimization problems are more representative of real-world problems and remain one of the most challenging tasks for the design of new type of evolutionary algorithms. Very recently, a new meta-heuristic algorithm named Phase Based Optimization (PBO) inspired by the different motional features of individuals under three different phases (gas phase, liquid phase and solid phase) was proposed. In order to improve PBO for solving large scale optimization problems, an effective search strategy combining complete stochastic search (the diffusion operator) and global-best guided search (the improved perturbation operator) is utilized. The proposed strategy can provide well-balanced compromise between the population diversity (diversification) and convergence speed (intensification) especially in solving large scale optimization problems. We term the improved algorithm as Global-best guided PBO (GPBO) to avoid ambiguity. Seven well-known scalable benchmark functions and a real-world large scale transmission pricing problem are used to validate the performance of GPBO compared with some state-of-the-art algorithms. The experimental results demonstrate that GPBO can provide better solution accuracy and convergence ability in both large scale benchmark functions and real-world optimization problem.