A Biogeography-based Dual-Strategy Particle Swarm Algorithm for Numerical and Engineering Design Optimization

Recently, researchers developed numerous nature-inspired meta-heuristics algorithms, such as particle swarm optimization (PSO), to solve numerical and engineering optimization problems. However, the PSO still suffers from complex optimization problems, such as slow convergence speed and getting stuck in local optima. This paper proposes a Biogeography-based Dual-strategy PSO algorithm (DBPSO). Firstly, a Dual-Strategy Search (DSS) is proposed, which utilizes an improved random learning strategy for global search and a modified quasi-Newton search strategy for local search. The proportion of resources allocated to the modified quasi-Newton search strategy is associated with the inertia weight value of the improved random learning, achieving a balance between exploration and exploitation. Secondly, an improved migration operator of biogeography-based optimization is embedded in DSS. Finally, an iteration-based hybridization strategy that allows one-way information transmission is proposed to fuse the two algorithms effectively. Experiments are conducted on CEC2013 and CEC2017 test suites, and the results show that DBPSO ranks first by comparing it with multiple PSO variants and other meta-heuristics variants. In addition, experiments are conducted on two real-world engineering optimization problems to demonstrate the applicability of DBPSO in solving practical optimization problems, such as the car side impact design problem.