A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global problems due to its single migration mechanism and random mutation operator. To overcome these limitations, this paper propose a dual BBO based on sine cosine algorithm (SCA) dynamic hybrid mutation, named SCBBO. Firstly, the Latin hypercube sampling method is innovatively used improve initial population ergodicity. Secondly, nonlinear transformation parameter inertia weight adjustment factor are designed into position update formula of SCA make SCBBO suitable for high dimensional environments. Then, operator by combining Laplacian Gaussian which helps escape from local optima balance exploration exploitation. Finally, learning strategy integrated, so convergence accuracy further improved generating individuals. Meanwhile, A sequence model established prove can converge optimal solution with probability 1. Compared other state-of-the-art evolutionary algorithms, improves speed problems. show superiority SCBBO, performance compared 1000, 2000, 5000 10000 dimensions, respectively. The comparsions that SCBBO’s results dimensions basically same. also applied engineering design problems, simulation demonstrate proposed effective constrained