Butterfly optimization algorithm with fusion strategy improvement and its truss optimization
To address the problems of slow convergence and poor stability of the basic butterfly optimization algorithm(BOA),an improved butterfly optimization algorithm(CSBOA)that incorporates the crow search algorithm(CSA)is proposed.The CSBOA uses the global search mechanism of the crow search algorithm instead of the global search phase of the butterfly optimization algo-rithm to enhance its global exploration capability,and adds adaptive weights to balance the iteration steps of the global and local search of the algorithm to help the butterfly find food in a larger area.The performance of the algorithm is experimented and tested by 12 benchmark functions with different characteristics and one engineering design structure optimization problem.The study shows that the convergence speed and solution accuracy of CSBOA are significantly improved,which further verifies that CSBOA can efficiently solve high-dimensional benchmark functions and complex engineering application problems.
万方数据
维普
