Existing methods for binary fuzzing are difficult to dive into programs to find vulnerabilities. To address this problem, this study proposes a multi-angle optimization method integrating hardware-assisted program tracing, static analysis, and concolic execution. Firstly, static analysis and hardware-assisted tracing are used to calculate program path complexity and execution probability. Then, seed selection and mutation energy allocation are performed according to the path complexity and execution probability. Meanwhile, concolic execution is leveraged to assist seed generation and record key bytes for targeted variations. Experimental results show that this method finds more program paths as well as crashes in most cases, compared to other fuzzing methods.