In recent years, coverage-based greybox fuzzing (CGF) has become one of the most important techniques to discover security bugs. The existing fuzzers linearly allocate energy for each test case and repeatedly select the same seed for fuzzing, but those strategies proved to be inefficient and it has proved to be inefficient. Our experimental observations show that various test cases have diverse effectiveness, and the effectiveness of test cases changes increase with execution time.In this paper, we propose a novel yet lightweight energy allocation and seeds selection strategy, called AcoFuzz, to improve fuzzing efficiency. AcoFuzz has a following distinct advantage:Dynamically allocate energy for test cases based on computations to cope with their effectiveness variation.Extensive experiments based on real-world programs and the LAVA-M dataset have been conducted to evaluate the path discovery and vulnerability detection ability of AcoFuzz, which substantially outperforms 3 state-of-the-art fuzzers.