通过模拟宇宙大爆炸过程构造一种新型智能优化算法——宇宙大爆炸搜索BBS算法。受经典最优化理论启发，提出“近似梯度”概念并构造“近似梯度爆炸”算子，得到基于“近似梯度”的宇宙大爆炸搜索算法AGBBS。AGBBS保留了基本BBS算法把候选解分布的均匀性和随机性相结合的优良特性，且充分利用了爆炸碎片的信息，提高了算法的搜索能力；通过改进一些启发性算子，提高了算法的收敛性和解的精度。通过对12个Benchmark标准函数的测试及与其他算法对比，验证了该算法的有效性和改进算法的鲁棒性。

A new intelligence optimization，Big Bang Search (BBS)，is proposed by simulating the big bang process. Inspired by the classical optimization method, the concept of “Approximate Gradient” is defined and the “Approximate Gradient Explosion” (AGE) operator is created，and an improved method called Approximate Gradientbased Big Bang Search (AGBBS) is proposed. AGBBS keeps down the excellent feature of BBS, the nice combination of uniformity and randomness of distributed candidate solutions; it fully uses the information of explosive pieces, which enhances the algorithm’s search ability. By improving some heuristic operators, the convergence of the algorithm and the accuracy of solutions are improved. The testing of 12 standard benchmark functions and a comparative analysis demonstrate the effectiveness of the new algorithm and the robustness of the AGBBS.