In solving complex optimization problems, intelligent optimization algorithms such as immune algorithm show better advantages than traditional optimization algorithms. Most of these immune algorithms, however, have disadvantages in population diversity and preservation of elitist antibodies genes, which will lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. By introducing the catastrophe factor into the ACAMHC algorithm, we propose a novel catastrophe-based antibody clone algorithm (CACA) to solve the above problems. CACA preserves elitist antibody genes through the vaccine library to improve its local search capability; it improves the antibody population diversity by gene mutation that mimics the catastrophe events to the natural world to enhance its global search capability. To expand the antibody search space, CACA will add some new random immigrant antibodies with a certain ratio. The convergence of CACA is theoretically proved. The experiments of CACA compared with the clone selection algorithm (ACAMHC) on some benchmark functions are carried out. The experimental results indicate that the performance of CACA is better than that of ACAMHC. The CACA algorithm provides new opportunities for solving previously intractable optimization problems.