A novel chaos danger model immune algorithm

• We propose a novel chaos danger model immune algorithm (CDMIA).
• Chaos perturbation and chaos regeneration are used for updating the antibody.
• The chaos operator has effects on improving the population diversity.
• CDMIA outperforms some optimization algorithms when considering some benchmarks.
• CDMIA is suitable for global optimization.

Making use of ergodicity and randomness of chaos, a novel chaos danger model immune algorithm (CDMIA) is presented by combining the benefits of chaos and danger model immune algorithm (DMIA). To maintain the diversity of antibodies and ensure the performances of the algorithm, two chaotic operators are proposed. Chaotic disturbance is used for updating the danger antibody to exploit local solution space, and the chaotic regeneration is referred to the safe antibody for exploring the entire solution space. In addition, the performances of the algorithm are examined based upon several benchmark problems. The experimental results indicate that the diversity of the population is improved noticeably, and the CDMIA exhibits a higher efficiency than the danger model immune algorithm and other optimization algorithms.