A novel adaptive mutative scale optimization algorithm based on chaos genetic method and its optimization efficiency evaluation

By combing the properties of chaos optimization method and genetic algorithm, an adaptive mutative scale chaos genetic algorithm (AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [−1, 1]. Some measures in the optimization algorithm, such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline, were taken to ensure its speediness and veracity in seeking the optimization process. The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision. Furthermore, the average truncated generations, the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally. It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.