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Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin(2/x) with infinite collapses within the finite region[-1,1] to some representative iterative chaotic maps with finite collapses (e.g., Logistic map, Tent map, and Chebyshev map), a new adaptive mutative scale chaos optimization algorithm (AMSCOA) is proposed by using the chaos model x = sin(2/x). In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken: 1) the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2) the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.
Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin (2 / x) with infinite collapses within the finite region [-1,1] to some representative iterative chaotic maps with finite collapses (eg, Logistic map , Tent map, and Chebyshev map), the new adaptive mutative scale chaos optimization algorithm (AMSCOA) is proposed by using the chaos model x = sin (2 / x). In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are: 1) the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2) the most circle time is viewed as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high hovering speed and precision.