A new adaptive mutative scale chaos optimization algorithm and its application

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.

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.