The Social Cognitive Optimization Algorithm: Modifiability and Application

The social cognitive optimization algorithm is one of the newest intelligent algorithms, and this algorithm can help the solvers to avoid tripping in local optimization when solving the nonlinear constraint problems effectively. The algorithm is based on the social cognitive theory and the key point of the ergodicity is the process of refreshing the knowledge points. Modified and optimized the conditions of neighborhood searching through bring in the Chaos and Kent mapping function to get more reasonable knowledge points which were distributed more uniform. Used the real nonlinear constraint problem to test the performance of the modified algorithm, through compared data, the later has advantages on the speed of convergence and the legitimacy, and the value of the target function is more closed to the theory value.