A NOVEL ITERATIVE OPTIMIZATION ALGORITHM BASED ON DYNAMIC RANDOM POPULATION

Various heuristic optimization methods have been developed in artificial intelligence. These methods are mostly inspired by natural evolution or some applicable innovations, which seek good (near-optimal) solutions at a reasonable computational cost for search problems. A new iterative optimization algorithm is proposed in this paper. The algorithm is based on searching the most valuable part of the solution space, which is normally concentrated about a targeted bias vector (in the form of a dynamic random population). This algorithm greedily searches the solution space for global extremum. The comparison results between the proposed algorithm and some of the well-known heuristic search methods confirm the superiority of our proposed method in solving various non-linear optimization problems from the viewpoint of simplicity and accuracy.