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A novel iterative optimization algorithm based on dynamic random population

Seyyed Meysam Hosseini ; Department of Communication Engineering, Faculty of Electrical Engineering, Shahid Beheshti University, Evin, 19839, Tehran, Iran
Hamid Reza Mirsalari ; Shoushtar Branch, Islamic Azad University, Shoushtar, Iran
Hossein Pourhoudhiary ; Khozestan Telecom Corporation, Iran

Puni tekst: hrvatski pdf 554 Kb

str. 27-33

preuzimanja: 303


Puni tekst: engleski pdf 554 Kb

str. 27-33

preuzimanja: 587



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.

Ključne riječi

dynamic random population, heuristic search algorithm, optimization

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