Mitra Hashemi’s research while affiliated with Islamic Azad University, Tehran and other places

The UMDA algorithm is a type of Estimation of Distribution Algorithms. This algorithm has better performance compared to others such as genetic algorithm in terms of speed, memory consumption and accuracy of solutions. It can explore unknown parts of search space well. It uses a probability vector and individuals of the population are created through the sampling. Furthermore, EO algorithm is suitable for local search of near global best solution in search space, and it dose not stuck in local optimum. Hence, combining these two algorithms is able to create interaction between two fundamental concepts in evolutionary algorithms, exploration and exploitation, and achieve better results of this paper represent the performance of the proposed algorithm on two NP-hard problems, multi processor scheduling problem and graph bi-partitioning problem.

Proceedings of the 3rd International Conference on Computer Technology and Development (ICCTD 2011) held in Chengdu, China during November 25–27, 2011. The aim objective of ICCTD 2011 is to provide a platform for researchers, engineers, academicians as well as industrial professionals from all over the world to present their research results and development activities in Computer Technology and Development . This conference provides opportunities for the delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration.

UMDA algorithm is a type of Estimation of Distribution Algorithms. This algorithm has better performance compared to others such as genetic algorithm in terms of speed, memory consumption and accuracy of solutions. It can explore unknown parts of search space well. It uses a probability vector and individuals of the population are created through the sampling. Furthermore, EO algorithm is suitable for local search of near global best solution in search space, and it does not stuck in local optimum. Hence, combining these two algorithms is able to create interaction between two fundamental concepts in evolutionary algorithms, exploration and exploitation, and achieve better results of this paper is used adaptive version of  -EO algorithm called EO-LA. In this method the task of choosing a replacement component is assigned to Learning Automata. During the implementation of this algorithm, according to the suitability of produced solutions, feedback signals are sent to Learning Automata until adapt selected replacement component well. In this paper, results represent the better performance of the proposed algorithm (combination of three methods) on a Graph Bi-partitioning, NP-hard problem.