Parallel Genetic Algorithm based on a new migration strategy

Parallel Genetic Algorithm (PGA) is used in many practical global optimizations to achieve high speed in convergence. The Island Model Parallel Genetic Algorithm (IMPGA) is very useful. Genetic Algorithms are one of the most powerful search and optimization method when we must solvecomplex and time consuming problems. IMPGA are more flexible than other PGA methods. There are several variables in the IMPGA that determining them are effective to enhance performance of the IMPGA. In this paper, we proposed a Migration method (Max-Min method). In our proposed method, according to status of subpopulation and comparing subpopulation with other subpopulations, the individuals for migrationare selected. In addition to enhancing the performance of PGA, we propose another method that embedding Hill-Climbing Algorithm within the structure of the PGA. As we know, creating an optimized structure for a Neural Network is a time consuming problem and costly one. The problem was studied in this paper is to determine the structure of a Neural Network forforecasting next day air quality. In addition, we used real data which was received from the Meteorological Organization and Tehran's Air Pollution Company. Output of the neural network is the value of Ozone Gas (o3) for the next 24 hours. The results of our two proposed methods are compared with conventional methods in other papers. Our algorithm has better performance than other papers.