Hybrid approach based on cuckoo optimization algorithm and genetic algorithm for task scheduling

One of the most important issues in designing efficient scheduling algorithms in heterogeneous distribution systems is the reduction of execution time. In the proposed algorithm, the modified operators of the cuckoo optimization algorithm and the genetic algorithm are used to achieve a relatively optimal solution with fewer repetitions of the genetic algorithm and less execution time than the cuckoo optimization algorithm. The most important innovation in the proposed algorithm is the introduction of a new operator called spiral search, which increases the variety among the samples produced in each generation. The main idea of this operator is to replace linear search with the spiral search, which allows local search between similar schedules and accelerates the achievement of a relatively optimal answer. Also the multi objective function in the proposed algorithm is used to minimize makespan and maximize parallelization. The results obtained from the proposed algorithm on a large number of standard graphs with a various range of attributes show that it is superior to the other task scheduling algorithms.