An Efficient Task Scheduling Technique in Heterogeneous Systems Using Self-Adaptive Selection-Based Genetic Algorithm

Abstract

Optimal scheduling of parallel tasks with some precedence relationship, onto a parallel machine is known to be NP-complete. The complexity of the problem increases when task scheduling is to be done in a heterogeneous environment, where the processors in the network may not be identical and take different amounts of time to execute the same task. We propose a new genetics-based approach to scheduling parallel tasks on heterogeneous processors. Our approach requires minimal problem specific information and no problem specific operators or repair mechanisms. Key features of our system include a flexible, adaptive problem representation and an incremental fitness function. The selection scheme used in our scheduling algorithm is designed to maintain the genetic diversity within the population by advantageous self adaptive steering of selection pressure. This self-adaptive mechanism referred to as progeny selection in which the fitness of an offspring is compared to the fitness of its own parents. The sufficient amount of 'successful' offspring will become the member of next generation. Comparison with traditional scheduling methods indicates that the new GA is competitive in terms of solution quality if it has sufficient resources to perform its search.

A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

“…e most used algorithm is the genetic algorithm (GA) [10]. Genetic algorithm is widely used in solving complex problems such as nonlinearity and optimization [11,12]. However, genetic algorithms are also flawed, such as the disadvantages of falling into local optimum and low computational efficiency when solving large-scale task scheduling [13].…”