Task scheduling using multi-objective particle swarm optimization with hamming inertia weight

Abstract

Task scheduling in a distributed environment is an NP-hard problem. A large amount of time is required for solving this NP-hard problem using traditional techniques. Heuristics/meta-heuristics are applied to obtain a near optimal solution within a finite duration. Discrete Particle Swarm Optimization (DPSO) is a newly developed meta-heuristic population-based algorithm. The performance of DPSO is significantly affected by the control parameter such as inertia weight. The new inertia weight based on hamming distance is presented in this paper in order to improve the searching ability of DPSO. Make span, mean flow time, and reliability cost are performance criteria used to assess the effectiveness of the proposed DPSO algorithm for scheduling independent tasks on heterogeneous computing systems. Simulations are carried out based on benchmark ETC instances to evaluate the performance of the algorithm.