Multi-Objective Optimization Using Bat Algorithm to Solve Multiprocessor Scheduling and Workload Allocation Problem

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Abstract

In this paper, a new heuristic called bat intelligence (BI) is introduced for solving energy aware multiprocessor scheduling problems. Bat intelligence is a novel optimization method that models prey hunting behaviors of bats. Bat intelligence and genetic algorithm (GA) are used to solve single-objective multiprocessor scheduling problem using, makespan, tardiness, and energy consumption as objective functions. Bat intelligence shows considerable improvement in terms of solution quality when compared with GA. Different combinations of these objectives are used to solve bi-objective multiprocessor scheduling problems, (makespan vs. energy, and also tardiness vs. energy). Tri-objective multiprocessor scheduling problem is also presented at the end. To generate desirable efficient alternatives, a Normalized Weighted Additive Utility Function is used. Simulation shows that BI identifies a set of efficient solutions that correspond to the assigned weights. The computational simulation also shows conflicting relationships between makespan and energy, and also between tardiness and energy
Translated title of the contributionMulti-Objective Optimization Using Bat Algorithm to Solve Multiprocessor Scheduling and Workload Allocation Problem
Original languageAmerican English
Article number10
Pages (from-to)41-51
Number of pages10
JournalComputer Science and Information Systems
Volume2
Issue number2
StatePublished - 15 Jun 2015

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