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

Título traducido de la contribución: Multi-Objective Optimization Using Bat Algorithm to Solve Multiprocessor Scheduling and Workload Allocation Problem

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

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
Título traducido de la contribuciónMulti-Objective Optimization Using Bat Algorithm to Solve Multiprocessor Scheduling and Workload Allocation Problem
Idioma originalInglés estadounidense
Número de artículo10
Páginas (desde-hasta)41-51
Número de páginas10
PublicaciónComputer Science and Information Systems
Volumen2
N.º2
EstadoPublicada - 15 jun 2015

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