Abstract
This paper presents an improved version of a music-inspired meta-heuristic algorithm, Harmony Search (HS), for successfully solving the NP-complete task assignment problem (TAP) in distributed computing systems. Task assignment is an important and core step in distributed systems where program tasks must be properly allocated to the processors to effectively harness the computing power by better exploitation of the system parallelism and improving system performance. The proposed HS-based algorithm explores the search space effectively and efficiently by exploiting the factors of randomness, experience, and variation of experience. Our main contributions in this work are: introducing a modification in the pitch adjustment operator of the Harmony Search, mapping Harmony Search solutions to the clustering methodology which shows its superiority in solving TAP, and using a local refinement heuristic to improve a given assignment. The effectiveness of the proposed HS-based algorithm is demonstrated by comparing it with a recently reported Harmony Search algorithm, NGHS, and with a wide variety of other earlier reported meta-heuristic techniques such as GA, SA, PSO and many others, to solve the TAP. Simulation results indicate that the proposed HS-based algorithm is a viable approach for the TAP, which could find better quality (or even optimal) solutions within reasonable computation time.








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Salman, A., Ahmad, I., AL-Rushood, H. et al. Solving the task assignment problem using Harmony Search algorithm. Evolving Systems 4, 153–169 (2013). https://doi.org/10.1007/s12530-012-9058-1
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DOI: https://doi.org/10.1007/s12530-012-9058-1