Abstract
This paper proposes a probabilistic local search based memetic algorithm for the task scheduling problem with the objective to minimize the maximum completion time, which is known to be NP-Hard problem. It has been proven to be NP-Complete for which optimal solutions can be found only after an exhaustive search. The main positive effect of the proposed approach is by choosing only good individuals as initial solutions for Local search thereby assigning an appropriate local search direction to each initial solution. The proposed probabilistic approach is compared with the non probabilistic memetic approach where tabu search act as local search. From these observations, it is found that the minimum local search probability will avoid the premature convergence of MA and also reduce the processing time rather than trapping into a local minima.
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© 2012 Springer-Verlag Berlin Heidelberg
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Padmavathi, S., MohitGolchha, S., SeeniMohamed, A. (2012). Memetic Algorithm Based Task Scheduling Using Probabilistic Local Search. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_27
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DOI: https://doi.org/10.1007/978-3-642-35380-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35379-6
Online ISBN: 978-3-642-35380-2
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