An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem |
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Authors: | Mohsen Ebrahimi Moghaddam Mohammad Reza Bonyadi |
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Affiliation: | (1) School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA; |
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Abstract: | Multiprocessor task scheduling is an important problem in parallel applications and distributed systems. In this way, solving
the multiprocessor task scheduling problem (MTSP) by heuristic, meta-heuristic, and hybrid algorithms have been proposed in literature. Although the problem has been addressed
by many researchers, challenges to improve the convergence speed and the reliability of methods for solving the problem are
still continued especially in the case that the communication cost is added to the problem frame work. In this paper, an Immune-based
Genetic algorithm (IGA), a meta-heuristic approach, with a new coding scheme is proposed to solve MTSP. It is shown that the proposed coding reduces the search space of MTSP in many practical problems, which effectively influences
the convergence speed of the optimization process. In addition to the reduced search space offered by the proposed coding
that eventuate in exploring better solutions at a shorter time frame, it guarantees the validity of solutions by using any
crossover and mutation operators. Furthermore, to overcome the regeneration phenomena in the proposed GA (generating similar
chromosomes) which leads to premature convergence, an affinity based approach inspired from Artificial Immune system is employed
which results in better exploration in the searching process. Experimental results showed that the proposed IGA surpasses related works in terms of found makespan (20% improvement in average) while it needs less iterations to find the
solutions (90% improvement in average) when it is applied to standard test benches. |
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