Abstract—In this paper, we solve the bi-objective scheduling problem on two dedicated processors with an evolutionary algorithm. The algorithm incorporates a look-ahead-based path-relinking as a learning strategy. The designed algorithm first determines a starting archive set by applying a knapsack procedure tailored for the scheduling. Second, an adaptation of the dominating local search, combined with exchange operators, is considered for generating a series of new non-dominated solutions that enrich the reference archive set. Third, a lookahead strategy-based path-relinking is added to the algorithm for iteratively highlighting the final Pareto front. A preliminary experimental part is given, where the performance of the method is evaluated on a set of benchmark instances extracted from the literature. Its results are compared to those achieved by the best methods of the literature. New results are obtained.
Index Terms—Bi-objective, evolutionary, look-ahead, scheduling.
Fatma Zohra Baatout is with LaROMaD, USTHB, Algeria (e-mail: fbaatout@usthb.dz).
Mhand Hifi is with EPROAD UR 4669, UPJV, Amiens, France (Corresponding author; e-mail: hifi@u-picardie.fr).
Cite: Fatma Zohra Baatout and Mhand Hifi, "A Look-Ahead operator as a Learning Strategy for Solving Bi-objective Scheduling Multiprocessor Tasks on Two Dedicated Processors," International Journal of Machine Learning and Computing vol. 12, no. 6, pp. 306-311, 2022.
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