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: firstname.lastname@example.org).
Mhand Hifi is with EPROAD UR 4669, UPJV, Amiens, France (Corresponding author; e-mail: email@example.com).
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.Copyright @ 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).