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See detailA New Parallel Multi-objective Cooperative Coevolutionary Algorithm Based on SPEA2
Dorronsoro, Bernabé UL; Cañero, J. Alberto UL; Danoy, Grégoire UL et al

in ALIO-INFORMS Joint International Meeting 2010 (2010)

We present in this work a new multi-objective cooperative coevolutionary algorithm based on SPEA2 (called CCSPEA2). In this algorithm, we split the solution chromosome into 4 different parts of the same ... [more ▼]

We present in this work a new multi-objective cooperative coevolutionary algorithm based on SPEA2 (called CCSPEA2). In this algorithm, we split the solution chromosome into 4 different parts of the same size, and 4 islands are optimizing every single partial solution by using SPEA2. For evaluating the solutions, the islands are sharing their best partial solutions. As a result, CCSPEA2 outperforms SPEA2 in most of tested problems. [less ▲]

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See detailMulti-objective Robust Static Mapping of Independent Tasks on Grids
Dorronsoro, Bernabé UL; Bouvry, Pascal UL; Cañero, J. Alberto UL et al

in World Conference in Computational Intelligence (WCCI) (2010)

We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is ... [more ▼]

We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is known. We are making the same assumption in this work, but we allow the existence of inaccuracies in these values. Our schedule will be robust versus such inaccuracies, ensuring that even when the estimated time to compute all the tasks is increased by a given percentage, the makespan of the schedule (i.e., the time when the last machine finishes its tasks) will not grow behind that percentage. We propose a new multi-objective definition of the problem, optimizing at the same time the makespan of the schedule and its robustness. Four well-known multi-objective evolutionary algorithms are used to find competitive results to the new problem. Finally, a new population initialization method for scheduling problems is proposed, leading to more efficient and accurate algorithms. [less ▲]

Detailed reference viewed: 92 (1 UL)