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Multi-objective algorithms are aimed to obtain a set of solutions, called Pareto set, covering the whole Pareto front, i.e. the representation of the optimal set of solutions. To this end, the algorithms should yield a wide amount of near-optimal solutions with a good diversity or spread along this front. This work presents a study on different coarse-grained distribution schemes dealing with Multi-Objective...
In this work we propose a fine grained approach with self-adaptive migration rate for distributed evolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach...
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