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Convergence profile of the conventional Moea/d w.r.t. population size (µ).

Convergence profile of the conventional Moea/d w.r.t. population size (µ).

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This paper intends to understand and to improve the working principle of decomposition-based multi-objective evolutionary algorithms. We review the design of the well-established Moea/d framework to support the smooth integration of different strategies for sub-problem selection, while emphasizing the role of the population size and of the number o...

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Context 1
... start our analysis by studying the impact of the population size for the conventional Moea/d, that is Moea/d-(μ, μ, sps All ) following our terminology. In Fig. 1, we show the convergence profile using different μ values for the considered instances. Recall that hypervolume is measured on the external ...
Context 2
... in particular for some instance types. Hence, we complement our analysis by studying the sensitivity of the sps Rnd strategy, which was found to have the best anytime behavior overall, w.r.t the population size μ. Results for sps Rnd with λ = 1 are reported in Fig. 4. In contrast with the sps All strategy from the conventional Moea/d reported in Fig. 1, we can clearly see that the anytime behavior underlying sps Rnd is much more stable. In fact, the hypervolume increases with μ, independently of the considered budget and instance type. Notice also that when using small μ values, convergence occurs much faster for smooth problems (K = 0) compared against rugged ones (K = 4). This ...

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