Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are\nevolutionary algorithms that have been used to solve both single and, to a less extent,multiobjective optimisation problems. In order\nto solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge,\ncircumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different\nevolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and\nthe third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem\n(BOUFLP), the biobjective version of the well-known un capacitated facility location problem. To the best of our knowledge, only\nfew articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact\nof the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important\nimprovements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II.\nThe conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained\nby each algorithm are compared using a hyper volume S metric.
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