Multi-Objective Probability Collectives
نویسندگان
چکیده
We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using a Pareto-based ranking strategy as a single objective. We partially evaluate this via testing on a number of problems, and compare the results with state of the art alternatives. We find that this first multi-objective probability collectives (MOPC) approach performs competitively, indicating both clear promise, and clear room for improvement.
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