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A RankMOEA to approximate the pareto front of a dynamic principal-agent model

Abstract In this paper, a new Multi-Objective Evolutionary Algorithm (MOEA) named RankMOEA is proposed. Innovative niching and ranking-mutation procedures which avoid the need of parameters definition are involved; such procedures outperform traditional diversity-preservation mechanisms under spread-hardness situations. RankMOEA performance is compared with those of other state of the art MOEAs: MOGA, NSGA-II and SPEA2, showing remarkable improvements. RankMOEA is also applied to approximate the Pareto Front of a Dynamic Principal-Agent model with Discrete Actions posed in a Multi-Objective Optimization framework allowing to consider more powerful assumptions than those used in the traditional single-objective optimization approach. Within this new framework a set of feasible contracts is described, while others similar studies only focus on one single contract. The results achieved with RankMOEA show better spread and minor error than those obtained by already mentioned MOEAs, allowing to perform better economic analysis by characterizing contracts in the trade-off surface.