Multiobjective evolutionary algorithm test suites

Multiobjective Evolutionary Algorithms (MOEAs) currently have no generic benchmark test suites. This paper provides several Multiobjective Optimization Problems (MOPs) for use as part of a standardized MOEA test suite, and proposes a methodology whereby various MOEAs can be directly compared. Supporting these contributions is a detailed discussion of MOP landscape and general test suite issues, and presentation of a new theorem defining the structural limitations of an MOPs global optimum. This paper also discusses high-performance computer software deterministically computing an MOPs Pareto front at a given computational resolution. 1 Introduction Multiobjective Evolutionary Algorithms (MOEAs) are now a well-established field within Evolutionary Computation. They were born in 1985 when Schaffer 16 and Fourman 6 implemented the first MOEAs dealing with Multiobjective Optimization Problems (MOPs). Since then, over 140 published papers propose various MOEA implementations and a...