  In solving multi-modal, multi-objective optimization problems (MMOPs), the
objective is not only to find a good representation of the Pareto-optimal front
(PF) in the objective space but also to find all equivalent Pareto-optimal
subsets (PSS) in the variable space. Such problems are practically relevant
when a decision maker (DM) is interested in identifying alternative designs
with similar performance. There has been significant research interest in
recent years to develop efficient algorithms to deal with MMOPs. However, the
existing algorithms still require prohibitive number of function evaluations
(often in several thousands) to deal with problems involving as low as two
objectives and two variables. The algorithms are typically embedded with
sophisticated, customized mechanisms that require additional parameters to
manage the diversity and convergence in the variable and the objective spaces.
In this letter, we introduce a steady-state evolutionary algorithm for solving
MMOPs, with a simple design and no additional userdefined parameters that need
tuning compared to a standard EA. We report its performance on 21 MMOPs from
various test suites that are widely used for benchmarking using a low
computational budget of 1000 function evaluations. The performance of the
proposed algorithm is compared with six state-of-the-art algorithms (MO Ring
PSO SCD, DN-NSGAII, TriMOEA-TA&R, CPDEA, MMOEA/DC and MMEA-WI). The proposed
algorithm exhibits significantly better performance than the above algorithms
based on the established metrics including IGDX, PSP and IGD. We hope this
study would encourage design of simple, efficient and generalized algorithms to
improve its uptake for practical applications.
