The primary objective in multi-objective optimization (MOO) is to evolve a set of solutions which are as close
as possible to the Pareto-optimal (PO) front, while being as diverse as possible. In this article, a new evolutionary
MOO algorithm called “Multi-objective Genetic Algorithm with Relative Distance (MOGARD)” is proposed. MOGARD
uses the concept of relative distance parameter as the fitness function that helps in better convergence to the true
PO-front. A novel diversity parameter is used to ensure a wide spread of the solutions. MOGARD incorporates the
concept of elitism, using the archive concept as in SPEA2 [9].