A novel Multi-Objective Evolutionary Algorithm
(MOEA), called Multi-objective Genetic Algorithm with
Relative Distance (MOGARD) is described. A novel
relative distance parameter that ensures convergence to
the Pareto optimal front and a nearest neighbour based
method for maintaining diversity in the non-dominated set
is used. Two novel performance measures are formulated to
estimate the performance of the MOEAs. A penalty based
constraint handling concept is introduced in MOGARD,
for handling constraints. Experimental results demonstrate
the superiority of MOGARD on several test problems, as
compared to other recent and well known algorithms.