  This paper proposes the multi objective variant of the recently introduced
fitness dependent optimizer (FDO). The algorithm is called a Multi objective
Fitness Dependent Optimizer (MOFDO) and is equipped with all five types of
knowledge (situational, normative, topographical, domain, and historical
knowledge) as in FDO. MOFDO is tested on two standard benchmarks for the
performance-proof purpose; classical ZDT test functions, which is a widespread
test suite that takes its name from its authors Zitzler, Deb, and Thiele, and
on IEEE Congress of Evolutionary Computation benchmark (CEC 2019) multi modal
multi objective functions. MOFDO results are compared to the latest variant of
multi objective particle swarm optimization (MOPSO), non-dominated sorting
genetic algorithm third improvement (NSGA-III), and multi objective dragonfly
algorithm (MODA). The comparative study shows the superiority of MOFDO in most
cases and comparative results in other cases. Moreover, MOFDO is used for
optimizing real-world engineering problems (e.g., welded beam design problems).
It is observed that the proposed algorithm successfully provides a wide variety
of well-distributed feasible solutions, which enable the decision-makers to
have more applicable-comfort choices to consider.
