Multi-objective fitness-dependent optimizer algorithm

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