A seminar on Multi-objective Fitness Dependent Optimizer or MOFDO
Dr. Jaza Abdulla, one of the faculty members of the computer science department, presented a seminar entitled “Multi-objective Fitness Dependent Optimizer or MOFDO” on Wednesday, 29th.
He introduced MOFDO as a multi-objective mode of his recent work; a novel single objective fitness dependent optimizer (FDO), which is inspired by the bee reproductive swarming process. He explained that MOFDO is well-thought as a cultural algorithm, and therefore, all five types of knowledge (situational, normative, topographical, domain, and historical knowledge) are considered during the implementation. For the performance-proof purpose, MOFDO tested on two standard benchmarks: classical ZDT test functions and CEC 2019 multimodal multi-objective test functions.
He demonstrated that MOFDO results have been 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 overall comparison shows the superiority of MOFDO in the majority of the cases and comparative results in other cases. Moreover, MOFDO is used for optimizing real-world engineering problems (e.g. welded beam design problem); the proposed algorithm successfully tackles the problem and provides a wide variety of well-distributed feasible solutions, which enable the decision-makers to have more applicable-comfort choices to consider.