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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, non-dominated sorting genetic algorithm third improvement (NSGA-III), and multi-objective dragonfly algorithm. 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. [ABSTRACT FROM AUTHOR]

Data-driven control methods are strong tools due to their predictions for controlling the systems with a nonlinear dynamic model. In this paper, the Koopman operator is used to linearize the nonlinear dynamic model. Generating the Koopman operator is the most important part of using the Koopman theory. Dynamic mode decomposition (DMD) is used to obtain eigenfunction for producing the Koopman operator. Then, a fractional order PID (FOPID) controller is applied to control the linearized dynamic model. A swarm intelligence bat optimization algorithm is utilized to tune the FOPID controller's parameters. Simulation results on micro-electromechanical systems (MEMS) gyroscope under conventional PID controller, FOPID, Koopman-based FOPID controller (Koopman-FOPID), and Koopman-FOPID control optimized by bat algorithm (Koopman-BAFOPID) show that the proposed Koopman-BAFOPID controller has better performance in comparison with three other controllers in terms of high tracking performance, low tracking error, and low control efforts. [ABSTRACT FROM AUTHOR]