A parrot optimizer for solving multiobjective design sensor placement in helicopter main rotor blade

Various sectors and applications, including machine learning, data mining, operations research, economical problem, and science, can be structured as multi-objective optimization problems. This study introduces a novel multi-objective algorithm based on the recently developed parrot optimizer (PO) called MOPO. An external repository matrix i.e. "archive" is incorporated with the PO so that maintain the Pareto optimal solutions achieved. The MOPO utilizes the elitist non-dominated sorting, to maintain the diversity among the optimal set of solutions, further the mutate-leaders strategy is proposed to to strengthen the diversity of obtained Pareto solutions and mitigates the risk of local minima. The efficacy of the MOPO is assessed through optimizing two categories of multi-objective, include twenty benchmark test suite from the IEEE CEC'20, and real-world multi-objective design challenge, through optimizing the sensor placement in helicopter main rotor blade. The MOPO is compared against nine well-known, recent and robust multi-objective optimization algorithms. Various quantative and qualitative metrics are employed to conduct a comprehensive examination of the results; further the Friedman test and Wilcoxon test are applied on results of the four performance metrics i.e. PSP, HV, IGDf and IDGX, it demonstrates that the MOPO performed comparably to other algorithms on the most test methods, and achieved the first rank among other competitors. The Wilcoxon test exhibit the significant variance of MOPO rather competitors on p-value = 0.05. The MOPO takes average execution time less than MOSMA, SPEA2, MOPSO by 20% rate.