Systematic Parameter Tuning For Multi-Objective Optimization Problems Through Statistical Experimental Design
Keywords:
Statistical Modelling, Multi-Objective Optimization Problem, Parameter Adjustment, Design Expert MethodAbstract
Multi-objective optimization (MOO) problems represent a pervasive challenge across diverse scientific and engineering disciplines, necessitating the simultaneous consideration and reconciliation of multiple, often conflicting, performance criteria. Unlike single-objective optimization, which seeks a unique optimal solution, MOO aims to identify a set of Pareto-optimal solutions that represent the most favorable trade-offs among competing objectives. Conventional optimization methodologies frequently fall short in adequately addressing the inherent complexities of MOO, leading to sub-optimal outcomes or an incomplete understanding of the solution landscape. This comprehensive article meticulously explores a sophisticated framework for the statistical adjustment and refinement of parameters within multi-objective optimization paradigms, leveraging the robust capabilities of the Design Expert method, a cornerstone of Design of Experiments (DOE). We delve deeply into the theoretical underpinnings of MOO, critically analyze the inherent limitations of traditional solution approaches, and elucidate the profound benefits derived from integrating advanced statistical methodologies for a more rigorous and efficient parameter tuning process. The overarching objective of this research is to present a detailed, adaptable, and statistically sound methodology that significantly augments the accuracy, efficiency, and robustness of identifying truly optimal solutions in complex multi-objective environments.
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Copyright (c) 2025 Dr. Sofia Petrova, Prof. Ivan Dimitrov
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