Decision making for multi‐objective problems: Mean and median metrics

When dealing with problems more than two objectives, sophisticated multi-objective optimization algorithms might be needed. Pareto optimization, which is based on the concept of dominated and non-dominated solutions, most widely utilized method when comparing solutions within a setting. However, in context where three or objectives are involved, effectiveness dominance approaches to drive convergence significantly compromised as tend by each other. This turn reduces selection pressure, especially for that rely evolving population such evolutionary algorithms, particle swarm differential evolution, etc. The size set trade-off can quite large, rendering decision-making process difficult if not impossible. solution increases exponentially an increase number objectives. paper aims expand framework coping many/multi-objective multidisciplinary through introduction min-max metric behaves like median measure locate center data set. We compare this Chebyshev norm L_∞ mean locating introduced first time, unlike independent normalization method. These metrics advocate balanced, natural, minimum compromise about all also demonstrate behavior Tradespace case study involving 1200 CubeSat design alternatives identifying manageable potential decision-makers.