Like many optimizers, Bayesian optimization often falls short of gaining user trust due to
opacity. While attempts have been made to develop human-centric optimizers, they typically …

The mean–variance model (MV) can help companies in an economic downturn identify
portfolios that increase revenue while reducing risk. However, the MV optimization model …

The problem-solving task of the multi-criteria decision-making (MCDM) approach involves
decision makers'(DMs') interaction by incorporating their preferences to arrive at one or …

There are a lot of real-world black-box optimization problems that need to optimize multiple
criteria simultaneously. However, in a multi-objective optimization (MOO) problem …

Work-related musculoskeletal disorders are a major health issue often caused by awkward
postures. Identifying and recommending more ergonomic body postures requires optimizing …

The mean–variance (MV) model provides numerous optimal portfolios for managing a firm's
asset portfolio. Portfolio decisions in large corporations involve many interest groups, such …

Evolutionary multi-objective optimization (EMO) algorithms are widely used to solve
problems involving multiple conflicting objectives. In general, these problems result in a well …

When the decision maker (DM) has unknown preferences, the standard approach to a multi-
objective problem is to generate an approximation of the Pareto front and let the DM choose …

Learning problems in which multiple conflicting objectives must be considered
simultaneously often arise in various fields, including engineering, drug design, and …

Multi-objective optimization is a widely studied problem in diverse fields, such as
engineering and finance, that seeks to identify a set of non-dominated solutions that provide …