Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential ...

In real world applications, there are many multi-objective optimization problems. Most existing multi-objective optimization algorithms focus on improving the diversity, spread and convergence of the ...

It has been shown that non-convex optimization problems can be transformed into better structured problems through monotonic transformations of the objective functions. This work proposes Pairing ...

Multi-objective optimization (MOO) is a branch of optimization that deals with problems that have more than one objective function to optimize simultaneously. For example, if you are building a ...

1. Effective decision-making in environmental management requires the consideration of multiple objectives that may conflict. Common optimization methods use weights on the multiple objectives to ...

For efficient multi-start optimization, TPTD leverages a target point defined in the objective space, which plays a critical role in shaping the scalarized function.