In evolutionary multi-objective optimization (EMO), the pair-potential energy functions (PPFs)
have been used to construct diversity-preserving mechanisms to improve Pareto front …

In this work we are interested in stochastic particle methods for multi-objective optimization.
The problem is formulated via scalarization using parametrized, single-objective sub …

Multi-objective evolutionary algorithms (MOEAs) have shown a good capability to
approximate the Pareto front (PF) of complex multi-objective optimization problems (MOPs) …

FML (Fiber metal laminate) is widely used in aerospace as an advanced composite material.
Metal hybrid bistable composites are one type of FML structure. The hybrid bistable …

Abstract Generating Pareto Front Approximations with good convergence, uniformity, and
spread regardless of the geometry of the Pareto Front remains as an open problem. Many …

Reference sets generated with uniformly distributed weight vectors on a unit simplex are
widely used by several multi-objective evolutionary algorithms (MOEAs). They have been …

Achieving uniform Pareto front (PF) approximations across various PF geometries and
dimensions is a significant challenge. Most multi-objective evolutionary algorithms (MOEAs) …

Scalarization allows to solve a multi‐objective optimization problem by solving many single‐
objective sub‐problems, uniquely determined by some parameters. In this work, several …

In recent years, evolutionary computation has signif-icantly advanced in processes related to
machine learning. How-ever, the reciprocal integration of machine learning techniques into …

Stochastic particle methods in optimization constitute a popular class of heuristic techniques
where a set of possible solutions is iteratively updated according to deterministic and …