Over the recent years, continuous optimization has significantly evolved to become the
mature research field it is nowadays. Through this process, evolutionary algorithms had an …

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

Many important engineering optimization problems require a strong and simple optimization
algorithm to achieve the best solutions. In 2020, Rao introduced three non-parametric …

Engineering optimization problems usually involve computationally expensive simulations
and massive design variables. Solving these problems in an efficient manner is still a big …

Non-parametric tests can determine the better of two stochastic optimization algorithms
when benchmarking results are ordinal—like the final fitness values of multiple trials—but for …

Mean-Variance Mapping Optimization (MVMO) is a recent addition to the heuristic
optimization field. The main traits of its evolutionary mechanism reside in the adoption of a …

Surrogates are essential in surrogate-assisted evolutionary algorithms (SAEAs) for solving
expensive optimization problems. Gaussian processes (GPs) are often used as surrogates …

Comparing various metaheuristics based on an equal number of objective function
evaluations has become standard practice. Many contemporary publications use a specific …

This paper presents an on-line variable-fidelity surrogate-assisted harmony search
algorithm (VFS-HS) for expensive engineering design optimization problems. VFS-HS …

Surrogate-assisted evolutionary algorithms (SAEAs) prevail in the solution of
computationally expensive optimization problems. However, with the growth of problem …