Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

In this paper, a novel and powerful metaheuristic optimizer, named the growth optimizer
(GO), is proposed. Its main design inspiration originates from the learning and reflection …

Ship position prediction is the key to inland river and sea navigation warning. Maritime traffic
control centers, according to ship position monitoring, ship position prediction and early …

Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the
enhanced variants of velocity-free particle swarm optimizer with proven defects and …

Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve
computationally expensive problems with some success. However, traditional EAs are not …

Vegetation evolution (VEGE) is a newly proposed meta-heuristic algorithm (MA) with
excellent exploitation but relatively weak exploration capacity. We thus focus on further …

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf
optimizer (GWO) has a wide range of applications in practical problems. As a kind of local …

Surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) have been
developed for solving expensive optimization problems. According to the roles that the …

Standard evolutionary optimization algorithms assume that the evaluation of the objective
and constraint functions is straightforward and computationally cheap. However, in many …