Surrogate models have shown to be effective in assisting metaheuristic algorithms for
solving computationally expensive complex optimization problems. The effectiveness of …

Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms.
In general, DE does not exploit distribution information provided by the population and, as a …

Innovization is a task of learning common relationships among some or all of the Pareto-
optimal (PO) solutions in multi-and many-objective optimization problems. A recent study …

In the expensive structural optimization, the data-driven surrogate model has been proven to
be an effective alternative to physical simulation (or experiment). However, the static …

Abstract The performance of Differential Evolution (DE) largely depends on the choice of trial
vector generation strategy and the values of its control parameters. In the past years, quite a …

ABSTRACT Composite Differential Evolution (CoDE) is categorized as a (µ+ λ)-Evolutionary
Algorithm where each parent produces three trials. Thanks to that, the CoDE algorithm has a …

This paper proposes a machine learning (ML) based unified innovized progress (UIP)
operator to simultaneously enhance the convergence and diversity capabilities of reference …

Differential Evolution (DE), a population-based stochastic search technique is adept at
solving real-world optimization problems. Unlike most population based algorithms, the use …

Surrogate model assisted evolutionary algorithms (SAEAs) have attracted much research
attention in solving computationally expensive optimization problems. They show excellent …

Search strategies play an essential role in artificial bee colony (ABC) algorithm. Different
optimisation problems and search stages may need different search strategies. However, it …