Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …

In their original versions, nature-inspired search algorithms such as evolutionary algorithms
and those based on swarm intelligence, lack a mechanism to deal with the constraints of a …

Optimal power flow (OPF) problem is a large-scale, non-convex, multi-modal, and non-linear
constrained optimization problem, which has been widely used in power system operation …

When solving constrained optimization problems (COPs) by evolutionary algorithms, the
search algorithm plays a crucial role. In general, we expect that the search algorithm has the …

Over the last few decades, a number of differential evolution (DE) algorithms have been
proposed with excellent performance on mathematical benchmarks. However, like any other …

When solving constrained optimization problems by evolutionary algorithms, an important
issue is how to balance constraints and objective function. This paper presents a new …

This article proposes an evolutionary algorithm using multiple penalties and multiple local
surrogates (MPMLS) for expensive constrained optimization. In each generation, MPMLS …

Effective constraint handling techniques (CHTs) are of great significance for evolutionary
algorithms (EAs) dealing with constrained optimization problems (COPs). To date, many …

This paper presents a cooperative differential evolution framework (CCMODE) for
constrained multiobjective optimization, and two instantiations of the CCMODE framework …