Some real-world optimisation problems are dynamic; that is, their objective function and/or
constraints vary over time. Solving such problems is very challenging. Particle swarm …

This article presents the second Part of a two-Part survey that reviews evolutionary dynamic
optimization (EDO) for single-objective unconstrained continuous problems over the last two …

Many real-world optimization problems are dynamic. The field of dynamic optimization deals
with such problems where the search space changes over time. In this two-part article, we …

With the effect of global warming, the frequency of floods, one of the most important natural
disasters, increases, and this increases the damage it causes to people and the …

Swarm intelligence is a relatively recent approach for solving optimization problems that
usually adopts the social behavior of birds and animals. The most popular class of swarm …

This paper presents a novel framework for improving the performance of multi-population
algorithms in solving dynamic optimization problems (DOPs). The fundamental idea of the …

The objective of disassembly lines is to disassemble end-of-life products in a
remanufacturing field. The disassembly line balancing problem (DLBP) considers how to …

This paper presents an adaptive bi-flight cuckoo search algorithm for continuous dynamic
optimization problems. Unlike the standard cuckoo search which relies on Levy flight, the …

This paper proposes a novel hybrid approach based on particle swarm optimization and
local search, named PSOLS, for dynamic optimization problems. In the proposed approach …

Self-adaptation is a popular parameter control technique in evolutionary computation, which
has been extensively studied in stationary optimisation. In the context of dynamic …