From the past few decades many nature inspired algorithms have been developed and
gaining more popularity because of their effectiveness in solving problems of distinct …

Optimization in dynamic environments is a challenging but important task since many real-
world optimization problems are changing over time. Evolutionary computation and swarm …

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm
optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish …

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 …

In dynamic optimization problems (DOPs), as the environment changes through time, the
optima also dynamically change. How to adapt to the dynamic environment and quickly find …

A considerable amount of research has been devoted to solving static optimization problems
via bio-inspired metaheuristic algorithms. However, most of the algorithms assume that all …

Recently, interest in solving real-world problems that change over the time, so called
dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP …

Many practical, real-world applications have dynamic features. This paper introduces a
novel dynamic optimization problem applied to Unmanned Aerial Vehicle (UAV) search and …

The multi-population method has been widely used to solve unconstrained continuous
dynamic optimization problems with the aim of maintaining multiple populations on different …

Dynamic multi-objective optimization problems (DMOPs) have several conflicting and time-
varying objectives or constraints. To quickly follow the dynamical Pareto optimal front (POF) …