Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly
growing area of investigation. EDMO employs evolutionary approaches to handle multi …

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

In recent years, the research community has witnessed an explosion of literature dealing
with the mimicking of behavioral patterns and social phenomena observed in nature towards …

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

One of the major distinguishing features of the dynamic multiobjective optimization problems
(DMOPs) is that optimization objectives will change over time, thus tracking the varying …

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the
goal of automating the design of heuristic methods to solve hard computational search …

Ant colony algorithm can resolve dynamic optimization problems due to its robustness and
adaptation. The aim of such algorithms in dynamic environments is no longer to find an …

Multi-population algorithms have been widely used for solving the real-world problems.
However, it is not easy to get the number of sub-populations to be used for a given problem …

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between
two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms …

Abstract Dynamic Multi-objective Optimization Problem (DMOP) is emerging in recent years
as a major real-world optimization problem receiving considerable attention. Tracking the …