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

Dynamic multi-objective optimization problems (DMOPs) typically contain multiple conflicting
objectives that vary over time, requiring the optimization algorithms to quickly track the …

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and
subspace-based diversity maintenance (SDM) for handling dynamic multiobjective …

Recently, dynamic multi-objective optimization has received growing attention due to its
popularity in real-world applications. Inspired by polynomial fitting, this paper proposes a …

Many real− world applications are dynamic multi− objective optimization problems (DMOPs).
The transfer of knowledge in the evolutionary process is believed to have advantages in …

Most existing research on dynamic optimization focuses on tracking the moving global
optimum (TMO). Recently, a new paradigm for handling dynamic optimization, known as …

In the domain of evolutionary computation, more and more attention has been paid to
dynamic multiobjective optimization. Generally, artificial benchmarks are effective tools for …

Preserving population diversity and providing knowledge, which are two core tasks in the
dynamic multi‐objective optimisation (DMO), are challenging since the sampling space is …

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a
new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective …

Multi-objective evolutionary algorithms suffer from performance degradation when solving
dynamic multi-objective optimization problems (DMOPs) with a new conditional …