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

In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective
optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer …

Dynamic multiobjective optimization problems (DMOPs) are characterized by optimization
functions that change over time in varying environments. The DMOP is challenging because …

In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed
based on a designed hierarchical response system (HRS). Named HRS-DMOA, the …

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-
dependent objective functions, while the ones with a changing number of objectives have …

Abstract Dynamic Multi-objective Optimization is a challenging research topic since the
objective functions, constraints, and problem parameters may change over time. Although …

In addition to the need for simultaneously optimizing several competing objectives, many
real-world problems are also dynamic in nature. These problems are called dynamic multi …

Dynamic multiobjective optimization problems (DMOPs) usually involve multiple conflicting
objectives that change over time. A good evolutionary algorithm should be able to quickly …

This paper presents a novel prediction model combined with a multiobjective evolutionary
algorithm based on decomposition to solve dynamic multiobjective optimization problems. In …

In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn
increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have …