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

摘要现实生活中, 存在许多动态多目标优化问题(DynamicMulti
objectiveOptimizationProblems, DMOPs), 这类问题的目标函数之间相互矛盾, 并且目标函数 …

The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-
optima closer to the true one as soon as possible once a new environment occurs. Previous …

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 …

Workflow scheduling is a largely studied research topic in cloud computing, which targets to
utilize cloud resources for workflow tasks by considering the objectives specified in QoS. In …

When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary
algorithms, the historical moving directions of some special points along the Pareto front …

Dynamic multiobjective optimization problems (DMOPs) are optimization problems with
multiple conflicting optimization objectives, and these objectives change over time. Transfer …

Prediction strategies are widely-used in dynamic multi-objective evolutionary algorithms
(DMOEAs). However, the characteristics of the environmental changes are different and only …

Various real-world multiobjective optimization problems are dynamic, requiring evolutionary
algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization …

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