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 …

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

A key issue in evolutionary algorithms for dynamic multi-objective optimization problems
(DMOPs) is how to detect and response environmental changes. Most existing evolutionary …

动态多目标优化问题(Dynamic multi-objective optimization problems, DMOPs)
已成为工程优化的研究热点, 其目标函数, 约束函数和相关参数都可能随时间不断变化 …

Dynamic multi-objective optimization problems are the multi-objective optimization problems
in which the objectives change with environment and time, and the optimization algorithm for …

Tracking pareto-optimal set or pareto-optimal front in limited time is an important problem of
dynamic multi-objective optimization evolutionary algorithms (DMOEAs). However, the …

针对现有的动态多目标优化算法种群收敛速度慢, 多样性难以保持等问题,
提出了一种基于Pareto 解集分段预测策略的动态多目标进化算法BPDMOP …

In dynamic black-box optimization, time-varying objective functions are optimized. Nature-
inspired optimization algorithms like evolution strategies and swarm optimization algorithms …

This paper presents a systematic method for the optimal settings of gas turbine operation
under changing environment. After building the high-fidelity model of the gas turbine, four …

Dynamic optimization problems typically appear in real-world systems underlying
environmental influence. Solving this kind of problems requires algorithms considering …