Optimization in dynamic environments is a challenging but important task since many real- world optimization problems are changing over time. Evolutionary computation and swarm …
D Yazdani, R Cheng, D Yazdani… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
This article presents the second Part of a two-Part survey that reviews evolutionary dynamic optimization (EDO) for single-objective unconstrained continuous problems over the last two …
In dynamic optimization problems (DOPs), as the environment changes through time, the optima also dynamically change. How to adapt to the dynamic environment and quickly find …
A considerable amount of research has been devoted to solving static optimization problems via bio-inspired metaheuristic algorithms. However, most of the algorithms assume that all …
SK Nseef, S Abdullah, A Turky, G Kendall - Knowledge-based systems, 2016 - Elsevier
Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP …
Many practical, real-world applications have dynamic features. This paper introduces a novel dynamic optimization problem applied to Unmanned Aerial Vehicle (UAV) search and …
C Li, TT Nguyen, M Yang, S Yang, S Zeng - Information Sciences, 2015 - Elsevier
The multi-population method has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different …
C Yang, D Wang, J Tang, J Qiao, W Yu - Information Sciences, 2024 - Elsevier
Dynamic multi-objective optimization problems (DMOPs) have several conflicting and time- varying objectives or constraints. To quickly follow the dynamical Pareto optimal front (POF) …