A novel adaptive control strategy for decomposition-based multiobjective algorithm
DOI10.1016/J.COR.2016.08.012zbMath1391.90659OpenAlexW2511692447MaRDI QIDQ1652059
Zhihua Du, Jianyong Chen, Zhong Ming, Chaoyu Tang, Qiuzhen Lin, Yueping Ma, Jian-Qiang Li
Publication date: 11 July 2018
Published in: Computers \& Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cor.2016.08.012
Nonconvex programming, global optimization (90C26) Multi-objective and goal programming (90C29) Transportation, logistics and supply chain management (90B06) Approximation methods and heuristics in mathematical programming (90C59) Optimal stochastic control (93E20)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- MOEA/D + uniform design: a new version of MOEA/D for optimization problems with many objectives
- Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm
- DE-VNS: self-adaptive differential evolution with crossover neighborhood search for continuous global optimization
- Constraint-handling through multi-objective optimization: the hydrophobic-polar model for protein structure prediction
- A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows
- A hybrid differential evolution approach based on surrogate modelling for scheduling bottleneck stages
- A differential evolution algorithm with self-adapting strategy and control parameters
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- A modification to MOEA/D-DE for multiobjective optimization problems with complicated Pareto sets
This page was built for publication: A novel adaptive control strategy for decomposition-based multiobjective algorithm