A diversity preservation method for expensive multi-objective combinatorial optimization problems using Novel-First Tabu Search and MOEA/D
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Title
A diversity preservation method for expensive multi-objective combinatorial optimization problems using Novel-First Tabu Search and MOEA/D
Authors
Keywords
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Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 202, Issue -, Pages 117251
Publisher
Elsevier BV
Online
2022-04-20
DOI
10.1016/j.eswa.2022.117251
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Note: Only part of the references are listed.- Improving NSGA-III algorithms with information feedback models for large-scale many-objective optimization
- (2020) Zi-Min Gu et al. Future Generation Computer Systems-The International Journal of eScience
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- (2020) Yin Zhang et al. INFORMATION SCIENCES
- Expected improvement for expensive optimization: a review
- (2020) Dawei Zhan et al. JOURNAL OF GLOBAL OPTIMIZATION
- Slime mould algorithm: A new method for stochastic optimization
- (2020) Shimin Li et al. Future Generation Computer Systems-The International Journal of eScience
- Knee Point-Based Imbalanced Transfer Learning for Dynamic Multiobjective Optimization
- (2020) Min Jiang et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Multi-objective unconstrained combinatorial optimization: a polynomial bound on the number of extreme supported solutions
- (2019) Britta Schulze et al. JOURNAL OF GLOBAL OPTIMIZATION
- A Tabu Search Based Algorithm for the Optimal Design of Multi-objective Multi-product Supply Chain Networks
- (2019) Awsan M. Mohammed et al. EXPERT SYSTEMS WITH APPLICATIONS
- The development of a novel multi-objective optimization framework for non-vertical well placement based on a modified non-dominated sorting genetic algorithm-II
- (2019) Auref Rostamian et al. COMPUTATIONAL GEOSCIENCES
- Harris hawks optimization: Algorithm and applications
- (2019) Ali Asghar Heidari et al. Future Generation Computer Systems-The International Journal of eScience
- Interval Multiobjective Optimization With Memetic Algorithms
- (2019) Jing Sun et al. IEEE Transactions on Cybernetics
- An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems
- (2018) Jiao-Hong Yi et al. Future Generation Computer Systems-The International Journal of eScience
- A New Decomposition-Based NSGA-II for Many-Objective Optimization
- (2018) Maha Elarbi et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems
- (2018) Gai Ge Wang et al. International Journal of Bio-Inspired Computation
- A Multi-Objective Memetic Algorithm for a Fuzzy Parallel Blocking Flow Shop Scheduling Problem of Panel Block Assembly in Shipbuilding
- (2018) Zhi Yang et al. Journal of Ship Production and Design
- Behavior of crossover operators in NSGA-III for large-scale optimization problems
- (2018) Jiao-Hong Yi et al. INFORMATION SCIENCES
- Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization
- (2018) Bing-Chuan Wang et al. IEEE Transactions on Systems Man Cybernetics-Systems
- A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization
- (2017) Shouyong Jiang et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Multi-criterion based well placement and control in the water-flooding of naturally fractured reservoir
- (2017) Abolfazl Bagherinezhad et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
- (2016) Ran Cheng et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Assisted process for design optimization of oil exploitation strategy
- (2016) Ana Teresa F.S. Gaspar et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems
- (2016) Gai-Ge Wang Memetic Computing
- Multi-objective optimization for rapid and robust optimal oilfield development under geological uncertainty
- (2015) Yuqing Chang et al. COMPUTATIONAL GEOSCIENCES
- Pareto-based robust optimization of water-flooding using multiple realizations
- (2015) Elham Yasari et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Monarch butterfly optimization
- (2015) Gai-Ge Wang et al. NEURAL COMPUTING & APPLICATIONS
- MOEA/D with uniform decomposition measurement for many-objective problems
- (2014) Xiaoliang Ma et al. SOFT COMPUTING
- An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
- (2013) Kalyanmoy Deb et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Application of multi-criterion robust optimization in water-flooding of oil reservoir
- (2013) Elham Yasari et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- MOEA/D-ACO: A Multiobjective Evolutionary Algorithm Using Decomposition and AntColony
- (2013) Liangjun Ke et al. IEEE Transactions on Cybernetics
- Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization
- (2011) David Hadka et al. EVOLUTIONARY COMPUTATION
- Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs
- (2011) Hadi Nasrabadi et al. Journal of Natural Gas Science and Engineering
- RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
- (2008) Qingfu Zhang et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II
- (2008) Hui Li et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
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