Multi-objective drilling trajectory optimization using decomposition method with minimum fuzzy entropy-based comprehensive evaluation
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Multi-objective drilling trajectory optimization using decomposition method with minimum fuzzy entropy-based comprehensive evaluation
Authors
Keywords
Drilling trajectory design, Multi-objective optimization, Comprehensive evaluation, Adaptive penalty function, Multiple criteria decision making
Journal
APPLIED SOFT COMPUTING
Volume 107, Issue -, Pages 107392
Publisher
Elsevier BV
Online
2021-04-21
DOI
10.1016/j.asoc.2021.107392
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Multi-objective cellular particle swarm optimization for wellbore trajectory design
- (2019) Jun Zheng et al. APPLIED SOFT COMPUTING
- A multi-objective differential evolutionary algorithm for constrained multi-objective optimization problems with low feasible ratio
- (2019) Yongkuan Yang et al. APPLIED SOFT COMPUTING
- Fuzzy comprehensive evaluation of virtual reality mine safety training system
- (2019) Hui Zhang et al. SAFETY SCIENCE
- Reduced-Cost Constrained Miniaturization of Wideband Antennas Using Improved Trust-Region Gradient Search With Repair Step
- (2018) Adrian Bekasiewicz et al. IEEE Antennas and Wireless Propagation Letters
- Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS
- (2016) Madjid Tavana et al. EXPERT SYSTEMS WITH APPLICATIONS
- Multi-objective sidetracking horizontal well trajectory optimization in cluster wells based on DS algorithm
- (2016) Zhiyue Wang et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Metaheuristic profiling to assess performance of hybrid evolutionary optimization algorithms applied to complex wellbore trajectories
- (2016) David A. Wood Journal of Natural Gas Science and Engineering
- Horizontal well’s path planning: An optimal switching control approach
- (2015) Zhaohua Gong et al. APPLIED MATHEMATICAL MODELLING
- Performance comparison of low-grade ORCs (organic Rankine cycles) using R245fa, pentane and their mixtures based on the thermoeconomic multi-objective optimization and decision makings
- (2015) Yongqiang Feng et al. ENERGY
- A novel multi criteria decision making model for optimizing time–cost–quality trade-off problems in construction projects
- (2015) Shahryar Monghasemi et al. EXPERT SYSTEMS WITH APPLICATIONS
- Three-dimensional trajectory design for horizontal well based on optimal switching algorithms
- (2015) Xiang Wu et al. ISA TRANSACTIONS
- 3-D well path design using a multi objective genetic algorithm
- (2015) Vahid Mansouri et al. Journal of Natural Gas Science and Engineering
- Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms
- (2014) Amin Atashnezhad et al. Journal of Natural Gas Science and Engineering
- An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
- (2013) Himanshu Jain et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Constraint Handling in Multiobjective Evolutionary Optimization
- (2009) Y.G. Woldesenbet et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- An Adaptive Penalty Formulation for Constrained Evolutionary Optimization
- (2009) B. Tessema et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
- Multi-objective inventory planning using MOPSO and TOPSIS
- (2007) C TSOU EXPERT SYSTEMS WITH APPLICATIONS
- Stochastic optimal control and algorithm of the trajectory of horizontal wells
- (2006) An Li et al. JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started