K-means cluster interactive algorithm-based evolutionary approach for solving bilevel multi-objective programming problems
Y. Abo-Elnaga,S. M. Nasr +1 more
33
TL;DR: In this article, the authors proposed a k-means cluster scheme for solving the bilevel multi-objective programming problem, where the first phase is before starting two nested algorithms to help the algorithm to start with more appropriates solutions to the bi-level problem.
read more
Abstract: Solving bilevel multi-objective programming problems is one of the hardest tasks facing researchers in the optimization community. Bilevel multi-objective programming problems is an optimization problem consists of two interconnected hierarchical multi-objective programming problems: upper-level problem and lower-level problem. Difficulty in solving bilevel multi-objective programming problems is the need to solve lower-level multi-objective programming problem to know the feasible space of the upper-level problem. The proposed algorithm consists of two nested artificial multi-objective algorithms. One algorithm is for the upper-level problem and the other is for the lower-level problem. Also, the proposed algorithm is enriched with a k-means cluster scheme in two phases. The first phase is before starting two nested algorithms to help the algorithm to start with more appropriates solutions to the bi-level problem. The second phase is within the two nested algorithms to guide the algorithm to the most preferred solutions to the upper-level decision-maker. The performance of the proposed algorithm has been evaluated on different test problems including low dimension and high dimension test problems. The experimental results show that the proposed algorithm is a feasible and efficient method for solving the bilevel multi-objective programming problem.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A research study on new energy brand users based on principal component analysis (PCA) and fusion target planning model for sustainable environment of smart cities
TL;DR: In this paper , the potential users of energy based vehicles and their expectations from the energy-based vehicles are analyzed and feature transformations are devised such as feature reduction and feature merger to make changes in the feature dimension of the original data source to facilitate the solution of the problem statement.
25
Color Classification and Texture Recognition System of Solid Wood Panels
TL;DR: In this paper, a machine vision technology and an unsupervised learning technique was introduced to reduce labor costs of sorting and to improve production efficiency, in order to reduce production efficiency.
23
Failure Evaluation of Electronic Products Based on Double Hierarchy Hesitant Fuzzy Linguistic Term Set and K-Means Clustering Algorithm
TL;DR: In this paper , the authors used the double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) and the K-means clustering algorithm to improve the shortcomings of traditional failure mode and effect analysis.
An improved method MSS-YOLOv5 for object detection with balancing speed-accuracy
TL;DR: Zhang et al. as discussed by the authors presented a superior network named MSS-YOLOv5, which not only considers the reliability in complex scenes but also promotes its timeliness to better adapt to practical scenarios.
8
Optimal techno-economic energy coordination of solar PV water pumping irrigation systems
Ahmed Elnozahy,Mazen Abdel-Salam,Farag K. Abo-Elyousr +2 more
TL;DR: A techno-economic energy coordination system for solar PV water pumping irrigation is developed, minimizing water supply loss and total costs, and saving 45% of pumped water through vibration avoidance, enhancing crop yield and economic feasibility in Farafra oasis, Egypt.
7
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
•Book
Engineering Optimization : Theory and Practice
Singiresu S. Rao
- 01 Jan 2011
TL;DR: This chapter discusses Optimization Techniques, which are used in Linear Programming I and II, and Nonlinear Programming II, which is concerned with One-Dimensional Minimization.
4K
A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications
TL;DR: A comprehensive review on bilevel optimization from the basic principles to solution strategies is provided in this paper, where a number of potential application problems are also discussed and an automated text-analysis of an extended list of papers has been performed.
907
Performance indicators in multiobjective optimization
Charles Audet,Jean Bigeon,Dominique Cartier,Sébastien Le Digabel,Ludovic Salomon,Ludovic Salomon +5 more
TL;DR: A review of a total of 63 performance indicators partitioned into four groups according to their properties: cardinality, convergence, distribution and spread is proposed.
371