scholarly journals A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Said Ali El-Qulity ◽  
Ali Wagdy Mohamed
Keyword(s):  
Higher Education ◽  
Differential Evolution ◽  
Goal Programming ◽  
Capacity Planning ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Solution Quality ◽  
De Algorithm ◽  
Planning Approach ◽  
Goal Programming Model

This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.

scholarly journals A Nonlinear Goal Programming Model for University Admission Capacity Planning with Modified Differential Evolution Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Said Ali El-Quliti ◽  
Abdul Hamid Mohamed Ragab ◽  
Reda Abdelaal ◽  
Ali Wagdy Mohamed ◽  
Abdulfattah Suliman Mashat ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Goal Programming ◽  
Capacity Planning ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Planning Problem ◽  
The Past ◽  
University Admission ◽  
Goal Programming Model ◽  
Evolution Algorithm

This paper proposes a nonlinear Goal Programming Model (GPM) for solving the problem of admission capacity planning in academic universities. Many factors of university admission capacity planning have been taken into consideration among which are number of admitted students in the past years, total population in the country, number of graduates from secondary schools, desired ratios of specific specialties, faculty-to-students ratio, and the past number of graduates. The proposed model is general and has been tested at King Abdulaziz University (KAU) in the Kingdom of Saudi Arabia, where the work aims to achieve the key objectives of a five-year development plan in addition to a 25-year future plan (AAFAQ) for universities education in the Kingdom. Based on the results of this test, the proposed GPM with a modified differential evolution algorithm has approved an ability to solve general admission capacity planning problem in terms of high quality, rapid convergence speed, efficiency, and robustness.

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

2014 ◽  
Vol 1046 ◽  
pp. 367-370
Author(s):  
Yu Zhou ◽  
Yong Bin Li ◽  
Zhong Zheng Shi ◽  
Zheng Xin Li ◽  
Lei Zhang
Keyword(s):  
Differential Evolution ◽  
Goal Programming ◽  
Large Scale ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Goal Programming Model ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

scholarly journals Ranking the Performance of Universities: The Role of Sustainability

2021 ◽  
Vol 13 (23) ◽  
pp. 13286
Author(s):  
Christoph Burmann ◽  
Fernando García ◽  
Francisco Guijarro ◽  
Javier Oliver
Keyword(s):  
Higher Education ◽  
Sensitivity Analysis ◽  
Goal Programming ◽  
Programming Model ◽  
Decision Makers ◽  
University Rankings ◽  
Teaching And Research ◽  
Sustainability Criteria ◽  
Goal Programming Model

University rankings assess the performance of universities in various fields and aggregate that performance into a single value. In this way, the aggregate performance of universities can be easily compared. The importance of rankings is evident, as they often guide the policy of Higher Education Institutions. The most prestigious multi-criteria rankings use indicators related to teaching and research. However, many stakeholders are now demanding a greater commitment to sustainable development from universities, and it is therefore necessary to include sustainability criteria in university rankings. The development of multi-criteria rankings is subject to numerous criticisms, including the subjectivity of the decision makers when assigning weights to the criteria. In this paper we propose a methodology based on goal programming that allows objective, transparent and reproducible weighting of the criteria. Moreover, it avoids the problems associated with the existence of correlated criteria. The methodology is applied to a sample of 718 universities, using 11 criteria obtained from two prestigious university rankings covering sustainability, teaching and research. A sensitivity analysis is carried out to assess the robustness of the results obtained. This analysis shows how the weights of the criteria and the universities’ rank change depending on the λ parameter of the goal programming model, which is the only parameter set by the decision maker.

A new differential evolution algorithm with a combined mutation strategy for optimum synthesis of path-generating four-bar mechanisms

2016 ◽  
Vol 231 (14) ◽  
pp. 2690-2705 ◽  
Author(s):  
WY Lin ◽  
KM Hsiao
Keyword(s):  
Differential Evolution ◽  
Synthesis Method ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Dimensional Synthesis ◽  
Solution Quality ◽  
De Algorithm ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Solution Accuracy

A one-phase synthesis method using heuristic optimization algorithms can solve the dimensional synthesis problems of path-generating four-bar mechanisms. However, due to the difficulty of the problem itself, there is still room for improvement in solution accuracy and reliability. Therefore, in this study, a new differential evolution (DE) algorithm with a combined mutation strategy, termed the combined-mutation differential evolution (CMDE) algorithm, is proposed to improve the solution quality. In the combined mutation strategy, the DE/best/1 operator and the DE/current-to-best/1 operator are respectively executed on some superior parents and some mediocre parents, and the DE/rand/1 operator is executed on the other inferior parents. Furthermore, the individuals participating in the three mutation operators are randomly selected from the entire set of parents. The proposed CMDE algorithm with the three different search modes possesses better population diversity as well as search ability than the DE algorithm. The effectiveness of the proposed CMDE algorithm is demonstrated using five representative problems. Findings show a marked improvement in solution accuracy and reliability. The most accurate results are obtained with an approximate combination ratio for the three mutation operators.

Scalable Differential Evolutionary Clustering Algorithm for Big Data Using Map-Reduce Paradigm

2017 ◽  
Vol 8 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Zakaria Benmounah ◽  
Souham Meshoul ◽  
Mohamed Batouche
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Map Reduce ◽  
Data Sets ◽  
Traditional Use ◽  
Evolutionary Clustering ◽  
De Algorithm ◽  
Large Sets

One of the remarkable results of the rapid advances in information technology is the production of tremendous amounts of data sets, so large or complex that available processing methods are inadequate, among these methods cluster analysis. Clustering becomes more challenging and complex. In this paper, the authors describe a highly scalable Differential Evolution (DE) algorithm based on map-reduce programming model. The traditional use of DE to deal with clustering of large sets of data is so time-consuming that it is not feasible. On the other hand, map-reduce is a programming model emerged lately to allow the design of parallel and distributed approaches. In this paper, four stages map-reduce differential evolution algorithm termed as DE-MRC is presented; each of these four phases is a map-reduce process and dedicated to a particular DE operation. DE-MRC has been tested on a real parallel platform of 128 computers connected with each other and more than 30 GB of data. Experimental results show the high scalability and robustness of DE-MRC.

Sign in / Sign up

Export Citation Format

Share Document