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 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 Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm

2020 ◽  
Vol 10 (14) ◽  
pp. 5000
Author(s):  
Pengfei Xiao ◽  
Hehua Ju ◽  
Qidong Li ◽  
Feifei Chen
Keyword(s):  
Differential Evolution ◽  
Optimal Path ◽  
Differential Evolution Algorithm ◽  
Task Assignment ◽  
Space Robot ◽  
Path Model ◽  
Planning Problem ◽  
Penalty Function Method ◽  
Task Planning ◽  
Evolution Algorithm

This study studies the problem of on-orbit maintenance task planning for space-robot clusters. Aiming at the problem of low maintenance efficiency of space-robot cluster task-planning, this study proposes a cluster-task-planning method based on energy and path optimization. First, by introducing the penalty-function method, the task planning problem of the space-robot cluster under limited energy is analyzed, and the optimal-path model for task planning with comprehensive optimization of revenue and energy consumption are constructed; then, the maintenance task points are clustered to reduce the scale of the problem, thus reducing the difficulty of solving the problem; finally, a modified differential evolution algorithm is proposed to solve the problem of space-robot cluster task-planning, improve the performance of space-robot cluster task-assignment and path planning. Simulation results show that the proposed optimal-path model of space-robot cluster and the modified differential evolution algorithm can effectively solve the task-planning problem of spatial robot clusters.

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