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.

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.

An Optimization Approach of SRM Sphere Slot Grain Design Based on Improved Differential Evolution Algorithm

2014 ◽  
Vol 971-973 ◽  
pp. 1072-1075
Author(s):  
Wei Chen ◽  
Guo Zhu Liang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Penalty Function Method ◽  
Optimization Approach ◽  
Solid Rocket Motor ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Optimum Solution ◽  
Global Optimum Solution

The sphere slot is a 3D configuration of solid rocket motor (SRM) grain. Accompanied with multi-design-variable and strong constraints, its optimization is always a complicated problem. In this article, a methodology has been presented for the optimization of sphere slot grain configuration. Parameterized CAD model method and unsteady lumped parameter internal ballistic model are adopted to evaluate the performance of sphere slot grain to establish the optimization objective and constraint functions. An improved differential evolution algorithm incorporated with Oracle penalty function method is developed and applied on the optimization to solve global optimum solution converging difficulty of multi-variable constrained problem. The approach is validated by a small sphere slot SRM. The results illustrate it is effective method and has robustness and efficient capacity to explore the design space for global optimum solution of sphere slot grain.

scholarly journals Optimization of Wireless Sensor Networks Based on Differential Evolution Algorithm

2019 ◽  
Vol 15 (01) ◽  
pp. 183 ◽  
Author(s):  
Qing Wan ◽  
Ming-Jiang Weng ◽  
Song Liu
Keyword(s):  
Differential Evolution ◽  
Optimization Model ◽  
Optimization Problem ◽  
Optimal Path ◽  
Minimum Energy ◽  
Differential Evolution Algorithm ◽  
Path Optimization ◽  
Wireless Sensor ◽  
Advantages And Disadvantages ◽  
Evolution Algorithm

To study the optimization problem of wireless sensor network (WSN) based on differential evolution, the single objective differential evolution algorithm is applied and combined with the advantages and disadvantages crossover strategy. Firstly, the path optimization problem in WSN is analyzed, and the optimization model is established. Then, the differential evolution algorithm is used as the search tool to solve the minimum energy consumption in the path optimization model, that is, the optimal path problem. Finally, the comparison experiment is carried out on the classical algorithm genetic algorithm (GA), particle swarm optimization (PSO) and standard differential evolution (DE) algorithm. The results show that the performance of differential evolution algorithm based on crossover strategy is superior to or not worse than that of several contrast algorithms. It can be seen that the differential evolution algorithm based on advantage and disadvantage crossover strategy has good effectiveness.

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