scholarly journals Application of Multiobjective Particle Swarm Optimization in Rural Credit System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wuxia Xue
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Credit Constraints ◽  
Particle Swarm ◽  
Optimization Strategy ◽  
Credit System ◽  
Swarm Optimization ◽  
Rural Credit ◽  
Rural Economic Development ◽  
Multiobjective Particle Swarm Optimization

In China’s rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China’s rural economic development. Aimed at solving the rural credit constraints, this paper makes an optimization analysis on the rural credit system and loan decision-making. To more reasonably evaluate customers’ borrowing ability, the credit risk based on farmers’ data on the big data platform is evaluated in this paper. The stacked denoising autoencoder network is improved by adopting the deep learning framework to improve the accuracy of credit evaluation. For improving the loan decision-making ability of rural credit system, a loan optimization strategy based on multiobjective particle swarm optimization algorithm is proposed. The simulation results show that the optimization ability, speed, and stability of the proposed algorithm have achieved good results in dealing with the loan portfolio decision-making problem.

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

scholarly journals Developing an approach for decision making units integration using multi objective particle swarm optimization

2014 ◽  
Vol 4 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdorrahman Haeri ◽  
Kamran Rezaie ◽  
Seyed Morteza Hatefi
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Performance Measures ◽  
Particle Swarm ◽  
Final Decision ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Decision Making Unit ◽  
The Difference ◽  
Decision Making Units

In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively.  Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.

Sign in / Sign up

Export Citation Format

Share Document