Journal ArticleDOI
Genetic algorithm based model for optimizing bank lending decisions
TLDR
An intelligent model based on the Genetic Algorithm to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC), which provides a framework to optimize bank objectives when constructing the loan portfolio.Abstract:
The bank lending decisions in credit crunch environments are big challenge.This NP-hard optimization problem is solved using a proposed GA based model.The proposed model is tested using two scenarios with simulated and real data.The real data is collected from Southern Louisiana Credit Union.The proposed model increased the bank profit and improved the system performance. To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%50%. Moreover, it greatly increases the bank profit by a range of 3.9%8.1%.read more
Citations
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A hybrid model of Internet of Things and cloud computing to manage big data in health services applications
Mohamed Elhoseny,Ahmed Abdelaziz,Ahmed Salama,Ahmed Salama,Alaa Riad,Khan Muhammad,Arun Kumar Sangaiah +6 more
TL;DR: A new model to optimize virtual machines selection in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0 applications is proposed and outperforms on the state-of-the-art models in total execution time and the system efficiency.
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Bezier Curve Based Path Planning in a Dynamic Field using Modified Genetic Algorithm
TL;DR: An efficient, Bezier curve based approach for the path planning in a dynamic field using a Modified Genetic Algorithm (MGA), which aims to boost the diversity of the generated solutions of the standard GA which increases the exploration capabilities of the MGA.
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Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm
Alaa Tharwat,Alaa Tharwat,Mohamed Elhoseny,Mohamed Elhoseny,Aboul Ella Hassanien,Thomas Gabel,Arun Kumar +6 more
TL;DR: A novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve and it is proved that the proposed algorithm is capable of finding the optimal path.
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Energy-Optimized Partial Computation Offloading in Mobile-Edge Computing With Genetic Simulated-Annealing-Based Particle Swarm Optimization
TL;DR: In this paper, a hybrid metaheuristic algorithm named genetic simulated annealing-based particle swarm optimization (GSPO) was proposed to minimize the total energy consumed by mobile devices and edge servers by jointly optimizing the offloading ratio of tasks, CPU speeds of mobile devices, allocated bandwidth of available channels, and transmission power of each mobile device in each time slot.
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Optimizing K-coverage of mobile WSNs
TL;DR: A new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources is proposed.
References
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