A Mixed Coding Scheme of a Particle Swarm Optimization and a Hybrid Genetic Algorithm with Sequential Quadratic Programming for Mixed Integer Nonlinear Programming in Common Chemical Engineering Practice

2017 ◽  
Vol 204 (8) ◽  
pp. 840-851 ◽  
Author(s):  
Manatsanan Chanthasuwannasin ◽  
Bundit Kottititum ◽  
Thongchai Srinophakun
Keyword(s):  
Genetic Algorithm ◽  
Sequential Quadratic Programming ◽  
Chemical Engineering ◽  
Hybrid Genetic Algorithm ◽  
Mixed Integer ◽  
Engineering Practice ◽  
Swarm Optimization ◽  
Coding Scheme ◽  
Integer Nonlinear Programming ◽  
Mixed Coding

Stackcelberg Game Inventory Model With Progressive Permissible Delay of Payment Scheme

2018 ◽  
pp. 194-214
Author(s):  
Gede Agus Widyadana ◽  
Nita H. Shah ◽  
Daniel Suriawidjaja Siek
Keyword(s):  
Genetic Algorithm ◽  
Interest Rate ◽  
Stackelberg Game ◽  
Hybrid Genetic Algorithm ◽  
Inventory Model ◽  
Delay Period ◽  
Game Model ◽  
Inventory Cost ◽  
Swarm Optimization ◽  
High Interest Rate

Supplier has many schemes to motivate retailer to buy more and of them one is a progressive permissible delay of payment. Instead of analyst from the retailer side alone, in this chapter, we develop the inventory model of supplier and retailer. In reality, some suppliers and retailers cannot have collaboration and they try to optimize their own decision so we develop a Stackelberg Game model. Two models are developed wherein the first model supplier acts as the leader and in the second model, the retailer acts a leader. Since the models are complex, a hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is developed to solve the model. A numerical analysis and sensitivity analysis are conducted to get management insights of the model. The results show that a Stackelberg Game model for progressive permissible delay of payment is sensitive in varies values of the first and second delay interest rate if supplier acts as a leader. The retailer gets less inventory cost when he acts as a leader compared to when vendor acts a leader at high interest rate of the first and second delay period.

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

Index fund optimization using a hybrid model: genetic algorithm and mixed-integer nonlinear programming

2019 ◽  
Vol 64 (3) ◽  
pp. 298-309 ◽  
Author(s):  
Juan Díaz ◽  
María Cortés ◽  
Juan Hernández ◽  
Óscar Clavijo ◽  
Carlos Ardila ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Hybrid Model ◽  
Mixed Integer Nonlinear Programming ◽  
Mixed Integer ◽  
Integer Nonlinear Programming

Engineering-Oriented Hybrid Genetic Algorithm for Frequency Assignment Problem

2010 ◽  
Vol 40-41 ◽  
pp. 410-418
Author(s):  
Ting Ting Zhou ◽  
Ying Zheng ◽  
Ming Chen
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Assignment Problem ◽  
Hybrid Genetic Algorithm ◽  
Mobile Telephone ◽  
Frequency Assignment ◽  
Real Word ◽  
Frequency Assignment Problem ◽  
Swarm Optimization

Since the usable range of the frequency spectrum is limited, the frequency assignment problem (FAP) is important in mobile telephone communication. In this paper, according to the characteristics of engineering- oriented FAP, an engineering-oriented hybrid genetic algorithm (EHGA) based on traditional genetic algorithm (TGA) is proposed, combined with particle swarm optimization (PSO) and simulated annealing (SA). The results obtained by the simulation to a real-word FAP case in GSM show that the algorithm we proposed is a better approach to solve the engineering-oriented FAP.

scholarly journals Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations

AIAA Journal ◽  
2005 ◽  
Vol 43 (8) ◽  
pp. 1844-1849 ◽  
Author(s):  
Vladimir B. Gantovnik ◽  
Zafer Gurdal ◽  
Layne T. Watson ◽  
Christine M. Anderson-Cook
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Mixed Integer Nonlinear Programming ◽  
Mixed Integer ◽  
Integer Nonlinear Programming

A New Hybrid Genetic Algorithm and Particle Swarm Optimization

2012 ◽  
Vol 498 ◽  
pp. 115-125 ◽  
Author(s):  
H. Hachimi ◽  
Rachid Ellaia ◽  
A. El Hami
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Hybrid Technique ◽  
Swarm Optimization ◽  
New Generation ◽  
Crossover And Mutation

In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted asGA-PSO, this hybrid technique incorporates concepts fromGAandPSOand creates individuals in a new generation not only by crossover and mutation operations as found inGAbut also by mechanisms ofPSO. The performance of the two algorithms has been evaluated using several experiments.

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