scholarly journals HYBRID OPTIMIZATION ALGORITHM FOR CABLE DESIGN OF ELECTROMECHANICAL PRODUCTS BASED ON A* AND GENETIC ALGORITHM

2019 ◽  
Vol 1 (5) ◽  
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Hybrid Optimization ◽  
Hybrid Optimization Algorithm ◽  
Electromechanical Products

Tuning a Hybrid Optimization Algorithm by Determining the Modality of the Design Space

2001 ◽  
Author(s):  
Kurt Hacker ◽  
John Eddy ◽  
Kemper Lewis
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Optimization Algorithm ◽  
Design Space ◽  
Hybrid Optimization ◽  
Programming Algorithm ◽  
Sequential Linear Programming ◽  
Hybrid Optimization Algorithm ◽  
Linear Programming Algorithm ◽  
Algorithm Benchmarking

Abstract In this paper we present an approach for increasing the efficiency of a hybrid Genetic/Sequential Linear Programming algorithm. We introduce two metrics for evaluating the modality of the design space and then use this information to efficiently switch between the Genetic Algorithm and SLP algorithm. The motivation for this study is an effort to reduce the computational expense associated with the use of a Genetic Algorithm by reducing the number of function evaluations needed to find good solutions. In the paper the two metrics used to evaluate the modality of the design space are the variance in fitness of the population of the designs in the Genetic Algorithm and the error associated with fitting a response surface to the designs evaluates by the Genetic Algorithm. The effectiveness of this approach is demonstrated by considering a highly multimodal Genetic Algorithm benchmarking problem.

The Improved Route Optimization Based on Hybrid Optimization Algorithm Based on GA and TS

2012 ◽  
Vol 170-173 ◽  
pp. 3695-3698
Author(s):  
Jun Ma ◽  
Li Ying Zhang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Route Optimization ◽  
Hybrid Optimization ◽  
Improved Genetic Algorithm ◽  
Hybrid Optimization Algorithm ◽  
Search Speed

The vehicle routing planning in the process of logistics is a hot issue. There is a lack of traditional genetic algorithm used to solve this issue, so the taboos is introduced to improve it. The improved genetic algorithm based on taboos get high-search speed compared to the traditional genetic algorithm, and this improvement is verified by the example.

scholarly journals A Novel Hybrid Optimization Algorithm for Scalable Video Coding in an SDN

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Li Wang ◽  
Xiaokai Wang
Keyword(s):  
Genetic Algorithm ◽  
Video Coding ◽  
Optimization Algorithm ◽  
Programming Model ◽  
Moving Average ◽  
Scalable Video Coding ◽  
Single Layer ◽  
Hybrid Optimization ◽  
Scalable Video ◽  
Hybrid Optimization Algorithm

Scalable Video Coding (SVC) is a powerful solution to video application over heterogeneous networks and diversified end users. In the recent years, works mostly concentrate on transported layers or path for a single layer in the Software-Defined Network (SDN). This paper proposes the Novel Hybrid Optimization Algorithm for Scalable Video Coding (NHO-SVC) based on Genetic Algorithm to select the layer and path simultaneously. The algorithm uses the 0/1 knapsack programming model to set up the model, predicts the network states by the Autoregressive Integrated Moving Average Model (ARIMA), and then, makes decision based on Genetic Algorithm.

Hybrid optimization for lithologic inversion and time-lapse monitoring using a binary formulation

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. I55-I65 ◽  
Author(s):  
Richard A. Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Time Lapse ◽  
Geophysical Data ◽  
Hybrid Optimization ◽  
Superior Performance ◽  
Scale Model ◽  
Gravity Inversion ◽  
Hybrid Optimization Algorithm ◽  
Model Features

We have developed a hybrid optimization algorithm for binary inversion of geophysical data associated with lithologic inversion and time-lapse monitoring. The binary condition is designed to invert geophysical data for well-defined physical properties within discrete lithologic units, such as a salt body within a sedimentary host, or temporal changes in physical property associated with dynamic processes, such as the density and conductivity change in an oil and gas reservoir or groundwater aquifer. The solution of such inverse problems with discrete model values requires specialized optimization algorithms. To meet this need, we develop a hybrid optimization algorithm by combining a genetic algorithm with quenched simulated annealing. The former allows for easy incorporation of prior geologic information and rapid buildup of large-scale model features, whereas the latter guides genetic algorithm to faster evolution by rapidly adjusting the finer model features. In examining the performance of the hybrid algorithm, we note its superior performance. Our investigations of the algorithm’s capability with a 3D gravity inversion for the SEG/EAGE salt model and a time-lapse gravity data set from an aquifer storage and recovery process reveal its benefits.

scholarly journals Multi-user Remote Lab: Timetable Scheduling Using Simplex Nondominated Sorting Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Simplex Algorithm ◽  
Local Optimum ◽  
Hybrid Optimization Algorithm ◽  
Multimodal Function ◽  
Remote Laboratories ◽  
Remote Lab ◽  
Timetable Problem

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.

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