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.

AN IMPROVED HYBRID OPTIMIZATION ALGORITHM FOR THE QUADRATIC ASSIGNMENT PROBLEM

2005 ◽  
Vol 9 (2) ◽  
pp. 149-168 ◽  
Author(s):  
A. Misevičius
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Quadratic Assignment Problem ◽  
Hybrid Optimization ◽  
Quadratic Assignment ◽  
Hybrid Optimization Algorithm

In this paper, we present an improved hybrid optimization algorithm, which was applied to the hard combinatorial optimization problem, the quadratic assignment problem (QAP). This is an extended version of the earlier hybrid heuristic approach proposed by the author. The new algorithm is distinguished for the further exploitation of the idea of hybridization of the well‐known efficient heuristic algorithms, namely, simulated annealing (SA) and tabu search (TS). The important feature of our algorithm is the so‐called “cold restart mechanism”, which is used in order to avoid a possible “stagnation” of the search. This strategy resulted in very good solutions obtained during simulations with a number of the QAP instances (test data). These solutions show that the proposed algorithm outperforms both the “pure” SA/TS algorithms and the earlier author's combined SA and TS algorithm. Key words: hybrid optimization, simulated annealing, tabu search, quadratic assignment problem, simulation.

Optimal Image Watermarking using Hybrid Optimization Algorithm

2015 ◽  
Vol 5 (1) ◽  
pp. 27-47
Author(s):  
Satish Ramchandra Todmal ◽  
Suhas Haribhau Patil
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Image Watermarking ◽  
Optimal Location ◽  
Secret Data ◽  
Bee Colony ◽  
Hybrid Optimization Algorithm ◽  
Extraction Algorithm

Image watermarking is a process of embedding secret information into cover image to secure transmission of secret data. Literature presents several image watermarking techniques are based on different transformation such as, wavelet transform, Fourier transform and cosine transform. Most of the authors have been developed an embedding and extraction algorithm newly with the transformed image. Then, the optimal location for embedding the secret data was identified by using optimization algorithm. Accordingly, the authors have developed an optimal robust watermarking technique using genetic algorithm and wavelet transform. In the previous work, watermarks were embedded into the wavelet coefficients of HL and LH band after searching the optimal locations in order to improve both quality of watermarked image and robustness of the watermark. In this work, the authors have developed to improve the genetic algorithm by combining it with Artificial Bee Colony algorithm (ABC Algorithm). Here, they have used hybrid algorithm for finding of optimal location in watermarking process. Finally, the comparative evaluation of the hybrid algorithm will be done with the existing and previous technique using different images and the performance of the extended algorithm will be analyzed using the PSNR, NC with convergence rate.

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.

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

scholarly journals A Microstrip Antenna Design Using an Heuristic Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 25
Author(s):  
I Made Adhi Wiryawan ◽  
Maria Veronica Astrid Wahyuningtyas ◽  
Anugerah Galang Persada ◽  
Dyonisius Dony Ariananda
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Return Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Algorithm Design ◽  
Particle Swarm ◽  
Microstrip Antennas ◽  
Swarm Optimization

Microstrip antennas have several advantages. Some of them are that they have a compact shape and small dimensions. Moreover, they are also easy to be fabricated and easily connected as well as integrated with other electronic devices. Currently, designing antennas conventionally is limited by time, energy, and experience as well as expertise. As an alternative, a way to design antennas with revolutionary methods is developed using algorithms and computing. Algorithm design techniques can overcome limitations and automatically find practical solutions that usually take a long time to discover. The particle swarm optimization algorithm and a genetic algorithm can find solutions from microstrip antennas. Objective functions play an essential role in heuristic algorithms. With a proper objective function, simulation results are obtained on the particle swarm optimization algorithm with a return loss value of -47.837, VSWR of 1.0083, and impedance of 46.805 Ω. In contrast, the genetic algorithm obtains return loss of -16.157 dB, impedance of 50.233 Ω, and VSWR of 1.3687.

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.

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