scholarly journals Adaptive Plan System of Swarm Intelligent using Differential Evolution with Genetic Algorithm

2013 ◽  
Vol 7 (3) ◽  
pp. 458-473 ◽  
Author(s):  
Hieu PHAM ◽  
Hiroshi HASEGAWA
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

scholarly journals An Adaptive Genetic Algorithm-based Background Elimination Model for English Text

2021 ◽  
Author(s):  
Tang Xiaohui
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Differential Evolution ◽  
Recognition Accuracy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
English Text ◽  
Adaptive Genetic Algorithm ◽  
Feature Sets ◽  
Background Elimination

Abstract In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. A genetic algorithm is introduced to iterate the feature sets of angles and line segments, and the optimal features are finally derived in the process of cross evolution of generations to improve the recognition accuracy. And the character library is used as input items for average grouping for experiments, and the obtained feature sets are put into the position matrix and compared with the samples in the database one by one. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm.

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

2021 ◽  
pp. 1-34
Author(s):  
Shailendra Aote ◽  
Mukesh M. Raghuwanshi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mathematical Optimization ◽  
Evolutionary Computing ◽  
Swarm Optimization

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

Multi-Objective Optimization of Two-Stage Thermo-Electric Cooler Using Differential Evolution

2016 ◽  
pp. 139-170
Author(s):  
Doan V. K. Khanh ◽  
Pandian Vasant ◽  
Irraivan Elamvazuthi ◽  
Vo N. Dieu
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Cooling Rate ◽  
Input Current ◽  
Coefficient Of Performance ◽  
Multi Objective Optimization ◽  
Two Stage ◽  
Thermo Electric ◽  
Multi Objective ◽  
Technical Issues

In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. A input current to hot side and cold side of and the number ratio between the hot stage and cold stage are searched the optima solutions. Thermal resistance is taken into consideration. The results of optimization obtained by using differential evolution were validated by comparing with those obtained by using genetic algorithm and show better performance in terms of stability, computational efficiency, robustness. This work revealed that differential evolution more stable than genetic algorithm and the Pareto front obtained from multi-objective optimization balances the important role between cooling rate and coefficient of performance.

scholarly journals Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Monika Kumari ◽  
G. Sahoo
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Resource Provisioning ◽  
Vertical Scaling ◽  
Predator Prey ◽  
Average Completion Time ◽  
Predator Prey Model ◽  
De Algorithm ◽  
Optimal Resource ◽  
And Performance

Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.

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