scholarly journals AncDE with gaussian distribution for numerical optimization problem

2019 ◽  
Vol 5 (1) ◽  
pp. 48
Author(s):  
Siti Khadijah Mohd Salleh ◽  
Siti Azirah Asmai ◽  
Zuraida Abal Abas ◽  
Abdul Samad Shibghatullah ◽  
Diarmuid O'Donoghue
Keyword(s):  
Differential Evolution ◽  
Gaussian Distribution ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Current Generation ◽  
Two Parameter ◽  
Selection Of

This work is introducing an enhanced Differential Evolution (DE) called AncDE. This proposed algorithm is using an additional population from the current generation and located it as ancestor. There are two parameter controllers to manage the selection of ancestor vector; aup for selection frequency and arp for age of selection. In this work we were applying Gaussian distribution on aup and we tested it on CEC 2015 Numerical Optimization Problem. Standard Differential Evolution will act as the benchmark. The result shows that AncDE with Gaussian approach has produced better result than standard DE.

scholarly journals AncDE with Gaussian Distribution for Numerical Optimization Problem

2019 ◽  
Author(s):  
Siti Khadijah Mohd Salleh ◽  
Siti Azirah Asmai ◽  
Zuraida Abal Abas ◽  
Abd Samad Shibghatullah ◽  
Diarmuid O'Donoghue
Keyword(s):  
Gaussian Distribution ◽  
Numerical Optimization ◽  
Optimization Problem

scholarly journals Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

2021 ◽  
Vol 24 (2) ◽  
pp. 1-35
Author(s):  
Isabel Wagner ◽  
Iryna Yevseyeva
Keyword(s):  
Optimization Problem ◽  
Evolutionary Optimization ◽  
Multiple Objectives ◽  
Shared Value ◽  
Vehicular Communications ◽  
Open Problems ◽  
Value Range ◽  
Genomic Privacy ◽  
Metaheuristic Optimization Algorithms ◽  
Selection Of

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

General Method for Optimizing Composite Patch Repairs

2015 ◽  
Vol 1120-1121 ◽  
pp. 670-674
Author(s):  
Abdelmadjid Ait Yala ◽  
Abderrahmanne Akkouche
Keyword(s):  
Mechanical Properties ◽  
Stress Intensity Factor ◽  
Stress Intensity ◽  
Intensity Factor ◽  
Optimization Problem ◽  
Composite Patch ◽  
Matlab Program ◽  
Patch Repairs ◽  
Selection Of ◽  
General Method

The aim of this work is to define a general method for the optimization of composite patch repairing. Fracture mechanics theory shows that the stress intensity factor tends towards an asymptotic limit K∞.This limit is given by Rose’s formula and is a function of the thicknesses and mechanical properties of the cracked plate, the composite patch and the adhesive. The proposed approach consists in considering this limit as an objective function that needs to be minimized. In deed lowering this asymptote will reduce the values of the stress intensity factor hence optimize the repair. However to be effective this robust design must satisfy the stiffness ratio criteria. The resolution of this double objective optimization problem with Matlab program allowed us determine the appropriate geometric and mechanical properties that allow the optimum design; that is the selection of the adhesive, the patch and their respective thicknesses.

A decremental stochastic fractal differential evolution for global numerical optimization

2016 ◽  
Vol 372 ◽  
pp. 470-491 ◽  
Author(s):  
Noor H. Awad ◽  
Mostafa Z. Ali ◽  
Ponnuthurai N. Suganthan ◽  
Edward Jaser
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Global Numerical Optimization

scholarly journals A New Fruit Fly Optimization Algorithm Based on Differential Evolution

2015 ◽  
Vol 3 (4) ◽  
pp. 365-373 ◽  
Author(s):  
Dabin Zhang ◽  
Jia Ye ◽  
Zhigang Zhou ◽  
Yuqi Luan
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Local Extremum ◽  
Experimental Results ◽  
Fruit Fly Optimization Algorithm ◽  
A Algorithm ◽  
Fruit Fly Optimization ◽  
Searching Ability ◽  
Selection Of

Abstract In order to overcome the problem of low convergence precision and easily relapsing into local extremum in fruit fly optimization algorithm (FOA), this paper adds the idea of differential evolution to fruit fly optimization algorithm so as to optimizing and a algorithm of fruit fly optimization based on differential evolution is proposed (FOADE). Adding the operating of mutation, crossover and selection of differential evolution to FOA after each iteration, which can jump out local extremum and continue to optimize. Compared to FOA, the experimental results show that FOADE has the advantages of better global searching ability, faster convergence and more precise convergence.

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