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 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.

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.

scholarly journals Implementing the ISO 15746 Standard for Chemical Process Optimization

2016 ◽  
Author(s):  
Guodong Shao ◽  
Peter Denno ◽  
Albert Jones ◽  
Yan Lu
Keyword(s):  
Process Control ◽  
Process Optimization ◽  
Data Model ◽  
Numerical Optimization ◽  
Chemical Process ◽  
Optimization Problem ◽  
Modeling Tools ◽  
Advanced Process Control ◽  
Chemical Process Optimization ◽  
Level 2

This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization — and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times — each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control. We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.

A Modified Bat Algorithm Based on Gaussian Distribution for Solving Optimization Problem

2016 ◽  
Vol 13 (1) ◽  
pp. 706-714 ◽  
Author(s):  
Nazri Mohd Nawi ◽  
M. Z Rehman ◽  
Abdullah Khan ◽  
Haruna Chiroma ◽  
Tutut Herawan
Keyword(s):  
Gaussian Distribution ◽  
Optimization Problem ◽  
Bat Algorithm ◽  
Modified Bat Algorithm

scholarly journals Algorithm of numerical optimization of steel structures on basis of minimum weight criterion

2018 ◽  
Vol 212 ◽  
pp. 01024
Author(s):  
Tatiana Dmitrieva
Keyword(s):  
Numerical Optimization ◽  
Optimization Problem ◽  
Minimum Weight ◽  
Steel Structures ◽  
Element Model ◽  
Calculation Model ◽  
Regulatory Requirements ◽  
Short Term ◽  
Wide Range ◽  
Discrete Variations

The description of the algorithm of steel structures optimal design is described in the article. The calculation model is represented by flat and spatial rod elements. The problem of optimization is resolved with the help of mathematical programming. The material consumption is taken as a criterion of optimality. A wide range of inspections is presented in the form of regulatory requirements for strength, rigidity and local stability. The geometry parameters of the sections are varied, as well as the external geometry of the nodes of the final element model. Both continuous and discrete variations might be possible. The algorithm is implemented in two versions: on the basis of imitating the short-term problem and with the direct reference to the target and restrictive functions at each step of optimization process. The results of the solution of the steel framework optimization problem during on-going and discrete variations of geometric parameters are presented. The solutions obtained are estimated on uniqueness.

scholarly journals The choice of key geometric parameters in the numerical optimization of centrifugal compressor impellers

2018 ◽  
Vol 245 ◽  
pp. 09008 ◽  
Author(s):  
Vladimir Neverov ◽  
Yuri Kozhukhov ◽  
Sergey Kartashov ◽  
Vyacheslav Ivanov
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Centrifugal Compressor ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Geometric Parameters ◽  
Optimization Parameters ◽  
Standard Values ◽  
Parameter Values ◽  
Influence Degree

The article deals with the choice of key geometric parameters and the range of their variation in solving the optimization problem of centrifugal compressor impellers using computational fluid dynamics. The study was carried out using Numeca Fine / Turbo package. The influence of more than 10 geometric parameters on the efficiency and the head of the impeller was considered. The influence degree evaluation of investigated optimization parameters was provide by changing the parameters value in a preset range and analyzing their impact on the efficiency and head of the impeller. As a result, the main geometric parameters of optimization, which should be considered first, were identified. Other parameters may not be considered within the optimization problem, and can be assigned to the standard values. In addition, recommendations on optimal ranges of parameter values were given.

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