The use of differential evolution algorithm for solving chemical engineering problems

2016 ◽  
Vol 32 (2) ◽  
Author(s):  
Elena Niculina Dragoi ◽  
Silvia Curteanu
Keyword(s):  
Differential Evolution ◽  
Chemical Engineering ◽  
Differential Evolution Algorithm ◽  
Real Life ◽  
Point Of View ◽  
Large Area ◽  
Specific Chemical ◽  
Life Problems ◽  
Engineering Problems ◽  
And Performance

AbstractDifferential evolution (DE), belonging to the evolutionary algorithm class, is a simple and powerful optimizer with great potential for solving different types of synthetic and real-life problems. Optimization is an important aspect in the chemical engineering area, especially when striving to obtain the best results with a minimum of consumed resources and a minimum of additional by-products. From the optimization point of view, DE seems to be an attractive approach for many researchers who are trying to improve existing systems or to design new ones. In this context, here, a review of the most important approaches applying different versions of DE (simple, modified, or hybridized) for solving specific chemical engineering problems is realized. Based on the idea that optimization can be performed at different levels, two distinct cases were considered – process and model optimization. In both cases, there are a multitude of problems solved, from different points of view and with various parameters, this large area of successful applications indicating the flexibility and performance of DE.

A New Differential Evolution Algorithm and Its Application to Real Life Problems

2009 ◽  
Author(s):  
Millie Pant ◽  
Musrrat Ali ◽  
V. P. Singh ◽  
A. H. Siddiqi ◽  
M. Brokate ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Real Life ◽  
Life Problems ◽  
Evolution Algorithm

Logistic Map and Exponential Scaling Factor based Differential Evolution

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Differential Evolution ◽  
Logistic Map ◽  
Differential Evolution Algorithm ◽  
Scaling Factor ◽  
Exploration And Exploitation ◽  
Engineering Problems ◽  
Evolutionary Technique ◽  
Evolution Algorithm ◽  
State Of Art ◽  
Modified Algorithm

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.

Data Literacy

2017 ◽  
Vol 8 (2) ◽  
pp. 14-38 ◽  
Author(s):  
Dimitar Christozov ◽  
Katia Rasheva-Yordanova
Keyword(s):  
Big Data ◽  
Domain Knowledge ◽  
Real Life ◽  
Big Data Analytics ◽  
Point Of View ◽  
Life Problems ◽  
Big Data Applications ◽  
Master Data ◽  
Big Data Application ◽  
Data Driven Decisions

The article shares the authors' experiences in training bachelor-level students to explore Big Data applications in solving nowadays problems. The article discusses curriculum issues and pedagogical techniques connected to developing Big Data competencies. The following objectives are targeted: The importance and impact of making rational, data driven decisions in the Big Data era; Complexity of developing and exploring a Big Data Application in solving real life problems; Learning skills to adopt and explore emerging technologies; and Knowledge and skills to interpret and communicate results of data analysis via combining domain knowledge with system expertise. The curriculum covers: The two general uses of Big Data Analytics Applications, which are well distinguished from the point of view of end-user's objectives (presenting and visualizing data via aggregation and summarization [data warehousing: data cubes, dash boards, etc.] and learning from Data [data mining techniques]); Organization of Data Sources: distinction of Master Data from Operational Data, in particular; Extract-Transform-Load (ETL) process; and Informing vs. Misinforming, including the issue of over-trust vs. under-trust of obtained analytical results.

Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

2019 ◽  
Vol 10 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Ali Khater Mohamed ◽  
Ehab Z. Elfeky ◽  
Mohamed Saleh
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Test Problems ◽  
New Mutation ◽  
De Algorithm ◽  
Engineering Problems ◽  
Evolution Algorithm ◽  
Mutation Scheme ◽  
Exploration Exploitation

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC'2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.

The Role of BIM in the Architectural Design Process

2010 ◽  
pp. 587-618 ◽  
Author(s):  
Anita Moum
Keyword(s):  
Design Process ◽  
Architectural Design ◽  
Real Life ◽  
Point Of View ◽  
Research Approach ◽  
Factors Affecting ◽  
Life Problems ◽  
The Many ◽  
Holistic Research

The objective of this chapter is to identify the role of BIMs in the architectural design process from the practitioners’ point of view. The chapter investigates the main factors affecting the practitioners’ use of BIM, and how BIM impacts their work and interactions. The chapter presents a holistic research approach as well as the findings from its application in four real-life projects. In these projects, much of the practitioners’ focus was on upgrading skills and improving technology. Nevertheless, a number of their challenges were linked to the nature of the architectural design process, particularly to its “hardto- grasp” iterative and intuitive features. A conclusion of this research indicates that the role of BIM is affected by the many interdependencies, relations and interfaces embedded in the highly complex and partly unpredictable real world practice. A future challenge would be to understand, master and balance these relationships - upstream and downstream across multiple levels, processes and activities. The presented holistic research approach and the related findings contributed to research which aimed to embrace the complexity of real-life problems and gain a more comprehensive understanding of what is happening in practice.

scholarly journals New trends in constraint satisfaction, planning, and scheduling: a survey

2010 ◽  
Vol 25 (3) ◽  
pp. 249-279 ◽  
Author(s):  
Roman Barták ◽  
Miguel A. Salido ◽  
Francesca Rossi
Keyword(s):  
Constraint Satisfaction ◽  
Real Life ◽  
Point Of View ◽  
New Techniques ◽  
Planning And Scheduling ◽  
Research Issues ◽  
Open Research ◽  
Life Problems ◽  
Substantial Effort

AbstractDuring recent years, the development of new techniques for constraint satisfaction, planning, and scheduling has received increased attention, and substantial effort has been invested in trying to exploit such techniques to find solutions to real-life problems. In this paper, we present a survey on constraint satisfaction, planning, and scheduling from the Artificial Intelligence point of view. In particular, we present the main definitions and techniques, and discuss possible ways of integrating such techniques. We also analyze the role of constraint satisfaction in planning and scheduling, and hint at some open research issues related to planning, scheduling, and constraint satisfaction.

scholarly journals Tolerating Ambiguity: Reflections on the Schrems II Ruling

2021 ◽  
Vol 4 (2) ◽  
pp. 75-85
Author(s):  
Susanna Lindroos-Hovinheimo
Keyword(s):  
Data Protection ◽  
Real Life ◽  
Point Of View ◽  
Global Politics ◽  
Know How ◽  
Logical Conclusion ◽  
Life Problems ◽  
European Court ◽  
Data Protection Law ◽  
Sensitive Issues

This paper considers the European Court of Justice’s Schrems II ruling from a variety of angles. From a strictly legal point of view, considering the GDPR, the CJEU came to a logical conclusion. In this paper, I nevertheless try to think about other ways of understanding the dispute and the ruling. In addition to data protection law, the case is about surveillance, platform power, resistance, global politics, data territoriality and the Court’s competence. These sensitive issues come forth when the strict data protection issues are set aside and a slightly more open analysis undertaken. In the end, however, the ruling does bring about real-life problems that pertain to data protection law. Transfers of data to third countries are a pressing problem that no one seems to know how to solve. 

Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

2019 ◽  
Vol 13 (1) ◽  
pp. 36-61 ◽  
Author(s):  
Kangshun Li ◽  
Zhuozhi Liang ◽  
Shuling Yang ◽  
Zhangxing Chen ◽  
Hui Wang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Dynamic Properties ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Fitness Distance Correlation ◽  
And Performance

Dynamic fitness landscape analyses contain different metrics to attempt to analyze optimization problems. In this article, some of dynamic fitness landscape metrics are selected to discuss differential evolution (DE) algorithm properties and performance. Based on traditional differential evolution algorithm, benchmark functions and dynamic fitness landscape measures such as fitness distance correlation for calculating the distance to the nearest global optimum, ruggedness based on entropy, dynamic severity for estimating dynamic properties, a fitness cloud for getting a visual rendering of evolvability and a gradient for analyzing micro changes of benchmark functions in differential evolution algorithm, the authors obtain useful results and try to apply effective data, figures and graphs to analyze the performance differential evolution algorithm and make conclusions. Those metrics have great value and more details as DE performance.

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