Multi-population techniques in nature inspired optimization algorithms: A comprehensive survey

2019 ◽  
Vol 44 ◽  
pp. 365-387 ◽  
Author(s):  
Haiping Ma ◽  
Shigen Shen ◽  
Mei Yu ◽  
Zhile Yang ◽  
Minrui Fei ◽  
...  
Keyword(s):  
Optimization Algorithms ◽  
Comprehensive Survey

A comprehensive survey of brain storm optimization algorithms

2017 ◽  
Author(s):  
Shi Cheng ◽  
Yifei Sun ◽  
Junfeng Chen ◽  
Quande Qin ◽  
Xianghua Chu ◽  
...  
Keyword(s):  
Optimization Algorithms ◽  
Brain Storm Optimization ◽  
Comprehensive Survey

A Review of Teaching and Learning through Practice of Optimization Algorithms

2015 ◽  
pp. 65-87 ◽  
Author(s):  
J. Ángel Velázquez-Iturbide ◽  
Ouafae Debdi ◽  
Maximiliano Paredes-Velasco
Keyword(s):  
Science Education ◽  
Computer Science ◽  
Science Teachers ◽  
Teaching And Learning ◽  
Educational Software ◽  
Computer Science Education ◽  
Current Knowledge ◽  
Optimization Algorithms ◽  
Software Systems ◽  
Comprehensive Survey

Algorithmics is an important core subject matter in computer science education. In particular, optimization algorithms are some of the most difficult to master because their problem statement includes an additional property, namely optimality. The chapter contains a comprehensive survey of the teaching and learning through practice of optimization algorithms. In particular, three important issues are reviewed. Firstly, the authors review educational methods which partially or completely address optimization algorithms. Secondly, educational software systems are reviewed and classified according to technical and educational criteria. Thirdly, students' difficulties and misunderstandings regarding optimization algorithms are presented. The chapter intends to consolidate current knowledge about the education of this class of algorithms for both computer science teachers and computer science education researchers.

Automated Design of Deep Neural Networks

2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
El-Ghazali Talbi
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Optimization Algorithms ◽  
Search Space ◽  
Parallel Optimization ◽  
Objective Functions ◽  
Mixed Design ◽  
Comprehensive Survey ◽  
Variation Operators

In recent years, research in applying optimization approaches in the automatic design of deep neural networks has become increasingly popular. Although various approaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this hot research topic. In this article, we propose a unified way to describe the various optimization algorithms that focus on common and important search components of optimization algorithms: representation, objective function, constraints, initial solution(s), and variation operators. In addition to large-scale search space, the problem is characterized by its variable mixed design space, it is very expensive, and it has multiple blackbox objective functions. Hence, this unified methodology has been extended to advanced optimization approaches, such as surrogate-based, multi-objective, and parallel optimization.

scholarly journals Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 101
Author(s):  
Laith Abualigah ◽  
Amir H. Gandomi ◽  
Mohamed Abd Elaziz ◽  
Husam Al Hamad ◽  
Mahmoud Omari ◽  
...  
Keyword(s):  
Relevant Literature ◽  
Optimization Algorithms ◽  
Text Clustering ◽  
Learning Problems ◽  
Heuristic Optimization ◽  
Future Research ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Review Reports ◽  
Clustering Problems

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.

Doxastic Naturalism and Hume's Voice in theDialogues

2016 ◽  
Vol 14 (3) ◽  
pp. 253-274 ◽  
Author(s):  
C. M. Lorkowski
Keyword(s):  
Comprehensive Survey ◽  
Naturalistic Interpretation ◽  
New Evidence

I argue that acknowledging Hume as a doxastic naturalist about belief in a deity allows an elegant, holistic reading of his Dialogues. It supports a reading in which Hume's spokesperson is Philo throughout, and enlightens many of the interpretive difficulties of the work. In arguing this, I perform a comprehensive survey of evidence for and against Philo as Hume's voice, bringing new evidence to bear against the interpretation of Hume as Cleanthes and against the amalgamation view while correcting several standard mistakes. I ultimately isolate the interpretation of Philo's Reversal at the end of the Dialogues as of paramount importance, and show how my naturalistic interpretation makes this, and other notoriously difficult passages, unproblematic.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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