Torrent details for "Moreira O. Mathematical Optimization Terminology 2019" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
9.2 MB
Info Hash:
83FB40E60CBEEAB8A1C8687D11A694E35C4FC8A8
Added By:
Added:
Nov. 15, 2025, 12:27 p.m.
Stats:
|
(Last updated: Nov. 15, 2025, 12:27 p.m.)
| File | Size |
|---|---|
| Moreira O. Mathematical Optimization Terminology 2019.pdf | 9.2 MB |
Name
DL
Uploader
Size
S/L
Added
-
367.4 MB
[45
/
10]
2023-07-02
| Uploaded by indexFroggy | Size 367.4 MB | Health [ 45 /10 ] | Added 2023-07-02 |
-
17.5 MB
[39
/
4]
2023-07-27
| Uploaded by indexFroggy | Size 17.5 MB | Health [ 39 /4 ] | Added 2023-07-27 |
-
23.6 MB
[100
/
27]
2025-12-21
| Uploaded by andryold1 | Size 23.6 MB | Health [ 100 /27 ] | Added 2025-12-21 |
-
1020.8 MB
[25
/
15]
2025-08-09
| Uploaded by Anonymous | Size 1020.8 MB | Health [ 25 /15 ] | Added 2025-08-09 |
NOTE
SOURCE: Moreira O. Mathematical Optimization Terminology 2019
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format
Mathematical Optimization Terminology is an edited book consisting of a collection of 11 open-access and peer-reviewed scientific articles featuring several contemporaneous advanced optimization methods and algorithms. The selected articles aim to help readers gain a theoretical understanding and a broader view of mathematical optimization problems by including essential formulations, illustrative examples, and real-world applications.
Chapters 1 and 2 introduce the reader to a multi-objective optimization problem, inverse and surrogate modeling. Evolutionary algorithms are also introduced in these first two chapters and next following chapters, up to Chapter 11, feature several applications of these type of nature-inspired multi-objective optimization algorithms. These are the following evolutionary and multi-objective optimization methods and algorithms covered in this first section of the book:
Genetic algorithm (GA) - an optimization algorithm based on biological evolution and Darwin’s principle;
Differential evolution (DE) - an optimization algorithm based on a stochastic search method using populations;
Particle swarm optimization (PSO) - an optimization algorithm inspired by a bird flocking, fish schooling and swarming theory;
Cuckoo search (CS) - a stochastic optimization algorithm based on the brood parasitism behavior of the cuckoo bird.
Ant colony optimization (ACO) - an optimization algorithm based on the behavior of real ants when they forage for food which relies on pheromone communications.
Moth flame optimization algorithm - an optimization algorithm based on the navigation strategy of moths in nature;
Social group optimization (SGO) - a new population-based optimization algorithm;
This edited book is directed towards computational scientists, engineers or anyone whose research work requires the solution to an optimization problem
×