2013
DOI: 10.1007/978-3-642-30504-7_7
|View full text |Cite
|
Sign up to set email alerts
|

Multilocal Programming and Applications

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
6
0

Year Published

2014
2014
2021
2021

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 7 publications
(6 citation statements)
references
References 60 publications
0
6
0
Order By: Relevance
“…It is possible to prove that the sequence of solutions {x * (µ k )}, from (8), will converge to the solution x * of (1) [10], [14].…”
Section: A Penalty Methods With the L 1 Penalty Functionmentioning
confidence: 99%
See 1 more Smart Citation
“…It is possible to prove that the sequence of solutions {x * (µ k )}, from (8), will converge to the solution x * of (1) [10], [14].…”
Section: A Penalty Methods With the L 1 Penalty Functionmentioning
confidence: 99%
“…The Stretched Simulated Annealing algorithm stops when no new optimum is identified after r consecutive runs. For more details see [14], [15].…”
Section: A Stretched Simulated Annealing (Ssa)mentioning
confidence: 99%
“…The Stretched Simulated Annealing algorithm stops when no new optimum is identified after r consecutive runs. [15,16] provide more details.…”
Section: Stretched Simulated Annealingmentioning
confidence: 99%
“…Methods for solving multilocal optimization problems include evolutionary algorithms, such as genetic [1] and particle swarm [13] algorithms, and additional contributions, like [6,15,20,23,24]. Stretched Simulated Annealing (SSA) was also proposed [14], combining simulated annealing and a stretching function technique, to solve unconstrained multilocal programming problems.…”
Section: Introductionmentioning
confidence: 99%
“…The SSA algorithm stops when no new optimum is identified after l consecutive runs. For more details see [15,18].…”
Section: Stretched Simulated Annealingmentioning
confidence: 99%