Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
22
0

Publication Types

Select...
5
4

Relationship

0
9

Authors

Journals

citations
Cited by 27 publications
(22 citation statements)
references
References 6 publications
0
22
0
Order By: Relevance
“…In addition, from a theoretical aspect, we provide the convergence and stability analysis of the PSO with constriction coefficient, which is much simpler than the previous analysis [9]. Note that the constriction coefficient has been used to improve the performance of PSO [5,9].…”
Section: Introductionmentioning
confidence: 99%
“…In addition, from a theoretical aspect, we provide the convergence and stability analysis of the PSO with constriction coefficient, which is much simpler than the previous analysis [9]. Note that the constriction coefficient has been used to improve the performance of PSO [5,9].…”
Section: Introductionmentioning
confidence: 99%
“…Sahoo et al [9] explained the various natureinspired meta-heuristic algorithms and their performance. Supapornkansomkeat et al [15] [12] explained the aspects of developments, applications, and resources which are related to particle swarm optimization. According to Abdurazik [3], testing criteria is based on the collaboration diagrams for static checking and dynamic testing.…”
Section: Related Workmentioning
confidence: 99%
“…• inertia weight (explorative factor) can decrease with the number of iterations or can be the function of particle performance [15], • constriction factor can be introduced (canonical PSO) as constant one or variable one [16], • x best global can be redefined to represent best solution found so far by particle's neighbourhood limited by the distance (the radius of the neighbourhood is infinite in the rule as presented in (2)) or by the fixed number of neighbours (particles closest in distance) [17], • particle can be attracted by every other particles in its neighbourhood (i.e. knowledge of x best i is distributed among all particles) [18], • boundary conditions can be introduced with the help of absorbing, reflecting or invisible walls [19].…”
Section: Particle Swarm Optimization Of Artificial-neural-network-basmentioning
confidence: 99%
“…However, the constricted PSO with a constriction factor defined as constant (5) in many benchmark optimization problems does not require velocity clamping to deliver satisfactory speed of convergence. One can alternatively use modified strategies for the constriction factor, including time-dependent strategies and random effects [16].…”
Section: Particle Swarm Optimization Of Artificial-neural-network-basmentioning
confidence: 99%