CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

Xu, Xiaolong; Rong, Hanzhong; Trovati, Marcello; Liptrott, Mark; Bessis, Nik

doi:10.1007/s00500-016-2383-8

Cited by 79 publications

(27 citation statements)

References 21 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, some scholars have applied it to the optimization of neural network weights and achieved good results. Based on the three inherent properties of the chaos, including stochastic property, ergodicity, and regularity [39], the new superior individuals are reproduced by chaotic searching on the current global best individuals. For the regularity and ergodicity property, the chaos searching can traverse all states without repeating within a certain range.…”

Section: Offline Tuning Based On the Cpsomentioning

confidence: 99%

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

et al. 2019

View full text Add to dashboard Cite

This paper presents a composite parameter optimization method based on the chaos particle swarm optimization and the back propagation algorithms for a fuzzy neural network/proportion integration differentiation compound controller, which is applied for an aerial inertially stabilized platform for aerial remote sensing applications. Firstly, a compound controller combining both the adaptive fuzzy neural network and traditional PID control methods is developed to deal with the contradiction between the control precision and robustness due to disturbances. Then, on the basis of both the chaos particle swarm optimization and the back propagation compound algorithms, the parameters of the fuzzy neural network/PID compound controller are optimized offline and fine-tuned online, respectively. In this way, the compound controller can achieve good adaptive convergence so as to get high stabilization precision under the multisource dynamic disturbance environment. To verify the method, the simulations are carried out. The results show that the composite parameter optimization method can effectively enhance the convergence of the controller, by which the stabilization precision and disturbance rejection capability of the proposed fuzzy neural network/PID compound controller are improved obviously.

show abstract

Section: Offline Tuning Based On the Cpsomentioning

confidence: 99%

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In this section, we compare the PS-CTPSO algorithm, the GB-PSO algorithm (proposed in [19]), and the improved PS-CSPSO algorithm (based on CS-PSO [25]) on the same Web Service datasets. More specifically, the order of magnitudes of dataset was defined as 30, 90, 180, 450 and 1350, respectively.…”

Section: Experimental Data and Performance Indicatorsmentioning

confidence: 99%

Predatory Search-based Chaos Turbo Particle Swarm Optimisation (PS-CTPSO): A new particle swarm optimisation algorithm for Web service combination problems

Rong

Pereira

et al. 2018

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

Web service combinatorial optimisation is an NP problem (that is, characterised by a nondeterministic polynomial time solution), based on the logical relationship between each service pair. As a consequence, obtaining the best Web service composition scheme is typically a complex task. In this article, we propose the the Predatory Search-based Chaos Turbo Particle Swarm Optimization (PS-CTPSO) algorithm, a chaotic particle swarm optimisation algorithm based on the predatory search strategy, which has significant potential to enhance the overall performance of the Autonomous Cloud. This is achieved by integrating a predatory search and cotangent sequence strategies with the particle swarm optimisation algorithm. More specifically, the PS-CTPSO algorithm identifies a feasible service via a global search, and subsequently, it obtains suitable candidate services within the corresponding chain. The different Web services are grouped into the same class, depending on whether they have the same input and output sets, thus reducing the number of combinations and improving the searching efficiency. In the initialisation phase, the PS-CTPSO component introduces the cotangent method, rather than a ran

show abstract

“…The particle swarm optimization (PSO) has the limitation of prematurely convergence and it doesn't explore the available search space due to more dependency on the local best positions of swarms. Previously researchers had improved the PSO global nature by introducing chaotic mapping in the current position in every iteration [24]] [25][26] [27]. They also focused on updating the values of random constants 1 , 2 chaotically.…”

Section: Proposed Optimization Solutionmentioning

confidence: 99%

“…We propose a novel hole healing approach based on the hybrid Chaotic PSO GSA optimization algorithm. The papers form [24][25][26][27] reflected the Chaotic PSO used for different applications. In [24] the overall convergence improved by update the value of r1 and r2 in PSO chaotically.…”

Section: Introductionmentioning

confidence: 99%

“…In [24] the overall convergence improved by update the value of r1 and r2 in PSO chaotically. The addition of chaotic nature by logistic mapping in the current position of the swarm and update it with the usual formula [25,27]. In [26] algorithm generates the initial swarm chaotically using logistic mapping and add chaotic disturbance in gravitational constant of GSA to update the best position of redundant nodes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks

Mehta¹,

Malik

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

In the WSN network, nodes are always deprived of battery and can't be in operation for a long time. Some nodes may die sooner than others, creating a void in the area. Our work in this article covered this hole area efficiently with the introduction of a novel hybrid optimization algorithm. Our algorithm makes the locally converging particle swarm optimization to a global optimization algorithm by ensembling it with the Gravitational search algorithm. The perturbation in this algorithm is also reduced by logistic chaotic mapping. The current solution has detected the holes and healed them with additional redundant nodes. The Delaunay triangulation method detected the number of holes with the improved algebraic approach. The results are validated by extensive experimentation with different sensing range and node's density. Our method seems to perform well than state of the art schemes. We have also shown that increasing the redundant nodes, more coverage can be achieved.

show abstract

CS-PSO: chaotic particle swarm optimization algorithm for solving combinatorial optimization problems

Cited by 79 publications

References 21 publications

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

Predatory Search-based Chaos Turbo Particle Swarm Optimisation (PS-CTPSO): A new particle swarm optimisation algorithm for Web service combination problems

A Swarm Intelligence Based Coverage Hole Healing Approach for Wireless Sensor Networks

Contact Info

Product

Resources

About