Modification of particle swarm optimization with human simulated property

Tang, Ruoli; Fang, Yanjun

doi:10.1016/j.neucom.2014.11.015

Cited by 33 publications

(25 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the same time, referring to the parameters of the literature [1], compared with the existing methods, respectively, Local PSO (LPSO) [35], Hierarchical PSO (PS2O) [36], Multi-Population Cooperative PSO (MCPSO) [37], Competitive and Cooperative PSO with Information Sharing Mechanism (CCPSO-ISM) [38], and Human-Brain Simulated PSO (HSPSO) [39], Fully Informed PSO (FIPSO) [40], and Fitness-Distance-Ratio Based PSO (FDRPSO) [41]. e mean, Std, and median after 30 Monte Carlo simulations were obtained.…”

Section: Comparison Between the Combinations Of Grouping Methods And Smentioning

confidence: 99%

Improved Adaptive Holonic Particle Swarm Optimization

Li¹,

Jin²,

Wang³

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. e HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. erefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. e performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. erefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. e HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. is paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

show abstract

Section: Comparison Between the Combinations Of Grouping Methods And Smentioning

confidence: 99%

Improved Adaptive Holonic Particle Swarm Optimization

Li¹,

Jin²,

Wang³

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…for each particle i do 13) for each dimension d do 14) vi,d = w*vi,d + C1*Random(0,1)*( xi,dpworsti,d) + C2*Random(0,1)*( xi,dgworstd) 15) xi,d = xi,d + vi,d 17) end for 18) end for 19) iterations = iterations + 1 20) while ( termination condition is false)…”

Section: Human Safety Particle Swarm Optimizationmentioning

confidence: 99%

“…If ( f( xi ) < f( pbesti ) ) then 6) pbesti = xi 7) end if 8) if ( f( pbesti ) < f( gbest ) ) then 9) gbest = pbesti 10) end if 11) If ( f( xi ) > f( pworsti ) ) then 12) pworsti = xi 13) end if 14) if ( f( pworsti ) > f( gworst ) ) then 15) gworst = pworsti 16) end if 17) end for 18) If ((iterations == 0) || (iterations%2==0)) then // for starting and even iterations 19)…”

Section: )unclassified

See 1 more Smart Citation

Novel Artificial Human Optimization Field Algorithms – The Beginning

Mustafa

Gajawada

2019

TMLAI

View full text Add to dashboard Cite

“…Tang and Fang made further improvement on the human-brain simulated particle swarm optimization. They proposed extended memory and new velocity choosing and updating strategies to give the moving direction to each particle more intelligently and help them avoid trapping into local optimum [10].…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode

Jiao¹

2017

JEEE

View full text Add to dashboard Cite

Abstract:At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velocity-updating mode to control velocity of evolved particles, which is expected to be useful for tuning the search for the globally-optimal solution. Optimization examples showed that the improved PSO is superior to the BPSO, on not only convergence precision but also computation expense.

show abstract

Modification of particle swarm optimization with human simulated property

Cited by 33 publications

References 22 publications

Improved Adaptive Holonic Particle Swarm Optimization

Improved Adaptive Holonic Particle Swarm Optimization

Novel Artificial Human Optimization Field Algorithms – The Beginning

Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode

Contact Info

Product

Resources

About