Pith. Sign in

RECORD

Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Anvar Bahrampour, Omid Mohamad Nezami

Neural and Evolutionary Computing · Submitted 2 Jul 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We compared the promoted algorithm with similar new approaches and according to the numerical results, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO, CLPSO and APSO in most of the 12 standard benchmark problems with different properties taken in this study.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID1807.00438 (version 1)
Pith IDDFBRANHT, Pith's permanent record of this paper and its review
CitationAnvar Bahrampour, Omid Mohamad Nezami. "Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)." arXiv:1807.00438 (2018). https://pith.science/paper/DFBRANHT
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,010 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.07 · human participants 0.01 · controlled comparison 0.86 · randomized assignment 0.02 · states quantitative result 0.63 · compares with prior results 0.98 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Premature convergence in particle swarm optimization (PSO) algorithm usually leads to gaining local optimum and preventing from surveying those regions of solution space which have optimal points in. In this paper, by applying special mechanisms, suitable regions were detected and then swarm was guided to them by dispersing part of particles on proper times. This process is called dynamic swarm dispersion in PSO (DSDPSO) algorithm. In order to specify the proper times and to rein the evolutionary process alternating between exploring and exploiting behaviors, we used a diversity measuring approach and implemented the dispersion mechanism. To promote the performance of DSDPSO algorithm, three different policies including particle relocation, velocity settings of dispersed particles and parameters setting were applied. We compared the promoted algorithm with similar new approaches and according to the numerical results, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO, CLPSO and APSO in most of the 12 standard benchmark problems with different properties taken in this study.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Premature convergence in particle swarm optimization (PSO) algorithm usually leads to gaining local optimum and preventing from surveying those regions of solution space which have optimal points in. background 1.00 0.06 0.03 asserted 0.86
s1 In this paper, by applying special mechanisms, suitable regions were detected and then swarm was guided to them by dispersing part of particles on proper times. method 0.75 0.90 0.09 asserted 1.00
s2 This process is called dynamic swarm dispersion in PSO (DSDPSO) algorithm. contribution 0.61 0.56 0.04 asserted 0.99
s3 In order to specify the proper times and to rein the evolutionary process alternating between exploring and exploiting behaviors, we used a diversity measuring approach and implemented the dispersion mechanism. method 0.99 0.94 0.03 asserted 1.00
s4 To promote the performance of DSDPSO algorithm, three different policies including particle relocation, velocity settings of dispersed particles and parameters setting were applied. method 0.87 0.93 0.04 asserted 0.99
s5 We compared the promoted algorithm with similar new approaches and according to the numerical results, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO, CLPSO and APSO in most of the 12 standard benchmark problems with different properties taken in this study. result 1.00 0.97 0.89 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.99.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Premature convergence in particle swarm optimization (PSO) algorithm usually leads to gaining local optimum and preventing from surveying those regions of solution space which have optimal points in. In this paper, by applying special mechanisms, suitable regions were detected and then swarm was guided to them by dispersing part of particles on proper times. This process is called dynamic swarm dispersion in PSO (DSDPSO) algorithm. In order to specify the proper times and to rein the evolutionary process alternating between exploring and exploiting behaviors, we used a diversity measuring approach and implemented the dispersion mechanism. To promote the performance of DSDPSO algorithm, three different policies including particle relocation, velocity settings of dispersed particles and parameters setting were applied. We compared the promoted algorithm with similar new approaches and according to the numerical results, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO, CLPSO and APSO in most of the 12 standard benchmark problems with different properties taken in this study.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.