A Note On The Popularity of Stochastic Optimization Algorithms in Different Fields: A Quantitative Analysis from 2007 to 2017

Son Duy Dao

arxiv: 1907.01453 · v2 · pith:PP6GW3UKnew · submitted 2019-06-30 · 💻 cs.NE

A Note On The Popularity of Stochastic Optimization Algorithms in Different Fields: A Quantitative Analysis from 2007 to 2017

Son Duy Dao This is my paper

Pith reviewed 2026-05-25 12:12 UTC · model grok-4.3

classification 💻 cs.NE

keywords stochastic optimizationgenetic algorithmparticle swarm optimizationpublication analysisalgorithm popularityresearch fieldsquantitative study2007-2017

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The pith

Publication counts from 2007 to 2017 rank 14 stochastic optimization algorithms by how often they appear in papers from 18 fields.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tallies mentions of algorithm names such as Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization and others across publications in fields including Engineering, Computer science, Operations research, Physics, Chemistry and Robotics. It presents these counts as a way for researchers to identify which algorithms are most commonly referenced in each domain over the ten-year period. A sympathetic reader would care because the data offers a concrete basis for choosing an algorithm when facing large-scale optimization tasks rather than relying on anecdotal preference. The note lists the 18 fields explicitly and states that the resulting rankings can support algorithm selection in practice.

Core claim

Counting how many papers in each of 18 fields mention each of 14 named stochastic optimization algorithms produces field-specific popularity rankings that researchers and practitioners can consult when selecting an algorithm for complex large-scale problems.

What carries the argument

Publication mention counts of algorithm names within field-tagged papers, used as a direct proxy for relative popularity.

Load-bearing premise

The raw count of publications mentioning an algorithm name within a field accurately measures both its popularity and its suitability for solving problems in that field.

What would settle it

A direct survey of actual algorithm usage or success rates in any one of the 18 fields that shows no correlation with the publication mention counts would falsify the claim that the counts provide useful guidance for selection.

read the original abstract

Stochastic optimization algorithms are often used to solve complex large-scale optimization problems in various fields. To date, there have been a number of stochastic optimization algorithms such as Genetic Algorithm, Cuckoo Search, Tabu Search, Simulated Annealing, Particle Swarm Optimization, Ant Colony Optimization, etc. Each algorithm has some advantages and disadvantages. Currently, there is no study that can help researchers to choose the most popular optimization algorithm to deal with the problems in different research fields. In this note, a quantitative analysis of the popularity of 14 stochastic optimization algorithms in 18 different research fields in the last ten years from 2007 to 2017 is provided. This quantitative analysis can help researchers/practitioners select the best optimization algorithm to solve complex large-scale optimization problems in the fields of Engineering, Computer science, Operations research, Mathematics, Physics, Chemistry, Automation control systems, Materials science, Energy fuels, Mechanics, Telecommunications, Thermodynamics, Optics, Environmental sciences ecology, Water resources, Transportation, Construction building technology, and Robotics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper counts mentions of 14 algorithms across 18 fields but the claim that the numbers help pick the best optimizer is unsupported.

read the letter

The key point is that this is a count of how many papers mention each of 14 algorithms in 18 fields over ten years, but the idea that higher counts mean you should pick that algorithm as the best is not backed by any evidence in the work. The abstract itself shifts from popularity to suitability without any supporting data or caveats. What the paper does is compile those mention frequencies. If the counting was done consistently, it gives a factual picture of which names appear more often in the literature of each field. That snapshot is the actual contribution. The soft spot is the leap at the end. No benchmark results, success rates, or comparative performance data are included to support using the counts for selection decisions. The methods for finding the papers, handling synonyms, or dealing with false positives are not described in the abstract, which leaves the reliability of the numbers open. This kind of note might interest someone tracking publication trends in optimization, but it doesn't add new methods or insights beyond the tabulation. A reader looking for guidance on which algorithm to use would not get reliable help here. I would not bring it to a reading group or cite it in my work. It does not seem to warrant sending out for peer review, as the core is a basic count with an unsupported interpretation.

Referee Report

2 major / 1 minor

Summary. The paper performs a quantitative bibliometric analysis counting mentions of 14 stochastic optimization algorithms (Genetic Algorithm, Cuckoo Search, Tabu Search, Simulated Annealing, Particle Swarm Optimization, Ant Colony Optimization, and others) in publications across 18 fields from 2007 to 2017. It claims that the resulting popularity data can help researchers and practitioners select the best algorithm for complex large-scale optimization problems in those fields.

Significance. If the counts are reproducible and the utility claim is appropriately scoped to popularity rather than effectiveness, the tabulation supplies a descriptive snapshot of algorithm adoption trends across disciplines. This could be of limited historical interest in evolutionary computation but offers no performance benchmarks, success rates, or comparative studies, so it does not advance algorithm selection methodology.

major comments (2)

[Abstract] Abstract: The search methodology, database used, query construction, synonym handling for algorithm names, and any error estimation or confidence intervals on the counts are not described. Without these details the reported frequencies cannot be reproduced or evaluated for bias, directly undermining the quantitative foundation of the note.
[Abstract] Abstract (final paragraph): The assertion that the analysis 'can help researchers/practitioners select the best optimization algorithm' equates raw publication frequency with practical superiority. The manuscript supplies no performance data, benchmark results, or evidence linking higher mention counts to better outcomes, rendering this central utility claim unsupported.

minor comments (1)

[Abstract] Abstract: The phrasing shifts without comment from 'most popular' earlier in the paragraph to 'best' in the final sentence; a brief caveat distinguishing popularity from effectiveness would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed comments, which identify key areas where the manuscript requires greater clarity and more precise scoping of its claims. We address each point below and will revise the manuscript to improve transparency and avoid overstatement.

read point-by-point responses

Referee: [Abstract] Abstract: The search methodology, database used, query construction, synonym handling for algorithm names, and any error estimation or confidence intervals on the counts are not described. Without these details the reported frequencies cannot be reproduced or evaluated for bias, directly undermining the quantitative foundation of the note.

Authors: We agree that the current manuscript does not provide adequate methodological details. The revised version will add an explicit Methods section (or subsection) that specifies the database(s) queried, the exact search strings and Boolean operators used, how algorithm name variants and synonyms were handled, the time window and field classifications applied, and any acknowledged limitations such as potential indexing biases or lack of confidence intervals on the raw counts. revision: yes
Referee: [Abstract] Abstract (final paragraph): The assertion that the analysis 'can help researchers/practitioners select the best optimization algorithm' equates raw publication frequency with practical superiority. The manuscript supplies no performance data, benchmark results, or evidence linking higher mention counts to better outcomes, rendering this central utility claim unsupported.

Authors: We accept that the phrasing in the abstract overstates the utility of the work. The study is strictly a descriptive bibliometric note on publication counts and does not contain any performance comparisons. In the revision we will replace the claim with language that accurately reflects the contribution: the tabulated counts supply a snapshot of relative adoption trends across fields, which may serve as one informational input when researchers consider algorithm choice, but the data do not demonstrate effectiveness or superiority. revision: yes

Circularity Check

0 steps flagged

No circularity; paper is a direct tabulation of external publication counts with no derivations or self-referential steps

full rationale

The manuscript conducts a bibliometric count of mentions of 14 algorithm names across 18 fields using external publication records (2007-2017). No equations, fitted parameters, predictions, or derivations exist that could reduce to inputs by construction. The central output is a straightforward tabulation of data from outside sources. No self-citations, ansatzes, or uniqueness claims are load-bearing. The interpretive claim that counts can help select the 'best' algorithm is an untested assertion about utility, not a circular reduction in any derivation chain. This is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The analysis rests on the untested premise that publication mention counts equal popularity and suitability; no free parameters are fitted because the paper performs only counting, but the choice of 14 algorithms and 18 fields is an ad-hoc selection without stated justification.

axioms (1)

domain assumption Publication mention counts in a field accurately reflect both popularity and practical suitability of an algorithm.
Invoked in the final sentence of the abstract as the justification for using the counts to select algorithms.

pith-pipeline@v0.9.0 · 5710 in / 1220 out tokens · 25339 ms · 2026-05-25T12:12:37.030348+00:00 · methodology

A Note On The Popularity of Stochastic Optimization Algorithms in Different Fields: A Quantitative Analysis from 2007 to 2017

Core claim

What carries the argument

Load-bearing premise

What would settle it

discussion (0)