The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Problem solving by intelligent water drops
Abstract
In this paper, we propose a new problem solving algorithm called "intelligent water drops" or IWD algorithm which is based on the processes that happen in the natural river systems and the actions and reactions that take place between water drops in the river and the changes that happen in the environment that river is flowing. It is observed that a river often chooses an optimum path regarding the conditions of its surroundings to get to its ultimate goal which is often a lake or sea. These ideas are embedded into the proposed algorithm for solving the traveling salesman problem or the TSP. The IWD algorithm is tested with artificial and standard TSP problems and the experimental results demonstrate that it is a very promising problem solving algorithm and deserves more research to improve it and/or to adapt it to other engineering problems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1979) · Cited by 43,841
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
Swarm Intelligence: From Natural to Artificial Systems
Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999) · Oxford University Press eBooks · doi:10.1093/oso/9780195131581.001.0001 · Cited by 6,435
Molecular Computation of Solutions to Combinatorial Problems
Adleman, L. M. (1994) · Science · doi:10.1126/science.7973651 · Cited by 4,474
Introduction to Evolutionary Computing
Eiben, A. E., & Smith, J. E. (2003) · Natural Computing Series · doi:10.1007/978-3-662-05094-1 · Cited by 4,176
Computer Solutions of the Traveling Salesman Problem
Lin, S. (1965) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1965.tb04146.x · Cited by 1,986
Optimization by simulated annealing: Quantitative studies
Kirkpatrick, S. (1984) · Journal of Statistical Physics · doi:10.1007/bf01009452 · Cited by 1,805
(2004) · Lecture Notes in Computer Science · doi:10.1007/b99492 · Cited by 1,511
Cited by
Black hole: A new heuristic optimization approach for data clustering
Hatamlou, A. (2013) · Information Sciences · doi:10.1016/j.ins.2012.08.023 · Cited by 1,370
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method
Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115079 · Cited by 1,115
The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm
Hosseini, H. S. (2009) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2009.022775 · Cited by 473
Self-citationA Brief Review of Nature-Inspired Algorithms for Optimization
Fister, I., Yang, X., Fister, I., Brest, J., & Fister, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1307.4186 · Cited by 388
A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem
Sharma, M., & Kaur, P. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09412-6 · Cited by 303
Metadata
- Authors
- Hosseini, H. S.
- Venue
- 2007 IEEE Congress on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2007-09
- Pages
- 3226-3231
- OpenAlex
- W2131332502
- Cited by
- 313
- References
- 8
- FWCI
- 2.39
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Problem solving by intelligent water drops. https://app.cochat.ai/papers/10.1109/cec.2007.4424885
- The paper
- Hosseini, H. S. (2007). Problem solving by intelligent water drops. 2007 IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/cec.2007.4424885
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.