{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:03:25Z","timestamp":1763017405074,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2014,5,5]],"date-time":"2014-05-05T00:00:00Z","timestamp":1399248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented here. Parameter estimation is the task of suitably fitting a model to given data; some form of parameter estimation is a key element of many computer vision processes. Here, the task of hyperplane estimation in many dimensions is investigated. Following RANSAC (random sample consensus), a widely used optimisation technique and a standard technique for many parameter estimation problems, increasingly sophisticated data-driven forms of SDS are developed. The performance of these SDS algorithms and RANSAC is analysed and compared for a hyperplane estimation task. SDS is shown to perform similarly to RANSAC, with potential for tuning to particular search problems for improved results.<\/jats:p>","DOI":"10.3390\/a7020206","type":"journal-article","created":{"date-parts":[[2014,5,5]],"date-time":"2014-05-05T11:53:53Z","timestamp":1399290833000},"page":"206-228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Stochastic Diffusion Search: A Comparison of Swarm Intelligence Parameter Estimation Algorithms with RANSAC"],"prefix":"10.3390","volume":"7","author":[{"given":"Howard","family":"Williams","sequence":"first","affiliation":[{"name":"Queen Mary University London, Mile End Road, London E1 4NS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Bishop","sequence":"additional","affiliation":[{"name":"Goldsmiths College, University of London, New Cross, London SE14 6NW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,5,5]]},"reference":[{"key":"ref_1","unstructured":"Kennedy, J., Eberhart, R., and Shi, Y. (2001). Swarm Intelligence, Morgan Kauffman."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press.","DOI":"10.1093\/oso\/9780195131581.001.0001"},{"key":"ref_3","unstructured":"Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley."},{"key":"ref_4","unstructured":"Holland, J. (1975). Adaptation in Natural and Artificial Systems, The University of Michigan Press."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Back, T. (1996). Evolutionary Algorithms in Theory and Practice, Oxford University Press.","DOI":"10.1093\/oso\/9780195099713.003.0007"},{"key":"ref_6","unstructured":"Bishop, J. (1989, January 16\u201318). Stochastic Searching Networks. Proceedings of the 1st IEE International Conference Artificial Neural Networks, London, UK."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1126\/science.186.4168.1046","article-title":"Tandem calling: A new kind of signal in ant communication","volume":"186","author":"Moglich","year":"1974","journal-title":"Science"},{"key":"ref_8","unstructured":"De Meyer, K., Nasuto, S., and Bishop, J. (2006). Stigmergic Optimization, Studies in Computational Intelligence, Springer. Chapter Stochastic Diffusion Optimisation: The Application of Partial Function Evaluation and Stochastic Recruitment."},{"key":"ref_9","unstructured":"Whitaker, R., and Hurley, S. (2002, January 10\u201314). An agent based approach to site selection for wireless networks. Proceedings of the 2002 ACM Symposium on Applied Computing, Madrid, Spain."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1023\/A:1008033229660","article-title":"Self-Localisation in the \u2018Senario\u2019 Autonomous Wheelchair","volume":"22","author":"Beattie","year":"1998","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bishop, J., and Torr, P. (1992). Neural Networks for Images, Speech and Natural Language, Chapman Hall. Chapter The Stochastic Search Network.","DOI":"10.1007\/978-94-011-2360-0_24"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1049\/ij-cdt.1979.0009","article-title":"Guide to pattern recognition using random access memories","volume":"2","author":"Aleksander","year":"1979","journal-title":"Comput. Digit. Tech."},{"key":"ref_13","unstructured":"Grech-Cini, E. (1995). Locating Facial Features. [Master\u2019s Thesis, University of Reading]."},{"key":"ref_14","first-page":"89","article-title":"Convergence Analysis of Stochastic Diffusion Search","volume":"14","author":"Nasuto","year":"1999","journal-title":"J. Parallel Alg. Appl."},{"key":"ref_15","unstructured":"Nasuto, S., Bishop, J., and Lauria, S. (1998, January 23\u201315). Time Complexity of Stochastic Diffusion Search. Proceedings of the International ICSC\/IFAC Symposium on Neural Computation, Vienna, Austria."},{"key":"ref_16","unstructured":"Nasuto, S. (1999). Analysis of Resource Allocation of Stochastic Diffusion Search. [Ph.D. Thesis, University of Reading]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Neumaier, A. (2004). Complete Search in Continuous Global Optimization and Constraint Satisfaction, Cambridge University Press.","DOI":"10.1017\/CBO9780511569975.004"},{"key":"ref_18","first-page":"155","article-title":"Stochastic Diffusion Search Review","volume":"4","author":"Bishop","year":"2013","journal-title":"Paladyn J. Behav. Robot."},{"key":"ref_19","first-page":"406","article-title":"Inductive reasoning and bounded rationality, (The El Farol Problem)","volume":"84","author":"Arthur","year":"1994","journal-title":"Am. Econ. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bishop, J., and Nasuto, S. (2002). Artificial Neural Networks Lecture Notes in Computer Science, Springer. Chapter Dynamic Knowledge Representation in Connectionist Systems.","DOI":"10.1007\/3-540-46084-5_51"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough Transformation to Detect Lines and Curves in Pictures","volume":"15","author":"Duda","year":"1972","journal-title":"Commun. ACM Arch."},{"key":"ref_22","first-page":"381","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. Assoc. Comp. Mach."},{"key":"ref_23","unstructured":"Press, W., Flannery, B., Teukolsky, S., and Vetterling, W. (1992). Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press. [2nd ed.]."},{"key":"ref_24","unstructured":"Vince, J. (2005). Geometry for Computer Graphics: Formulae, Examples and Proofs, Springer. [1st ed.]."},{"key":"ref_25","unstructured":"Al Rifaie, M., Bishop, J., and Blackwell, T. (2011, January 24\u201326). An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search. Proceedings of the International Conference on Evolutionary Computation Theory and Application, (ECTA 3), Faris, France."},{"key":"ref_26","unstructured":"Omran, M., Moukadem, I., al Sharhan, S., and Kinawi, M. (2011, January 12\u201315). Stochastic diffusion search for continuous global optimization. Proceedings of the International Conference on Swarm Intelligence, (ICSI 2011), Chongqing, China."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1109\/TPAMI.2005.199","article-title":"Guided-mlesac: Faster image MLESAC: Transform estimation by using matching priors","volume":"27","author":"Tordoff","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Myatt, D., Torr, P., Nasuto, S., Bishop, J., and Craddock, R. (2002, January 2\u20135). NAPSAC: High noise, high dimensional robust estimation\u2014It\u2019s in the bag. Proceedings of the 13th British Machine Vision Conference, (BMVC), Cardiff, UK.","DOI":"10.5244\/C.16.44"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wolpert, D., and Macready, W. (1997). No Free Lunch Theorems for Search, Santa Fe Institute. Technical Report.","DOI":"10.1109\/4235.585893"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No Free Lunch Theorems for Optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/7\/2\/206\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:11:04Z","timestamp":1760217064000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/7\/2\/206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,5,5]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2014,6]]}},"alternative-id":["a7020206"],"URL":"https:\/\/doi.org\/10.3390\/a7020206","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2014,5,5]]}}}