ArticleSwarm and evolutionary computation2014
Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization.
Article in Swarm and evolutionary computation, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Nature-inspired metaheuristics for optimizing dose-finding and computationally challenging clinical trial designs.Clinical trials (London, England) · 2025Article
- Enhanced Nutcracker Optimization Algorithm with Hyperbolic Sine-Cosine Improvement for UAV Path Planning.Biomimetics (Basel, Switzerland) · 2024Article
- An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications.Journal of medical Internet research · 2023Article
- Appropriate noise addition to metaheuristic algorithms can enhance their performance.Scientific reports · 2023Article
- Metaheuristics for pharmacometrics.CPT: pharmacometrics & systems pharmacology · 2021Review
- Article
- Finding High-Dimensional D-Optimal Designs for Logistic Models via Differential Evolution.IEEE access : practical innovations, open solutions · 2019Article
- Extended two-stage adaptive designs with three target responses for phase II clinical trials.Statistical methods in medical research · 2018Article
- Standardized maximimChemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2017Article
- Particle swarm optimization using multi-information characteristics of all personal-best information.SpringerPlus · 2016Article
- Particle Swarm Optimization with Double Learning Patterns.Computational intelligence and neuroscience · 2016Article
- A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.PloS one · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.