{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:45:39Z","timestamp":1772783139301,"version":"3.50.1"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In the field of multi-objective optimization, there are a multitude of algorithms from which to choose. Each algorithm has strengths and weaknesses associated with the mechanics for finding the Pareto front. Recently, researchers have begun to examine how multi-agent environments can be used to help solve multi-objective optimization problems. In this work, we propose a multi-objective optimization algorithm based on a multi-agent blackboard system (MABS). The MABS framework allows for multiple agents to read and write pertinent optimization problem data to a central blackboard agent. Agents can stochastically search the design space, use previously discovered solutions to explore local optima, or update and prune the Pareto front. A centralized blackboard framework allows the optimization problem to be solved in a cohesive manner and permits stopping, restarting, or updating the optimization problem. The MABS framework is tested against three alternative optimization algorithms across a suite of engineering design problems and typically outperforms the other algorithms in discovering the Pareto front. A parallelizability study is performed where we find that the MABS is able to evaluate a set number of designs, which require an evaluation time ranging from 0 to 300 seconds, quicker than a traditional optimization algorithm: this fact becomes more apparent the longer it takes to evaluate a design. To provide context for the benefits provided by MABS, a real-world nuclear engineering design problem is examined. MABS is used to examine the placement of experiments in a nuclear reactor, where we are able to evaluate hundreds of configurations for experimental placement while maintaining a strict set of safety constraints.<\/jats:p>","DOI":"10.1093\/jcde\/qwac009","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T04:12:49Z","timestamp":1643083969000},"page":"480-506","source":"Crossref","is-referenced-by-count":12,"title":["An agent-based blackboard system for multi-objective optimization"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4867-6555","authenticated-orcid":false,"given":"Ryan","family":"Stewart","sequence":"first","affiliation":[{"name":"Idaho National Laboratory, 2525\u00a0N Freemont Avenue, Idaho Falls, ID 83415, USA"},{"name":"Oregon State University, School of Nuclear Science and Engineering, 1500 SW Jefferson Street, Corvallis, OR 97331, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Todd S","family":"Palmer","sequence":"additional","affiliation":[{"name":"Oregon State University, School of Nuclear Science and Engineering, 1500 SW Jefferson Street, Corvallis, OR 97331, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Bays","sequence":"additional","affiliation":[{"name":"Idaho National Laboratory, 2525\u00a0N Freemont Avenue, Idaho Falls, ID 83415, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,3,21]]},"reference":[{"key":"2022032112225093200_bib1","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/ICTAI.2007.108","article-title":"Ant colony optimization for multi-objective optimization problems","volume-title":"19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007)","author":"Alaya","year":"2007"},{"key":"2022032112225093200_bib2","doi-asserted-by":"crossref","first-page":"4661","DOI":"10.1109\/CEC.2007.4425083","article-title":"Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition","volume-title":"2007 IEEE Congress on Evolutionary Computation","author":"Atashpaz-Gargari","year":"2007"},{"key":"2022032112225093200_bib3","doi-asserted-by":"crossref","first-page":"89497","DOI":"10.1109\/ACCESS.2020.2990567","article-title":"Pymoo: Multi-objective optimization in Python","volume":"8","author":"Blank","year":"2020","journal-title":"IEEE Access"},{"key":"2022032112225093200_bib4","first-page":"2369","article-title":"Multi-agent blackboard architecture for a mobile robot","volume-title":"Proceedings of the 2001 IEEE\/RSJ International Conference on Intelligent Robots and Systems. 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