{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T15:50:50Z","timestamp":1762271450122},"reference-count":43,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2010,6]]},"abstract":"<jats:p> Many real world industrial applications involve the Traveling Salesman Problem (TSP), which is a problem that finds a Hamiltonian path with minimum cost. Examples of problems that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a Bee Colony Optimization (BCO) algorithm for symmetrical TSP. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The algorithm is integrated with a fixed-radius near neighbour 2-opt (FRNN 2-opt) heuristic to further improve prior solutions generated by the BCO model. To limit the overhead incurred by the FRNN 2-opt, a frequency-based pruning strategy is proposed. The pruning strategy allows only a subset of the promising solutions to undergo local optimization. Experimental results comparing the proposed BCO algorithm with existing approaches on a set of benchmark problems are presented. For 84 benchmark problems, the BCO algorithm is able to obtain an overall average solution quality of 0.31% from known optimum. The results also show that it is comparable to other algorithms such as Ant Colony Optimization and Particle Swarm Optimization. <\/jats:p>","DOI":"10.1142\/s0218213010000200","type":"journal-article","created":{"date-parts":[[2010,7,1]],"date-time":"2010-07-01T05:49:12Z","timestamp":1277963352000},"page":"305-334","source":"Crossref","is-referenced-by-count":36,"title":["BEE COLONY OPTIMIZATION WITH LOCAL SEARCH FOR TRAVELING SALESMAN PROBLEM"],"prefix":"10.1142","volume":"19","author":[{"given":"LI-PEI","family":"WONG","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore"},{"name":"School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"MALCOLM YOKE HEAN","family":"LOW","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"CHIN SOON","family":"CHONG","sequence":"additional","affiliation":[{"name":"Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1126\/science.185.4152.663"},{"key":"rf2","doi-asserted-by":"crossref","unstructured":"M.\u00a0Dorigo and T.\u00a0St\u00fctzle, Handbook of Metaheuristics, International Series In Operations Research & Management Science (Springer, New York, 2002)\u00a0pp. 250\u2013285.","DOI":"10.1007\/0-306-48056-5_9"},{"key":"rf3","unstructured":"W.\u00a0Kocay and D. 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