{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:57:45Z","timestamp":1757591865034},"reference-count":24,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2006,10,24]],"date-time":"2006-10-24T00:00:00Z","timestamp":1161648000000},"content-version":"vor","delay-in-days":5136,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Concurrency: Pract. Exper."],"published-print":{"date-parts":[[1992,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Three optimization methods derived from natural sciences are considered for allocating data to multicomputer nodes. These are simulated annealing, genetic algorithms and neural networks. A number of design choices and the addition of preprocessing and postprocessing steps lead to versions of the algorithms which differ in solution qualities and execution times. In this paper the performances of these versions are critically evaluated and compared for test cases with different features. The performance criteria are solution quality, execution time, robustness, bias and parallelizability. Experimental results show that the physical algorithms produce better solutions than those of recursive bisection methods and that they have diverse properties. Hence, different algorithms would be suitable for different applications. For example, the annealing and genetic algorithms produce better solutions and do not show a bias towards particular problem structures, but they are slower than the neural network algorithms. Preprocessing graph contraction is one of the additional steps suggested for the physical methods. It produces a significant reduction in execution time, which is necessary for their applicability to large problems.<\/jats:p>","DOI":"10.1002\/cpe.4330040705","type":"journal-article","created":{"date-parts":[[2006,11,17]],"date-time":"2006-11-17T16:25:24Z","timestamp":1163780724000},"page":"557-574","source":"Crossref","is-referenced-by-count":16,"title":["Allocating data to multicomputer nodes by physical optimization algorithms for loosely synchronous computations"],"prefix":"10.1002","volume":"4","author":[{"given":"Nashat","family":"Mansour","sequence":"first","affiliation":[]},{"given":"Geoffrey C.","family":"Fox","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2006,10,24]]},"reference":[{"key":"e_1_2_1_2_2","volume-title":"Solving Problems on Concurrent Processors","author":"Fox G. C.","year":"1988"},{"issue":"5","key":"e_1_2_1_3_2","first-page":"570","article-title":"A partitioning strategy for nonuniform problems on multiprocessors","volume":"36","author":"Berger M.","year":"1987","journal-title":"IEEE Trans."},{"key":"e_1_2_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/109025.109060"},{"key":"e_1_2_1_5_2","unstructured":"F.Ercal \u2018Heuristic approaches to task allocation for parallel computing\u2019 Doctoral Dissertation Ohio State University (1988)."},{"key":"e_1_2_1_6_2","volume-title":"Numerical Algorithms for Modern Parallel Computers","author":"Fox G. C.","year":"1988"},{"key":"e_1_2_1_7_2","doi-asserted-by":"publisher","DOI":"10.1137\/0611030"},{"key":"e_1_2_1_8_2","volume-title":"Proc. Conf. Parallel Methods on Large Scale Structural Analysis and Physics Applications","author":"Simon H.","year":"1991"},{"key":"e_1_2_1_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4330020402"},{"key":"e_1_2_1_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4330030502"},{"key":"e_1_2_1_11_2","unstructured":"G. C.Fox \u2018Physical computation\u2019 Int. Conf. Parallel Computing: Achievements Problems and Prospects Italy June (1990)."},{"key":"e_1_2_1_12_2","unstructured":"H.Muhlenbein \u2018Parallel genetic algorithms population genetics and combinatorial optimization\u2019 Proc. Int. Conf. on Genetic Algorithms (1989) pp.416\u2013421."},{"key":"e_1_2_1_13_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.37.6.865"},{"key":"e_1_2_1_14_2","unstructured":"G.Laszewski \u2018Intelligent structural operators for the k\u2010way graph partitioning problem\u2019 Proc. Int. Conf. on Genetic Algorithms July (1991) pp.45\u201352."},{"issue":"16","key":"e_1_2_1_15_2","article-title":"An analogue approach to the travelling salesman problem using an elastic net method","volume":"326","author":"Durbin R.","year":"1987","journal-title":"Nature"},{"key":"e_1_2_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1990.137673"},{"key":"e_1_2_1_17_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"e_1_2_1_18_2","unstructured":"J.Flower S.OttoandM.Salama \u2018A preprocessor for finite element problems\u2019 Proc. Symp. Parallel Computations and their Impact on Mechanics ASME Winter Meeting Dec. (1987)."},{"key":"e_1_2_1_19_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.3755256"},{"key":"e_1_2_1_20_2","doi-asserted-by":"crossref","unstructured":"G. C.FoxandW.Furmanski \u2018Load balancing loosely synchronous problems with a neural network\u2019 Proc 3rd Conf. Hypercube Concurrent Computers and Applications 241\u2013278(1988).","DOI":"10.1145\/62297.62327"},{"key":"e_1_2_1_21_2","volume-title":"Genetic Algorithms in Search, Optimization and Machine Learning","author":"Goldberg D. E.","year":"1989"},{"key":"e_1_2_1_22_2","volume-title":"Adaptation in Natural and Artificial Systems","author":"Holland J. H.","year":"1975"},{"key":"e_1_2_1_23_2","unstructured":"N.MansourandG. C.Fox \u2018A hybrid genetic algorithm for task allocation\u2019 Proc. Int. Conf. Genetic Algorithms July (1991) pp.466\u2013473."},{"key":"e_1_2_1_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/103724.103725"},{"key":"e_1_2_1_25_2","unstructured":"N.MansourandG. 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