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\u8def\u7dda\u898f\u5283\uff1a<\/strong>\u8def\u5f91\u898f\u5283\u662f\u6700\u4f73\u5316\u9818\u57df\u4e2d\u975e\u5e38\u7d93\u5178\u7684\u554f\u984c\uff0c\u76ee\u6a19\u70ba\u5728\u6bcf\u689d\u8def\u7dda\u90fd\u4e0d\u5f97\u91cd\u8907\u7d93\u904e\u540c\u4e00\u5730\u9ede\u7684\u60c5\u6cc1\u4e0b\u4ee5\u627e\u51fa\u6700\u77ed\u8def\u5f91\uff0c\u5176\u61c9\u7528\u5305\u62ec\u500b\u4eba\u6216\u65c5\u884c\u793e\u7684\u884c\u7a0b\u898f\u5283\uff0c\u6216\u662f\u7269\u6d41\u696d\u88e1\u8ca8\u904b\u6210\u672c\u548c\u8def\u7dda\u512a\u5316\u7b49\u3002\u4e0d\u5c11\u5143\u555f\u767c\u5f0f\u6f14\u7b97\u6cd5\u4e5f\u5c0d\u6b64\u554f\u984c\u9032\u884c\u7814\u7a76\uff0c\u5176\u4e2d\u87fb\u7fa4\u6f14\u7b97\u6cd5\u662f\u6700\u5e38\u88ab\u4f7f\u7528\u7684\u7d93\u5178\u65b9\u6cd5\u3002\u5728\u4f7f\u7528 SIB \u6f14\u7b97\u6cd5\u6642\uff0c\u6bcf\u500b\u7c92\u5b50\u662f\u4e00\u500b\u5411\u91cf\uff0c\u9577\u5ea6\u70ba\u554f\u984c\u4e2d\u6307\u5b9a\u76ee\u7684\u5730\u7684\u6578\u91cf\uff0c\u5176\u4e2d\u6bcf\u500b\u5143\u7d20\u70ba\u4f7f\u7528\u8005\u4e8b\u5148\u5b9a\u7fa9\u597d\u7684\u5730\u9ede\u7de8\u865f\u3002\u300c\u6df7\u5408\u300d\u4f9d\u7136\u4ee5\u8ff4\u5708\u7684\u65b9\u5f0f\u9032\u884c\uff0c\u800c\u4f7f\u7528\u8005\u5fc5\u9808\u4e8b\u5148\u6c7a\u5b9a\u6b64\u8ff4\u5708\u9032\u884c\u7684\u6b21\u6578\u3002\u5728\u6bcf\u500b\u8ff4\u5708\u4e2d\uff0c\u5148\u6839\u64da\u539f\u7c92\u5b50\u7b97\u51fa\u6bcf\u4e00\u500b\u5730\u9ede\u548c\u5176\u5728\u8def\u7dda\u4e0a\u524d\u5f8c\u5169\u9ede\u7684\u8ddd\u96e2\u7e3d\u548c\uff0c\u4e26\u5f9e\u4e2d\u627e\u51fa\u7e3d\u548c\u6700\u5927\u7684\u5730\u9ede a \u3002\u63a5\u8457\uff0c\u627e\u51fa\u6700\u4f73\u89e3\u7c92\u5b50\u7684\u8def\u7dda\u4e2d\u76f8\u540c\u9806\u5e8f\u7684\u5730\u9ede\uff0c\u4e26\u5728\u539f\u7c92\u5b50\u4e2d\u627e\u5230\u76f8\u540c\u7de8\u865f\u7684\u5730\u9ede b \u3002\u6700\u5f8c\uff0c\u4ea4\u63db\u539f\u7c92\u5b50\u4e2d\u7684 a \u548c b \u5169\u500b\u9ede\u3002\u6b64\u904b\u7b97\u6a21\u5f0f\u53ef\u4ee5\u4fdd\u8b49\u6700\u5f8c\u5f97\u5230\u7684\u8def\u7dda\u4f9d\u7136\u4e0d\u6703\u91cd\u8907\u7d93\u904e\u76f8\u540c\u5730\u9ede\u3002\u5be6\u9a57\u7d50\u679c\u5c55\u793a SIB \u6f14\u7b97\u6cd5\u5728\u5730\u9ede\u6578\u70ba 10 \u5230 25 \u4e4b\u9593\u7684\u8def\u5f91\u898f\u5283\u554f\u984c\u4e0a\uff0c\u6700\u4f73\u5316\u7d50\u679c\u548c\u87fb\u7fa4\u6f14\u7b97\u6cd5\u76f8\u8fd1\uff0c\u4e14\u904b\u7b97\u901f\u5ea6\u66f4\u5feb\u3002\u6b64\u5916\uff0c\u91dd\u5c0d\u5be6\u52d9\u4e0a\u53ef\u80fd\u51fa\u73fe\u7684\u7d30\u7dfb\u9650\u5236\uff0c\u5982\uff1a\u8ca8\u54c1\u6750\u7a4d\u3001\u53f8\u6a5f\u6392\u73ed\u7b49\uff0c SIB \u6f14\u7b97\u6cd5\u7d93\u6539\u826f\u5f8c\u90fd\u5df2\u7d93\u7d0d\u5165\u8003\u91cf\u3002<\/p>\n

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\u53c3\u8003\u6587\u737b<\/strong><\/p>\n

1.Rao, S.S. (2009) Engineering Optimization: Theory and Practice (4th Edition), John Wiley & Sons, Inc.
\n2.Phoa, F.K.H., Chen, R.B., Wang, W.C. and Wong, W.K. (2016). Optimizing two-level supersaturated designs via swarm intelligence techniques. Technometrics 58, 43-49.
\n3.Lin, F.P.C. and Phoa, F.K.H. (2017a). A performance study of parallel programming via CPU and GPU on swarm intelligence based evolutionary algorithm. Proceedings of ISMSI 2017 1-5.
\n4.Lin, F.P.C. and Phoa, F.K.H. (2017b). An efficient construction of confidence regions via swarm intelligence and its application in target localization. IEEE Access 6, 8610-8618.
\n5.Phoa, F.K.H. (2017). A swarm intelligence based (SIB) method for optimization in designs of experiments. Natural Computing 16, 597-605.
\n6.Hsu, T.C. and Phoa, F.K.H. (2018). A smart initialization on the swarm intelligence based method for efficient search of optimal minimum energy design. Advances in Swarm Intelligence, LNCS volume 10941, 78-87.
\n7.Lin, F.P.C. and Phoa, F.K.H. (2019). Runtime estimation and scheduling on parallel processing supercomputers via instance-based learning and swarm intelligence. International Journal of Machine Learning and Computations 9, 592-598.
\n8.Phoa, F.K.H. and Tsai, T.C. (2020). A two-step approach to the search of minimum energy designs via swarm intelligence. Advances in Swarm Intelligence, LNCS volume 12145, 37-45.
\n9.Phoa, F.K.H., Liu, H.P., Chen-Burger, Y.H. and Lin, S.P. (2021). Metaheuristic optimization on tensortype solution via swarm intelligence and its application in the profit optimization in designing selling scheme. Advances in Swarm Intelligence, LNCS volume 12689, 72-82.
\n10.Singh, K., Lin, S.P., Phoa, F.K.H. and Chen-Burger, Y.H.J. (2021). Swarm intelligence optimization algorithms and their applications in a complex layer-egg supply chain. Agents and Multi-agent Systems: Technologies and Applications, SIST volume 241, 39-51.
\n11.Yen, P.C. and Phoa, F.K.H. (2021). Traveling salesman problem via swarm intelligence. Advances in Swarm Intelligence, LNCS volume 12689, 106-115.
\n12.Singh, K., Liu, H.P., Phoa, F.K.H., Lin, S.P. and Chen-Burger, Y.H.J. (2022). Decentralized supply chain optimization via swarm intelligence. Advances in Swarm Intelligence, LNCS volume 13344, 432-441.
\n13.Sun, W.H. and Phoa, F.K.H. (2022). Network community detection via an improved swarm intelligence approach. Advances in Swarm Intelligence, LNCS volume 13344, 419-431.<\/p>\n","protected":false},"excerpt":{"rendered":"

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