Yahoo Answers: Answers and Comments for 20點，中文文章翻譯成英文文章(請求協助...) [工程學]
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Sat, 18 Apr 2009 10:20:18 +0000
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Yahoo Answers: Answers and Comments for 20點，中文文章翻譯成英文文章(請求協助...) [工程學]
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https://answers.yahoo.com/question/index?qid=20090418000015KK02818
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From 妮妮: Particle Swarm Optimization algorithm (Particl...
https://answers.yahoo.com/question/index?qid=20090418000015KK02818
https://answers.yahoo.com/question/index?qid=20090418000015KK02818
Tue, 21 Apr 2009 13:30:00 +0000
Particle Swarm Optimization algorithm (Particle Swarm Optimization, PSO) in 1995, by the scholars of the Kennedy and Eberhart proposed a heuristic algorithm macro.
There are a number of relevant documents to prove the continuity of PSO in the optimization problem, quite a good search capability. In the discrete issues, such as scheduling, assigning, etc., there are many documents to PSO to solve this problem.
Among them, the travel salesman problem (Traveling Salesman Problem, TSP) is a typical optimization problem, many experts and scholars have been confirmed to be an NPComplete problem. This study a wide range of components such as printed circuit boards routing, logistics, route planning, traveling salesman can use the concept to solve the problem.
In this paper, particle swarm optimization algorithm, through the mechanism of the conversion space (Transfer Space, TS) to deal with the issue of realcoded. Particles in order to avoid premature convergence and the optimal solution in the region, so the use of simulated annealing (Simulated Annealing, SA) for the regional search, the ability to allow particles beyond the optimal solution of the restricted region, so this paper a new method known as the XXX algorithmXXXX.
And try to fuzzy clustering (Fuzzy Cmean Clustering, FCM) algorithm for solving large traveling salesman problem of clustering methods. After experimental testing and comparison of the relevant literature showed that the method proposed in this article, in the absence of clustering of cases, 50 days to solve the urban problems of solving a relatively better capacity, and through the cluster approach for solving large traveling salesman problem , help to reduce the error rate of solving, computation time, complexity.

From WAHAHA: The grain of subgroup optimization calculating...
https://answers.yahoo.com/question/index?qid=20090418000015KK02818
https://answers.yahoo.com/question/index?qid=20090418000015KK02818
Sat, 18 Apr 2009 11:26:09 +0000
The grain of subgroup optimization calculating method (Particle Swarm Optimization, PSO) in 1995, one great collection heuristic calculating method which proposed by scholar Kennedy and Eberhart. Had many related literature to prove that PSO in the continuous optimization question, has the quite outstanding search ability. But is leaving the divergence question, for example row of regulation, designation and so on, also many literature solve this aspect problem by PSO. And, the travel seller question (Traveling Salesman Problem, TSP) is a model optimization question, already by many experts confirmation was a NPComplete question. This aspect's research is quite widespread, for example the circuit wafer part's wiring, the physical distribution route plan and so on, all may solve using the travel seller question concept. This article by the grain of subgroup optimization calculating method, by the transformation space mechanism (Transfer Space, TS) deals with the real number code issue. In order to avoid granule premature restraining falling into the region best solution, therefore uses the simulation annealing (Simulated Annealing, SA) carries on the region search, lets the granule have ability bracelet region best solution limit, therefore this article proposed that one new method is called the XXXXXXX calculating method. And attempts by hives off fuzzily (Fuzzy Cmean Clustering, FCM) the calculating method hives off the method as the solution largescale travel seller question. Tests after the experiment with is related the literature to carry on the comparison, finally demonstrated this article proposed method, in has not hived off in the situation, solves 50 within the urban questions to have the relatively good solution ability, but by hives off the way solves the largescale travel seller question, is helpful in cuts the solution error coefficient, the operation time, the order of complexity.