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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A fuzzy particle swarm optimization algorithm for computer communication network topology design
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A fuzzy particle swarm optimization algorithm for computer communication network topology design

机译:计算机通信网络拓扑设计的模糊粒子群优化算法

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摘要

Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is topology design of distributed local area networks (DLANs). The problem is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle swarm optimization algorithm to efficiently solve the DLAN topology design problem. Fuzzy logic is incorporated in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692-2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN topology design problem.
机译:粒子群优化(PSO)是一种强大的优化技术,已应用于解决许多复杂的优化问题。这样的优化问题之一是分布式局域网(DLAN)的拓扑设计。该问题被定义为一个多目标优化问题,需要同时优化货币成本,平均网络延迟,通信节点之间的跳数和一组约束条件下的可靠性。本文提出了一种多目标粒子群优化算法,可以有效解决DLAN拓扑设计问题。 PSO算法中结合了模糊逻辑,以处理问题的多目标性质。具体地,最近提出的模糊聚合算子,即统一的“与-或”算子(Kha​​n and Engelbrecht in Inf。Sci。177:2692-2711,2007),用于聚合目标。通过对PSO参数的初步敏感性分析,对所提出的模糊PSO(FPSO)算法进行了经验评估。还将FPSO与模糊模拟退火和模糊蚁群优化算法进行了比较。结果表明,模糊PSO是解决DLAN拓扑设计问题的合适算法。

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