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Using NW small-world model to improve the performance of social emotional optimization algorithm

机译:使用NW小世界模型提高社交情绪优化算法的性能

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Topology structure is an important aspect to influence the performance of social emotional optimization algorithm (SEOA).A good topology structure may provide more chance to escape from a local optimum, therefore, in this paper, a new topology structure-Newman and Watts small world model is introduced and called NWSEOA. In NWSEOA, the topology is changed according to Newman-Watts rules within the whole evolutionary process. To test the performance of NWSEOA, three other algorithms are employed including standard version and one variant of particle swarm optimization, and the standard version of SEOA, and 5 famous benchmarks are used to compare, simulation results show the proposed algorithm is superior to other three algorithms.
机译:拓扑结构是影响社交情绪优化算法(SEOA)性能的重要方面。良好的拓扑结构可能会提供更多逃避局部最优的机会,因此,本文提出了一种新的拓扑结构-Newman和Watts小世界该模型被引入并称为NWSEOA。在NWSEOA中,拓扑在整个进化过程中根据Newman-Watts规则进行更改。为了测试NWSEOA的性能,采用了其他三种算法,包括标准版本和粒子群优化的一种变体,以及SEOA的标准版本,并使用5个著名的基准进行比较,仿真结果表明,该算法优于其他三种算法。算法。

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