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首页> 外文会议>International Conference on Swarm, Evolutionary and Memetic Computing >A Selective Teaching-Learning Based Niching Technique with Local Diversification Strategy
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A Selective Teaching-Learning Based Niching Technique with Local Diversification Strategy

机译：一种基于局部多元化策略的选择性教学职能技术

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摘要

Real world problems present instances where more than one optimal solution can be obtained for a system under consideration so as to switch between them without considerably affecting efficiency. In such instances the idea of niching provides a solution. In this paper we propose a swarm-based niching technique that enhances diversity by Teaching and Learning strategy that adapts to the local neighbourhood by controlled exploitation and the knowledge learned helps to preserve population diversity. Our algorithm, imitates the local-explorative swarm behaviour to hover around local sites in groups, exploiting the peaks with high degree of accuracy, is called TLB-lDS (Teaching-Learning Based Optimization with Local Diversification Strategy), without using any niching parameter. TLB-lDS algorithm is compared against sophisticated niching algorithms tested on a set of standard numerical benchmarks.
机译：现实世界问题存在在考虑的系统中可以获得多于一种最佳解决方案的情况，以便在它们之间切换而不会显着影响效率。在这种情况下，利基的想法提供了解决方案。在本文中，我们提出了一种基于群的职能技术，通过教学和学习策略来增强多样性，以控制剥削对当地社区适应，知识有助于维护人口多样性。我们的算法模仿了本地探索的群体行为，将悬停在本地站点周围，以小组利用高精度的峰值，被称为TLB-LDS（基于教学 - 学习的优化与局部多样化策略），而无需使用任何效力参数。将TLB-LDS算法与在一组标准数值基准上测试的复杂的抗真算法进行比较。

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