PSO based stability analysis of a computational intelligent algorithm using SOS

2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON) Pub Date : 2017-08-01 DOI:10.1109/IEMECON.2017.8079612

Dipika Ghosh, Ashish Kumar Mahato, Amazing Grace Asipita Onuya, Ashutosh Kumar Singh, Manish Kumar, Pritam Banik, Shubhamay Das, Mainak Biswas, Debasis Maji, S. Dutta, D. Jana, G. Sarkar

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引用次数: 3

Abstract

This work introduces Symbiotic Organism Search (SOS) for solving stability related problems. SOS is a new and robust approach in met heuristic fields and never been used to solve discrete problems. A sophisticated method to deal with stability related problem that is applied using the basic Symbiotic Organism Search (SOS) framework. The performance of the algorithm was evaluated on a set of benchmark instances and compared results with best known solution. The results show that the proposed algorithm can produce good solution. These results indicated that the proposed SOS can be applied as an alternative to solve the stability related issue. SOS algorithm is an effective met heuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency.

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基于PSO的一种基于SOS的计算智能算法稳定性分析

本文引入了共生生物搜索(SOS)来解决与稳定性相关的问题。SOS是一种新的、鲁棒的启发式方法，从未被用于解决离散问题。利用基本的共生生物搜索(SOS)框架，提出了一种处理稳定性相关问题的复杂方法。在一组基准实例上评估了算法的性能，并将结果与最优解进行了比较。结果表明，该算法能得到较好的解。这些结果表明，所提出的SOS可以作为解决稳定性相关问题的替代方案。SOS算法是2014年开发的一种有效的启发式算法，它模拟生物之间的共生关系，如互惠、共生、寄生等，在生态系统中生存。在本研究中，通过在基本的SOS算法中引入自适应效益因子，提出了三种改进版本的SOS算法，以提高其效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON)

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