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A Complementary Cyber Swarm Algorithm
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Author(s): Peng-Yeng Yin (National Chi Nan University, Taiwan), Fred Glover (OptTek Systems, Inc., USA), Manuel Laguna (University of Colorado, USA)and Jia-Xian Zhu (National Chi Nan University, Taiwan)
Copyright: 2015
Pages: 21
Source title:
Emerging Research on Swarm Intelligence and Algorithm Optimization
Source Author(s)/Editor(s): Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/978-1-4666-6328-2.ch003
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Abstract
A recent study (Yin, et al., 2010) showed that combining Particle Swarm Optimization (PSO) with the strategies of Scatter Search (SS) and Path Relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. In this chapter, the authors propose a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the Tabu Search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long-term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to the Standard PSO 2007 method (Clerc, 2008). These findings motivate future investigations of Cyber Swarm methods that combine features of both the original and complementary variants and incorporate additional strategic notions from the SS/PR template as a basis for creating a still more effective form of swarm optimization.
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