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A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing

A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing

Yingxu Wang
Copyright: © 2012 |Pages: 19
ISBN13: 9781466602700|ISBN10: 1466602708|EISBN13: 9781466602717
DOI: 10.4018/978-1-4666-0270-0.ch015
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MLA

Wang, Yingxu. "A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing." Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends, edited by Peng-Yeng Yin, IGI Global, 2012, pp. 267-285. https://doi.org/10.4018/978-1-4666-0270-0.ch015

APA

Wang, Y. (2012). A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing. In P. Yin (Ed.), Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends (pp. 267-285). IGI Global. https://doi.org/10.4018/978-1-4666-0270-0.ch015

Chicago

Wang, Yingxu. "A Sociopsychological Perspective on Collective Intelligence in Metaheuristic Computing." In Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends, edited by Peng-Yeng Yin, 267-285. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0270-0.ch015

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Abstract

In studies of genetic algorithms, evolutionary computing, and ant colony mechanisms, it is recognized that the higher-order forms of collective intelligence play an important role in metaheuristic computing and computational intelligence. Collective intelligence is an integration of collective behaviors of individuals in social groups or collective functions of components in computational intelligent systems. This paper presents the properties of collective intelligence and their applications in metaheuristic computing. A social psychological perspective on collected intelligence is elaborated toward the studies on the structure, organization, operation, and development of collective intelligence. The collective behaviors underpinning collective intelligence in groups and societies are analyzed via the fundamental phenomenon of the basic human needs. A key question on how collective intelligence is constrained by social environment and group settings is explained by a formal motivation/attitude-driven behavioral model. Then, a metaheuristic computational model for a generic cognitive process of human problem solving is developed. This work helps to explain the cognitive and collective intelligent foundations of metaheuristic computing and its engineering applications.

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