Macroscopic Indeterminacy Swarm Optimization (MISO) algorithm for real-parameter search

Chang, PC; He, X

Macroscopic Indeterminacy Swarm Optimization (MISO) algorithm for real-parameter search

Chang, PC He, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, 2014, pp. 1571 - 1578
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Chang, PC	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, 2014, pp. 1571 - 1578	en_US
dc.identifier.isbn	9781479914883	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34118
dc.description.abstract	© 2014 IEEE. Swarm Intelligence (SI) is a nature-inspired emergent artificial intelligence. They are often inspired by the phenomena in nature. Many proposed algorithms are focused on designing new update mechanisms with formulae and equations to emerge new solutions. Despite the techniques used in an algorithm being the key factor of the whole system, the evaluation of candidate solutions also plays an important role. In this paper, the proposed algorithm Macroscopic Indeterminacy Swarm Optimization (MISO) presents a new search scheme with indeterminate moment of evaluation. Here, we perform an experiment based on public benchmark functions. The results produced by MISO, Differential Evolution (DE) with various settings, Artificial Bee Colony (ABC), Simplified Swarm Optimization (SSO), and Particle Swarm Optimization (PSO) have been compared. The result shows MISO can achieve similar or even better performance than other algorithms.	en_US
dc.relation.ispartof	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014	en_US
dc.relation.isbasedon	10.1109/CEC.2014.6900281	en_US
dc.title	Macroscopic Indeterminacy Swarm Optimization (MISO) algorithm for real-parameter search	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	080106 Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Swarm Intelligence (SI) is a nature-inspired emergent artificial intelligence. They are often inspired by the phenomena in nature. Many proposed algorithms are focused on designing new update mechanisms with formulae and equations to emerge new solutions. Despite the techniques used in an algorithm being the key factor of the whole system, the evaluation of candidate solutions also plays an important role. In this paper, the proposed algorithm Macroscopic Indeterminacy Swarm Optimization (MISO) presents a new search scheme with indeterminate moment of evaluation. Here, we perform an experiment based on public benchmark functions. The results produced by MISO, Differential Evolution (DE) with various settings, Artificial Bee Colony (ABC), Simplified Swarm Optimization (SSO), and Particle Swarm Optimization (PSO) have been compared. The result shows MISO can achieve similar or even better performance than other algorithms.

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http://hdl.handle.net/10453/34118