NDLI: Locust Swarms - A new multi-optima search technique

Content Provider	IEEE Xplore Digital Library
Author	Chen, S.
Copyright Year	2009
Description	Author affiliation: School of Information Technology, York University, Toronto, ON M3J 1P3 Canada (Chen, S.)
Abstract	Locust Swarms are a new multi-optima search technique explicitly designed for non-globally convex search spaces. They use “smart” start points to scout for promising new areas of the search space before using particle swarms and a greedy local search technique (e.g. gradient descent) to find a local optimum. These scouts start a minimum distance away from the previous optimum, and this gap is an important part of achieving a non-convergent search trajectory. Equally, the search for “smart” start points centers around the previous local optimum, and this provides the basis for also having a non-random search trajectory. Experiments on a 30-dimensional rotated Schwefel function demonstrate that the ability of Locust Swarms to successfully balance these two search characteristics is an important factor in its ability to effectively explore this non-globally convex search space.
Starting Page	1745
Ending Page	1752
File Size	371925
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983152
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Particle swarm optimization Space exploration Convergence Displays Genetic algorithms Simulated annealing Optimization methods
Content Type	Text
Resource Type	Article

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