NDLI: A topological niching covariance matrix adaptation for multimodal optimization

Content Provider	IEEE Xplore Digital Library
Author	Weck Pereira, M. Schwedersky Neto, G. Roisenberg, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. & Stat., Fed. Univ. of Santa Catarina, Florianopolis, Brazil (Weck Pereira, M.; Roisenberg, M.) \|\| CENPES/PDGP/CMR - PETROBRAS S/A, Rio de Janeiro, Brazil (Schwedersky Neto, G.)
Abstract	Multimodal optimization attempts to find multiple global and local optima of a function. Finding a set of optimal solutions is particularly important for practical problems. However, this kind of problem requires optimization techniques that demand a high computational cost and a large amount of parameters to be adjusted. These difficulties increase in high-dimensional space problems. In this work, we propose a niching method based on recent developments in the basins (optimal locations) identification to reduce costs and perform better in high-dimensional spaces. Using Nearest-Better Clustering (NBC) and Hill-Valley (or Detect Multimodal) methods, an exploratory initialization routine is employed to identify basins on functions with different levels of complexity. To maintain diversity over the generations, we define a bi-objective function, which is composed by the original fitness function and the distance to the nearest better neighbor, assisted by a reinitialization scheme. The proposed method is implemented using Evolutionary Strategy (ES) known as Covariance Matrix Adaptation (CMA). Unlike recent multimodal approaches using CMA-ES, we use its step size to control the influence of niche, thus avoiding extra efforts in parameterization. We apply a benchmark of 20 test functions, specially designed for multimodal optimization evaluation, and compare the performance with a state-of-the-art method. Finally we discuss the results and show that the proposed approach can reach better and stable results even in high-dimensional spaces.
Starting Page	2562
Ending Page	2569
File Size	1001247
Page Count	8
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900554
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sociology Optimization Covariance matrices Equations Mathematical model Benchmark testing
Content Type	Text
Resource Type	Article

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