Search points distribution analysis for differential evolution based on maximum entropy method

Yuji Koguma; Eitaro Aiyoshi

doi:10.1541/ieejeiss.134.1341

Search points distribution analysis for differential evolution based on maximum entropy method

Yuji Koguma, Eitaro Aiyoshi

Department of Applied Physics and Physico-Informatics

Research output: Contribution to journal › Article › peer-review

Abstract

Recently, a paradigm of optimization method called meta-heuristic (MH) has attracted interest for its applicability. However, the most part of MHs have insufficient mathematical background in their dynamics. Some of MHs adopt pseudo-random numbers generated by a computer in their algorithm so that they are called "stochastic optimization methods". As for stochastic optimization methods, the positions of search points are able to be regarded as stochastic variables which are distributed according to a certain probability distribution. In this paper, we perform an analysis of search points distribution for DE dynamics based on maximum entropy method.

Original language	English
Pages (from-to)	1341-1347
Number of pages	7
Journal	IEEJ Transactions on Electronics, Information and Systems
Volume	134
Issue number	9
DOIs	https://doi.org/10.1541/ieejeiss.134.1341
Publication status	Published - 2014

Keywords

Differential evolution
Maximum entropy method
Meta-heuristics
Stochastic optimization method

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1541/ieejeiss.134.1341

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abstract = "Recently, a paradigm of optimization method called meta-heuristic (MH) has attracted interest for its applicability. However, the most part of MHs have insufficient mathematical background in their dynamics. Some of MHs adopt pseudo-random numbers generated by a computer in their algorithm so that they are called {"}stochastic optimization methods{"}. As for stochastic optimization methods, the positions of search points are able to be regarded as stochastic variables which are distributed according to a certain probability distribution. In this paper, we perform an analysis of search points distribution for DE dynamics based on maximum entropy method.",

keywords = "Differential evolution, Maximum entropy method, Meta-heuristics, Stochastic optimization method",

author = "Yuji Koguma and Eitaro Aiyoshi",

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AU - Aiyoshi, Eitaro

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AB - Recently, a paradigm of optimization method called meta-heuristic (MH) has attracted interest for its applicability. However, the most part of MHs have insufficient mathematical background in their dynamics. Some of MHs adopt pseudo-random numbers generated by a computer in their algorithm so that they are called "stochastic optimization methods". As for stochastic optimization methods, the positions of search points are able to be regarded as stochastic variables which are distributed according to a certain probability distribution. In this paper, we perform an analysis of search points distribution for DE dynamics based on maximum entropy method.

KW - Differential evolution

KW - Maximum entropy method

KW - Meta-heuristics

KW - Stochastic optimization method

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Search points distribution analysis for differential evolution based on maximum entropy method

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Keywords

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