Multi-population adapative inflationary differential evolution

Marilena Di Carlo; Massimiliano Vasile; Edmondo Minisci

Multi-population adapative inflationary differential evolution

Marilena Di Carlo, Massimiliano Vasile, Edmondo Minisci

Mechanical And Aerospace Engineering

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Abstract

In this paper, a multi-population version of Adaptive Inflationary Differential Evolution, which automatically adapts the crossover probability and the differential weight of the Differential Evolution, is presented. The multi-population algorithm exploits the use of different populations, and the local minima found by each population, to assess the distance between minima; a probabilistic kernel based approach is then used to automatically adapt the dimension of a bubble in which the population is re-initialized after converging to a local minimum. The algorithm is tested on two real case functions and on two difficult academic functions.

Original language	English
Pages	41-54
Publication status	Published - 13 Sept 2014
Event	Bio-inspired Optimization Methods and their Applications, BIOMA 14 - Ljubljana, Slovenia Duration: 13 Sept 2014 → 13 Sept 2014

Conference

Conference	Bio-inspired Optimization Methods and their Applications, BIOMA 14
Country/Territory	Slovenia
City	Ljubljana
Period	13/09/14 → 13/09/14

Keywords

adaptive algorithms
differential evolution
global optimization

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Di Carlo M et al - Pure - Multi-population adaptive inflationary differential evolution Sep 2014Accepted author manuscript, 187 KB

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title = "Multi-population adapative inflationary differential evolution",

abstract = "In this paper, a multi-population version of Adaptive Inflationary Differential Evolution, which automatically adapts the crossover probability and the differential weight of the Differential Evolution, is presented. The multi-population algorithm exploits the use of different populations, and the local minima found by each population, to assess the distance between minima; a probabilistic kernel based approach is then used to automatically adapt the dimension of a bubble in which the population is re-initialized after converging to a local minimum. The algorithm is tested on two real case functions and on two difficult academic functions.",

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AU - Di Carlo, Marilena

AU - Vasile, Massimiliano

AU - Minisci, Edmondo

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Y1 - 2014/9/13

N2 - In this paper, a multi-population version of Adaptive Inflationary Differential Evolution, which automatically adapts the crossover probability and the differential weight of the Differential Evolution, is presented. The multi-population algorithm exploits the use of different populations, and the local minima found by each population, to assess the distance between minima; a probabilistic kernel based approach is then used to automatically adapt the dimension of a bubble in which the population is re-initialized after converging to a local minimum. The algorithm is tested on two real case functions and on two difficult academic functions.

AB - In this paper, a multi-population version of Adaptive Inflationary Differential Evolution, which automatically adapts the crossover probability and the differential weight of the Differential Evolution, is presented. The multi-population algorithm exploits the use of different populations, and the local minima found by each population, to assess the distance between minima; a probabilistic kernel based approach is then used to automatically adapt the dimension of a bubble in which the population is re-initialized after converging to a local minimum. The algorithm is tested on two real case functions and on two difficult academic functions.

KW - adaptive algorithms

KW - differential evolution

KW - global optimization

UR - http://bioma.ijs.si/conference/2014/

M3 - Paper

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EP - 54

T2 - Bio-inspired Optimization Methods and their Applications, BIOMA 14

Y2 - 13 September 2014 through 13 September 2014

ER -

Multi-population adapative inflationary differential evolution

Abstract

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Keywords

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Adaptive multi-population inflationary differential evolution

Aerodynamic design optimization of wind turbine airfoils under aleatory and epistemic uncertainty

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