Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems

被引：106

作者：

Olivas, Frumen ^{[1
]}

Valdez, Fevrier ^{[1
]}

Castillo, Oscar ^{[1
]}

Gonzalez, Claudia I. ^{[1
]}

Martinez, Gabriela ^{[1
]}

Melin, Patricia ^{[1
]}

机构：

[1] Tijuana Inst Technol, Tijuana, Mexico

来源：

APPLIED SOFT COMPUTING | 2017年 / 53卷

关键词：

Interval type-2 fuzzy system; Ant colony optimization; Parameter adaptation; ACO;

D O I：

10.1016/j.asoc.2016.12.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A dynamic parameter adaptation methodology for Ant Colony Optimization (ACO) based on interval type-2 fuzzy systems is presented in this paper. The idea is to be able to apply this new ACO method with parameter adaptation to a wide variety of problems without the need of finding the best parameters for each particular problem. We developed several fuzzy systems for parameter adaptation and a comparison was made among them to decide on the best design. The use of fuzzy logic is to control the diversity of the solutions, and in this way controlling the exploration and exploitation abilities of ACO. The travelling salesman problem (TSP) and the design of a fuzzy controller for an autonomous mobile robot are the benchmark problems used to test the proposed methodology. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：74 / 87

页数：14

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