ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

被引：28

作者：

Humeau, J. ^{[1
]}

Liefooghe, A. ^{[2
,3
]}

Talbi, E-G ^{[2
,3
]}

Verel, S. ^{[2
,4
]}

机构：

[1] Ecole Mines Douai, Dept IA, F-59508 Douai, France

[2] Inria Lille Nord Europe, DOLPHIN Res Team, F-59650 Villeneuve Dascq, France

[3] Univ Lille 1, UMR CNRS 8022, Lab LIFL, F-59655 Villeneuve Dascq, France

[4] Univ Nice Sophia Antipolis, UMR CNRS 6070, Lab I3S, F-06903 Sophia Antipolis, France

来源：

JOURNAL OF HEURISTICS | 2013年 / 19卷 / 06期

关键词：

Local search; Metaheuristic; Fitness landscapes; Conceptual unified model; Algorithm design and analysis; Software framework; TRAVELING SALESMAN PROBLEM; GLOBAL OPTIMIZATION; FRAMEWORK; PARALLEL; EVOLUTION; MODEL;

D O I：

10.1007/s10732-013-9228-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search metaheuristics: ParadisEO-MO. A substantial number of single solution-based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition, a conceptual model is proposed and is validated by regarding a number of state-of-the-art methodologies as simple variants of the same structure. This model is then incorporated into the ParadisEO-MO software framework. This framework has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.

引用

页码：881 / 915

页数：35