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
收藏
页码：881 / 915
页数：35
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