PHC-NSGA-II: A Novel Multi-objective Memetic Algorithm for Continuous Optimization

被引：10

作者：

Bechikh, Slim ^{[1
]}

Belgasmi, Nabil ^{[1
]}

Ben Said, Lamjed ^{[1
]}

Ghedira, Khaled ^{[1
]}

机构：

[1] Higher Inst Management Tunis, Intelligent Informat Engn Lab, Tunis, Tunisia

来源：

20TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL 1, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/ICTAI.2008.87

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce in this paper a new multi-objective memetic algorithm. This algorithm is a result of hybridization of the NSGA-II algorithm with a new designed local search procedure that we named Pareto Hill Climbing. Verification of our novel algorithm is carried out by testing it on two sets of multi-objective test problems and comparing it to other multi-objective evolutionary algorithms (MOEAs) and other multi-criterion memetic algorithms (MMAs). Simulation results show the algorithm ability in tackling continuous multi-objective problems in terms of convergence and diversity. Our hybrid algorithm (1) outperforms pure MOEAs, (2) is competent with other gradient based MMAs, and (3) can solve non differentiable problems.

引用

页码：180 / 189

页数：10

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