Unconstrained Global Optimization: A Benchmark Comparison of Population-based Algorithms

被引：0
|
作者：
Sidorov, Maxim [1 ]
Semenkin, Eugene [2 ]
Minker, Wolfgang [1 ]
机构：
[1] Univ Ulm, Inst Commun Engn, Ulm, Germany
[2] Siberian State Aerosp Univ, Inst Syst Anal, Krasnoyarsk, Russia
来源：
ICIMCO 2015 PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL. 1 | 2015年
关键词：
Genetic Algorithm; Evolution Strategy; Cuckoo Search; Differential Evolution; Particle Swarm Optimization; Benchmark Comparison; Unconstrained Optimization;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper we provide a systematic comparison of the following population-based optimization techniques: Genetic Algorithm (GA), Evolution Strategy (ES), Cuckoo Search (CS), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The considered techniques have been implemented and evaluated on a set of 67 multivariate functions. We carefully selected the tested optimization functions which have different features and gave exactly the same number of objective function evaluations for all of the algorithms. This study has revealed that the DE algorithm is preferable in the majority of cases of the tested functions. The results of numerical evaluations and parameter optimization are presented in this paper.
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收藏
页码：230 / 237
页数：8
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