Authors:
Maxim Sidorov
1
;
Eugene Semenkin
2
and
Wolfgang Minker
1
Affiliations:
1
Ulm University, Germany
;
2
Siberian State Aerospace University, Russian Federation
Keyword(s):
Genetic Algorithm, Evolution Strategy, Cuckoo Search, Differential Evolution, Particle Swarm Optimization, Benchmark Comparison, Unconstrained Optimization.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
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.