Authors:
Dimitris V. Koulocheris
and
Vasilis K. Dertimanis
Affiliation:
Vehicles Laboratory, National Technical University of Athens, Greece
Keyword(s):
Hybrid optimization, Evolution strategy, Deterministic mutation, Line–search, Trust–region, Vehicles.
Related
Ontology
Subjects/Areas/Topics:
Evolutionary Computation and Control
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
Abstract:
The interrelation of stochastic and deterministic optimization algorithms, as well as the exploitation of the advantages that each counterpart presents simultaneously, is studied in this paper. To this, a hybrid optimization algorithm is developed, which consists of a conventional Evolution Strategy that maintains its recombination and selection phases unaltered, while its mutation operator is replaced by well–known deterministic methods, such as line–search and/or trust–region. The alteration results in superior performance of the novel algorithm,
compared to other instances of Evolutionary Algorithms, as exploited out in tests using Griewangk and Rastrigin functions. The proposed algorithm is further examined through its implementation to the structural optimization problem of a full–car suspension model, with satisfying results.