Author:
Evgenii Sopov
Affiliation:
Siberian State Aerospace University, Russian Federation
Keyword(s):
Multimodal Optimization, Self-Configuration, Genetic Algorithm, Metaheuristic, Niching.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Concurrent Co-Operation
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
Multimodal optimization (MMO) is the problem of finding many or all global and local optima. In recent
years many efficient nature-inspired techniques (based on ES, PSO, DE and others) have been proposed for
real-valued problems. Many real-world problems contain variables of many different types, including
integer, rank, binary and others. In this case, the weakest representation (namely binary representation) is
used. Unfortunately, there is a lack of efficient approaches for problems with binary representation. Existing
techniques are usually based on general ideas of niching. Moreover, there exists the problem of choosing a
suitable algorithm and fine tuning it for a certain problem. In this study, a novel approach based on a
metaheuristic for designing multi-strategy genetic algorithm is proposed. The approach controls the
interactions of many search techniques (different genetic algorithms for MMO) and leads to the self-configuring
solving of problems with a priori unkn
own structure. The results of numerical experiments for
classical benchmark problems and benchmark problems from the CEC competition on MMO are presented.
The proposed approach has demonstrated efficiency better than standard niching techniques and comparable
to advanced algorithms. The main feature of the approach is that it does not require the participation of the
human-expert, because it operates in an automated, self-configuring way.
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