Hybrid Single Parent-Offspring MVMO for Solving CEC2018 Computationally Expensive Problems

Hybrid Single Parent-Offspring MVMO for Solving CEC2018 Computationally Expensive Problems

Rueda, José L. (TU Delft Intelligent Electrical Power Grids)
Erlich, Istvan (Universität Duisburg-Essen)

2018

Mean-Variance Mapping Optimization (MVMO) belongs to the family of evolutionary algorithms, and has proven to be competitive in solving computationally expensive problems proposed in the Icompetitions CEC2014, CEC2015, and CEC2016. MVMO can tackle such problems by evolving a set of solutions (population based approach) or a single solution (single parent-offspring approach). The evolutionary mechanism of MVMO performs within a normalized search space in the range [0, 1]. The power of MVMO stems from its ability - based on statistical analysis of the evolving solution based on a mapping function - to adaptively shift the search priority from exploration to exploitation. This paper introduces a newly defined mapping function as well as a new rule for using an embedded local search strategy, and presents several tests conducted by using the test bed of the CEC2018 competition. Numerical results indicate significant improvements on the results obtained in CEC2016 competition.

Computationally expensive optimization
heuristic optimization
mean-variance mapping optimization
single objective optimization

http://resolver.tudelft.nl/uuid:479ce647-e1d9-40c7-b4f1-b52e02e92947

https://doi.org/10.1109/CEC.2018.8477807

IEEE, Piscataway

978-1-5090-6018-4

2018 IEEE Congress on Evolutionary Computation, CEC 2018: Proceedings

2018 IEEE Congress on Evolutionary Computation, CEC 2018, 2018-07-08 → 2018-07-13, Rio de Janeiro, Brazil

Institutional Repository

conference paper

© 2018 José L. Rueda, Istvan Erlich