Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE Congress on Evolutionary Computation, CEC 2018 |
| Subtitle of host publication | Proceedings |
| Place of Publication | Piscataway |
| Publisher | IEEE |
| Pages | 1-8 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-5090-6017-7 |
| ISBN (Print) | 978-1-5090-6018-4 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 |
Conference
| Conference | 2018 IEEE Congress on Evolutionary Computation, CEC 2018 |
|---|---|
| Country/Territory | Brazil |
| City | Rio de Janeiro |
| Period | 8/07/18 → 13/07/18 |
Keywords
- Computationally expensive optimization
- heuristic optimization
- mean-variance mapping optimization
- single objective optimization