Title :
Testing MVMO on learning-based real-parameter single objective benchmark optimization problems
Author :
Rueda, Jose L. ; Erlich, Istvan
Author_Institution :
Department of Electrical Sustainable Energy, Delft University of Technology, Delft, The Netherlands
Abstract :
Mean-variance mapping optimization (MVMO) is an emerging evolutionary algorithm, which adopts a single-solution based approach and performs evolutionary operations within a normalized range of the search for all optimization variables. MVMO uses a special mapping function for mutation operation, which allows a controlled shift from exploration priority at early stages of the search process to exploitation at later stages. Recently, the MVMO has been extended to a population-based and hybrid variant denoted as MVMO-SH, which includes strategies for local search and multi-parent crossover. This paper provides an study on the performance of MVMO-SH on the IEEE-CEC 2015 competition test suite on learning-based real-parameter single objective optimization. Experimental results evidence the effectiveness of MVMO-SH for successfully solving different optimization problems with different mathematical properties and dimensionality.
Keywords :
Arrays; Benchmark testing; Flowcharts; Heuristic algorithms; Optimization; Shape; Tuning; Heuristic optimization; learning-based optimization; mean-variance mapping optimization; single objective optimization;
Conference_Titel :
Evolutionary Computation (CEC), 2015 IEEE Congress on
Conference_Location :
Sendai, Japan
DOI :
10.1109/CEC.2015.7257002