Solving Large Scale Optimization Problems by Opposition-Based Differential Evolution (ODE)

This work investigates the performance of Differential Evolution (DE) and its opposition-based version (ODE) on large scale optimization problems. Opposition-based differential evolution (ODE) has been proposed based on DE; it employs opposition-based population initialization and generation jumping to accelerate convergence speed. ODE shows promising results in terms of convergence rate, robustness, and solution accuracy. A recently proposed seven-function benchmark test suite for the CEC-2008 special session and competition on large scale global optimization has been utilized for the current investigation. Results interestingly confirm that ODE outperforms its parent algorithm (DE) on all high dimensional (500D and 1000D) benchmark functions (F1-F7). Furthermore, authors recommend to utilize ODE for more complex search spaces as well. Because results confirm that ODE performs much better than DE when the dimensionality of the problems is increased from 500D to 1000D. All required details about the testing platform, comparison methodology, and also achieved results are provided. Key–Words: Opposition-Based Differential Evolution (ODE), Opposition-Based Optimization (OBO), OppositionBased Computation (OBC), Cooperative Coevolutionary Algorithms (CCA), Large Scale Optimization, Scalability, High-Dimensional Problems