Title :
A novel hybridization of opposition-based learning and cooperative co-evolutionary for large-scale optimization
Author :
Kazimipour, Borhan ; Omidvar, Mohammad Nabi ; Xiaodong Li ; Qin, A.K.
Author_Institution :
Sch. of Comput. Sci. & Inf. Technol., RMIT Univ., Melbourne, VIC, Australia
Abstract :
Opposition-based learning (OBL) and cooperative co-evolution (CC) have demonstrated promising performance when dealing with large-scale global optimization (LSGO) problems. In this work, we propose a novel framework for hybridizing these two techniques, and investigate the performance of simple implementations of this new framework using the most recent LSGO benchmarking test suite. The obtained results verify the effectiveness of our proposed OBL-CC framework. Moreover, some advanced statistical analyses reveal that the proposed hybridization significantly outperforms its component methods in terms of the quality of finally obtained solutions.
Keywords :
evolutionary computation; learning (artificial intelligence); optimisation; statistical analysis; LSGO benchmarking test suite; LSGO problems; OBL-CC framework; cooperative co-evolutionary hybridization; large-scale global optimization problems; opposition-based learning hybridization; solution quality; statistical analysis; Benchmark testing; Context; Heuristic algorithms; Optimization; Sociology; Statistics; Vectors;
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
DOI :
10.1109/CEC.2014.6900639