Name
Abstract | ||
|---|---|---|
Multi-tasking optimization has now become a promising research topic that has attracted increasing attention from researchers. In this paper, an efficient memetic evolutionary multi-tasking optimization framework is proposed. The key idea is to use multiple subpopulations to solve multiple tasks, with each subpopulation focusing on solving a single task. A knowledge transferring crossover is proposed to transfer knowledge between subpopulations during the evolution. The proposed framework is further integrated with a multi-objective differential evolution and an adaptive local search strategy, forming a memetic multiobjective DE named MM-DE for multi-tasking optimization. The proposed MM-DE is compared with the state-of-the-art multi-tasking multi-objective evolutionary algorithm (named MO-MFEA) on nine benchmark problems in the CEC 2017 multi-tasking optimization competition. The experimental results have demonstrated that the proposed MM-DE can offer very promising performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CEC.2018.8477722 | 2018 IEEE Congress on Evolutionary Computation (CEC) |
Keywords | Field | DocType |
Evolutionary Algorithm,Memetic Algorithm,Local Search,Multi-tasking Optimization,Multi-objective Optimization,Differential Evolution | Crossover,Evolutionary algorithm,Task analysis,Computer science,Evolutionary computation,Differential evolution,Artificial intelligence,Local search (optimization),Memetics,Human multitasking,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-6018-4 | 1 | 0.35 |
References | Authors | |
19 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongliang Chen | 1 | 17 | 3.27 |
| Jing-hui Zhong | 2 | 380 | 33.00 |
| Mingkui Tan | 3 | 501 | 38.31 |