Paper Info
Title
Evolutionary Multi-tasking Single-Objective Optimization Based on Cooperative Co-evolutionary Memetic Algorithm
Abstract
Evolutionary multi-tasking optimization has recently emerged as a promising new topic in the field of evolutionary computation. It is a promising framework for solving different optimization problems simultaneously. Compared with the classic evolutionary algorithms, evolutionary multi-tasking optimization (MTO) can take advantage of implicit genetic transfer in the optimization process and get better performance. Distinct tasks are solved simultaneously by utilizing similarities and differences across different tasks. In this paper, an evolutionary multi-tasking single-objective optimization based on cooperative co-evolutionary memetic algorithm (EMTSO-CCMA) is proposed. A local search method based on quasi-Newton is proposed to accelerate the convergence of the proposed algorithm. The effectiveness of the proposed algorithm is shown in this paper by comparing with the multifactorial evolutionary algorithm.
Year
DOI
Venue
2017
10.1109/CIS.2017.00050
2017 13th International Conference on Computational Intelligence and Security (CIS)
Keywords
Field
DocType
evolutionary multitasking,multifactorial optimization,memetic algorithm,cooperative co-evolutionary genetic algorithm
Convergence (routing),Memetic algorithm,Mathematical optimization,Evolutionary algorithm,Computer science,Evolutionary computation,Local search (optimization),Memetics,Optimization problem,Benchmark (computing)
Conference
ISBN
Citations 
PageRank 
978-1-5386-4823-0
2
0.37
References 
Authors
11
4
Name
Order
Citations
PageRank
Qunjian Chen130.78
Xiaoliang Ma218218.51
Zexuan Zhu398957.41
Yiwen Sun4302.58