Name
Title | ||
|---|---|---|
Adaptive Memetic Algorithm Based Evolutionary Multi-tasking Single-Objective Optimization. |
Abstract | ||
|---|---|---|
Evolutionary multitasking optimization has recently emerged as an effective framework to solve different optimization problems simultaneously. Different from the classic evolutionary algorithms, multi-task optimization (MTO) is designed to take advantage of implicit genetic transfer in a multitasking environment. It deals with multiple tasks simultaneously by leveraging similarities and differences across different tasks. However, MTO still suffers from a few issues. In this paper, a multifactorial memetic algorithm is introduced to solve the single-objective MTO problems. Particularly, the proposed algorithm introduces a local search method based on quasi-Newton, reinitializes a port of worse individuals, and suggests a self-adapt parent selection strategy. The effectiveness of the proposed algorithm is validated by comparing with the multifactorial evolutionary algorithm proposed in CEC’17 competition. |
Year | Venue | Field |
|---|---|---|
2017 | SEAL | Memetic algorithm,Mathematical optimization,Evolutionary algorithm,Computer science,Local search (optimization),Evolutionary programming,Single objective,Human multitasking,Optimization problem |
DocType | Citations | PageRank |
Conference | 1 | 0.41 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qunjian Chen | 1 | 3 | 0.78 |
| Xiaoliang Ma | 2 | 182 | 18.51 |
| Yiwen Sun | 3 | 30 | 2.58 |
| Zexuan Zhu | 4 | 989 | 57.41 |