Effects of Local Mating in Inter-task Crossover on the Performance of Decomposition-based Evolutionary Multiobjective Multitask optimization Algorithms

Hashimoto，Ryuichi1; Urita，Toshiki1; Masuyama，Naoki1; Nojima，Yusuke1; Ishibuchi，Hisao2

10.1109/CEC48606.2020.9185871

2020-07-01

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

978-1-7281-6930-9

2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

1-8

JUL 19-24, 2020

null,null,ELECTR NETWORK

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Recently, Evolutionary Multiobjective Multitask optimization (EMMO) was proposed as a new research topic in the field of Evolutionary Multiobjective optimization (EMO). In contrast to conventional EMO algorithms, EMMO algorithms solve multiple multiobjective optimization problems (multiple tasks) in their single run. Most EMMO algorithms have the same number of populations as the number of tasks to be solved simultaneously, and each population corresponds to a different task. The main feature of EMMO algorithms is that offspring solutions are generated by not only intra-task crossover but also inter-task crossover. Local mating in intra-task crossover improves the search performance of EMO algorithms that use uniformly distributed weight vectors during a search, such as MOEA/D. Therefore, local mating in inter-task crossover is a promising idea for EMMO algorithms. In this paper, we propose a simple extension of MOEA/D for EMMO algorithms and a local mating method in inter-task crossover based on uniformly distributed weight vectors. Through computational experiments, we examine the effects of local mating in inter-task crossover on the search performance of the proposed algorithm. Experimental results show that the local mating improves the search performance of the proposed algorithm.

decompositionbased evolutionary multiobjective multitask optimization algorithm inter-task crossover local mating

其他

英语

EI ; CPCI-S

National Natural Science Foundation of China[61876075]

Computer Science ; Engineering ; Mathematical & Computational Biology ; Operations Research & Management Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Mathematical & Computational Biology ; Operations Research & Management Science

WOS:000703998202128

20204109316807

Evolutionary algorithms

Optimization Techniques:921.5

2-s2.0-85092060047

Scopus

1.Graduate School of Engineering Osaka Prefecture University,Department of Computer Science and Intelligent Systems,Osaka,Japan
2.Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Hashimoto，Ryuichi,Urita，Toshiki,Masuyama，Naoki,et al. Effects of Local Mating in Inter-task Crossover on the Performance of Decomposition-based Evolutionary Multiobjective Multitask optimization Algorithms[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:1-8.