TY - GEN
T1 - Evolutionary multitasking in combinatorial search spaces
T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
AU - Zhou, Lei
AU - Feng, Liang
AU - Zhong, Jinghui
AU - Ong, Yew Soon
AU - Zhu, Zexuan
AU - Sha, Edwin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/9
Y1 - 2017/2/9
N2 - Multifactorial optimization (MFO) is a new paradigm proposed recently for evolutionary multi-tasking. In contrast to traditional evolutionary optimization approaches, which focus on solving only a single optimization problem at a time, MFO was proposed to solve multiple optimization problems simultaneously. It is contended that the concept of evolutionary multi-tasking provides the scope for implicit knowledge transfer of useful traits across different but related problem domains, thereby enhancing the evolutionary search for problem-solving. With the aim of evolutionary multi-tasking, multifactorial evolutionary algorithm (MFEA) was proposed in [1], and demonstrated efficient multi-tasking performances on several problem domains, including continuous, discrete, and the mixtures of continuous and combinatorial tasks. To solve different problems, the design of unified solution representations and effective problem specific decoding operators are required in MFEA. In particular, the random-key unified representation and the sorting based decoding operator were presented in MFEA for multi-tasking in the context of vehicle routing problem. However, problems such as ineffective solution representation and decoding are existed in this unified representation, which would deteriorate the multi-tasking performance of MFEA. Taking this cue, in this paper, we propose an improved MFEA (P-MFEA) with a permutation based unified representation and a split based decoding operator. To evaluate the efficacy of the proposed P-MFEA, comparison against the traditional single task evolutionary search paradigm on 12 multi-tasking capacitated vehicle routing problems is presented and discussed.
AB - Multifactorial optimization (MFO) is a new paradigm proposed recently for evolutionary multi-tasking. In contrast to traditional evolutionary optimization approaches, which focus on solving only a single optimization problem at a time, MFO was proposed to solve multiple optimization problems simultaneously. It is contended that the concept of evolutionary multi-tasking provides the scope for implicit knowledge transfer of useful traits across different but related problem domains, thereby enhancing the evolutionary search for problem-solving. With the aim of evolutionary multi-tasking, multifactorial evolutionary algorithm (MFEA) was proposed in [1], and demonstrated efficient multi-tasking performances on several problem domains, including continuous, discrete, and the mixtures of continuous and combinatorial tasks. To solve different problems, the design of unified solution representations and effective problem specific decoding operators are required in MFEA. In particular, the random-key unified representation and the sorting based decoding operator were presented in MFEA for multi-tasking in the context of vehicle routing problem. However, problems such as ineffective solution representation and decoding are existed in this unified representation, which would deteriorate the multi-tasking performance of MFEA. Taking this cue, in this paper, we propose an improved MFEA (P-MFEA) with a permutation based unified representation and a split based decoding operator. To evaluate the efficacy of the proposed P-MFEA, comparison against the traditional single task evolutionary search paradigm on 12 multi-tasking capacitated vehicle routing problems is presented and discussed.
UR - https://www.scopus.com/pages/publications/85016053561
U2 - 10.1109/SSCI.2016.7850039
DO - 10.1109/SSCI.2016.7850039
M3 - 会议稿件
AN - SCOPUS:85016053561
T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 December 2016 through 9 December 2016
ER -