A new hybrid memetic multi-objective optimization algorithm for multi-objective optimization

To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In t...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences 2018-06, Vol.448-449, p.164-186
Main Authors: Luo, Jianping, Yang, Yun, Liu, Qiqi, Li, Xia, Chen, Minrong, Gao, Kaizhou
Format: Article
Language:English
Subjects:
Algorithm diversity
Evolutionary computing
Extremal optimization
Hybrid computing
Multiple objective programming
Shuffled frog leaping algorithm
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To deal with the multi-objective optimization problems (MOPs), a meta-heuristic based on an improved shuffled frog leaping algorithm (ISFLA) which belongs to memetic evolution is presented. For the MOPs, both diversity maintenance and searching effectiveness are crucial for algorithm evolution. In this work, modified calculation of crowding distance to evaluate the density of a solution, memeplex clustering analyses based on a grid to divide the population, and new selection measure of global best individual are proposed to ensure the diversity of the algorithm. A multi-objective extremal optimization procedure (MEOP) is also introduced and incorporated into ISFLA to enable the algorithm to evolve more effectively. Finally, the experimental tests on thirteen unconstrained MOPs and DTLZ many-objective problems show that the proposed algorithm is flexible to handle MOPs and many-objective problems. The effectiveness and robustness of the proposed algorithm are also analyzed in detail.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2018.03.012