Hui Li
Evolutionary multi-objective simulated annealing with adaptive and competitive search direction
Li, Hui; Landa-Silva, Dario
Authors
Professor DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
PROFESSOR OF COMPUTATIONAL OPTIMISATION
Abstract
In this paper, we propose a population-based implementation of simulated annealing to tackle multi-objective optimisation problems, in particular those of combinatorial nature. The proposed algorithm is called Evolutionary Multiobjective Simulated Annealing Algorithm (EMOSA), which combines local and evolutionary search by incorporating two distinctive features. The first feature is to tune the weight vectors of scalarizing functions (i.e., search directions) for selection during local search using a two-phase strategy. The second feature is the competition between members of the current population with similar weight vectors. We compare the proposed algorithm to three other multi-objective simulated annealing algorithms and also to the Pareto archived evolutionary strategy (PAES). Experiments are carried out on a set of bi-objective travelling salesman problem (TSP) instances with convex or nonconvex Pareto-optimal fronts. Our experimental results demonstrate that the two-phase tuning of weight vectors and the competition between individuals make a significant contribution to the improved performance of EMOSA. © 2008 IEEE.
Citation
Li, H., & Landa-Silva, D. (2008, June). Evolutionary multi-objective simulated annealing with adaptive and competitive search direction. Presented at 2008 IEEE Congress on Evolutionary Computation, CEC 2008, Hong Kong, China
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | 2008 IEEE Congress on Evolutionary Computation, CEC 2008 |
Start Date | Jun 1, 2008 |
End Date | Jun 6, 2008 |
Online Publication Date | Sep 23, 2008 |
Publication Date | Nov 14, 2008 |
Deposit Date | Feb 10, 2020 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 3311-3318 |
DOI | https://doi.org/10.1109/CEC.2008.4631246 |
Public URL | https://nottingham-repository.worktribe.com/output/3088168 |
Publisher URL | https://ieeexplore.ieee.org/document/4631246 |
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