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Evolutionary multi-objective simulated annealing with adaptive and competitive search direction

Li, Hui; Landa-Silva, Dario

Authors

Hui Li



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