TY - GEN
T1 - Evolutionary multi-objective simulated annealing with adaptive and competitive search direction
AU - Li, Hui
AU - Landa-Silva, Dario
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/55749088184
U2 - 10.1109/CEC.2008.4631246
DO - 10.1109/CEC.2008.4631246
M3 - 会议稿件
AN - SCOPUS:55749088184
SN - 9781424418237
T3 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
SP - 3311
EP - 3318
BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
T2 - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
Y2 - 1 June 2008 through 6 June 2008
ER -