Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails

Hui Li, Dario Landa-silva, Xavier Gandibleux
2010 IEEE Congress on Evolutionary Computation  
Recently, the research on quantum-inspired evolutionary algorithms (QEA) has attracted some attention in the area of evolutionary computation. QEA use a probabilistic representation, called Q-bit, to encode individuals in population. Unlike standard evolutionary algorithms, each Q-bit individual is a probability model, which can represent multiple solutions. Since probability models store global statistical information of good solutions found previously in the search, QEA have good potential to
more » ... deal with hard optimization problems with many local optimal solutions. So far, not much work has been done on evolutionary multi-objective (EMO) algorithms with probabilistic representation. In this paper, we investigate the performance of two state-of-the-art EMO algorithms -MOEA/D and NSGA-II, with probabilistic representation based on pheromone trails, on the multi-objective travelling salesman problem. Our experimental results show that MOEA/D and NSGA-II with probabilistic presentation are very promising in sampling high-quality offspring solutions and in diversifying the search along the Pareto fronts.
doi:10.1109/cec.2010.5585998 dblp:conf/cec/LiSG10 fatcat:3jscrmbkavatvnn4ute7qv3bme