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

Abstract-- 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 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 presen...

• Type: Conference
• Year: 2010
• Where: CEC
• Authors: Hui Li, Dario Landa Silva, Xavier Gandibleux
• Keywords: Artificial Intelligence, CEC 2010, Evolutionary Algorithms, Probabilistic Representation, Quantum-Inspired Evolutionary Algorithms