Paper Info
Title
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 presentation are very promising in sampling high-quality offspring solutions and in diversifying the search along the Pareto fronts.
Year
DOI
Venue
2010
10.1109/CEC.2010.5585998
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
pheromone trails,evolutionary computation,evolutionary multiobjective optimization algorithms,probabilistic representation,travelling salesman problems,multiobjective travelling salesman problem,quantum-inspired evolutionary algorithms,q-bit,global statistical information,pareto fronts,travelling salesman problem,q bit,evolutionary computing,optimization problem,algorithm design and analysis,optimization,pareto front,probabilistic logic,evolutionary algorithm
Population,Evolutionary algorithm,Estimation of distribution algorithm,Computer science,Multi-objective optimization,Artificial intelligence,Probabilistic logic,Optimization problem,Mathematical optimization,Evolutionary computation,Algorithm,Probabilistic analysis of algorithms,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6909-3
3
0.44
References 
Authors
0
3
Name
Order
Citations
PageRank
Hui Li1151.10
Dario Landa Silva231628.38
Xavier Gandibleux343632.53