State-of-the-art Multiobjective Evolutionary Algorithms---Pareto Ranking, Density Estimation and Dynamic Population


The major interest of this research work is to investigate multiobjective optimization problems by evolutionary algorithms. This research classifies existing Multiobjective Evolutionary Algorithms (MOEAs) and analyzes several advanced MOEAs based on different design procedures of those crucial building blocks. Moreover, a Rank-Density based Genetic Algorithm (RDGA), a Dynamic Multiobjective Evolutionary Algorithm (DMOEA) and a Dynamic Particle Swarm Evolutionary Algorithm (DPSEA) are designed to generate a state-of-the-art MOEA, which can efficiently and effectively approximate a near-complete, near-optimal and uniformly distributed Pareto front for a given MOP. Findings and conclusions. From the simulation results of selected quantitative performance indicators, RDGA is found to be competitive with, or even superior to, other representative MOEAs in terms of keeping the diversity of the individuals along the trade-off surface, tending to extend the Pareto front to new areas, and finding a well-approximated Pareto optimal front. However, by suing fixed population size, RDGA is found to have difficulties in dealing with the most challenging issue existing in MOEA design— the confliction between avoiding and exploiting "genetic drift" phenomenon. For this reason, DMOEA and DPSEA show their advantages in estimating optimal population size, resulting in a higher quality Pareto front and faster convergence speed than RDGA and other representative MOEAs.