Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Using unconstrained elite archives for multiobjective optimization
IEEE Transactions on Evolutionary Computation
A dominance tree and its application in evolutionary multi-objective optimization
Information Sciences: an International Journal
A fast multi-objective evolutionary algorithm based on a tree structure
Applied Soft Computing
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There has emerged a surge of research activity on multiobjective optimization using evolutionary computation in recent years and a number of well performing algorithms have been published. The quick and highly efficient multiobjective evolutionary algorithm based on dominating tree has been criticized mainly for its restricted elite archive and absence of density estimation. This paper improves the algorithm in these two aspects. The nearest distance between the node and other nodes in the same sibling chain is used as its density estimation; the population growing and declining strategies are proposed to avoid the retreating and shrinking phenomenon caused by the restricted elite archive. The simulation results show that the improved algorithm is able to maintain a better spread of solutions and converge better in the obtained nondominated front compared with NSGA-II, SPEA2 and the original algorithm for most test functions.