Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied ...
In this paper, a novel multi-objective orthogonal simulated annealing algorithm MOOSA using a generalized Pareto-based scale-independent fitness function and multi-objective intell...
In this paper we explore the model–building issue of multiobjective optimization estimation of distribution algorithms. We argue that model–building has some characteristics t...
In this study, we introduce two improved assessment metrics of multiobjective optimizers, Nondominated Ratio and Spacing Distribution, and analyze their rationality and validity. ...
Maoguo Gong, Licheng Jiao, Haifeng Du, Ronghua Sha...
Abstract. In this paper, a general framework of quantum-inspired multiobjective evolutionary algorithms is proposed based on the basic principles of quantum computing and general s...