Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …

Whereas evolutionary multiobjective optimization (EMO) algorithms have successfully been
used in a wide range of real-world application tasks, difficulties in their scalability to many …

Recently, a number of high performance many-objective evolutionary algorithms with
systematically generated weight vectors have been proposed in the literature. Those …

Achieving balance between convergence and diversity is a key issue in evolutionary
multiobjective optimization. Most existing methodologies, which have demonstrated their …

Many-objective optimization has posed a great challenge to the classical Pareto dominance-
based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary …

Many-objective optimization problems (ManyOPs) refer, usually, to those multiobjective
problems (MOPs) with more than three objectives. Their large numbers of objectives pose …

Balancing convergence and diversity plays a key role in evolutionary multiobjective
optimization (EMO). Most current EMO algorithms perform well on problems with two or three …

The decomposition-based multiobjective evolutionary algorithms (MOEAs) generally make
use of aggregation functions to decompose a multiobjective optimization problem into …

Software systems nowadays are complex and difficult to maintain due to continuous
changes and bad design choices. To handle the complexity of systems, software products …

Multi-objective evolutionary algorithms (MOEAs) are well-suited for solving several complex
multi-objective problems with two or three objectives. However, as the number of conflicting …