Complexity and variety of modern multiobjective optimisation problems result in the
emergence of numerous search techniques, from traditional mathematical programming to …

This article suggests a multimodal multiobjective evolutionary algorithm with dual clustering
in decision and objective spaces. One clustering is run in decision space to gather nearby …

Task scheduling is an important research direction in cloud computing. The current research
on task scheduling considers mainly the design of scheduling strategies and algorithms and …

In many-objective optimization problems (MaOPs), forming sound tradeoffs between
convergence and diversity for the environmental selection of evolutionary algorithms is a …

In decomposition-based multi-objective evolutionary algorithms (MOEAs), a set of uniformly
distributed reference vectors (RVs) is usually adopted to decompose a multi-objective …

Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-
objective optimization problems (MaOPs). Generally, different solution-sorting methods …

For many-objective optimization problems (MaOPs), the proportion of non-dominated
solutions in a population scales up sharply with the increase in the number of objectives …

Solving constrained multiobjective optimization problems (CMOPs) is a challenging task
since it is necessary to optimize several conflicting objective functions and handle various …

Evaluation and benchmarking of many-objective optimization (MaOO) methods are
complicated. The rapid development of new optimization algorithms for solving problems …

Multi-objective optimization problems often load in the multi-scenario environment, and they
can be modeled as multi-scenario multi-objective optimization problems (MSMOs). So far …