A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …

Optimization in dynamic environments is a challenging but important task since many real-
world optimization problems are changing over time. Evolutionary computation and swarm …

When solving dynamic multiobjective optimization problems (DMOPs) by evolutionary
algorithms, the historical moving directions of some special points along the Pareto front …

Evolutionary algorithms are effective in solving static multiobjective optimization problems
resulting in the emergence of a number of state-of-the-art multiobjective evolutionary …

In this paper, a neighborhood mutation strategy is proposed and integrated with various
niching differential evolution (DE) algorithms to solve multimodal optimization problems …

In the real world, many optimization problems are dynamic. This requires an optimization
algorithm to not only find the global optimal solution under a specific environment but also to …

This paper provides a survey of the research done on optimization in dynamic environments
over the past decade. We show an analysis of the most commonly used problems, methods …

This work describes a forward-looking approach for the solution of dynamic (time-changing)
problems using evolutionary algorithms. The main idea of the proposed method is to …

To promote research on dynamic constrained multiobjective optimization, we first propose a
group of generic test problems with challenging characteristics, including different modes of …

To solve dynamic optimization problems, multiple population methods are used to enhance
the population diversity for an algorithm with the aim of maintaining multiple populations in …