2,629 research outputs found
A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances
Dynamic changes and multi-dimensional evolution of portfolio optimization
Although there has been an increasing number of studies investigate portfolio optimization from different perspectives, few
attempts could be found that focus on the development trend
and hotspots of this research area. Therefore, it motivates us to
comprehensively investigate the development of portfolio optimization research and give some deep insights into this knowledge domain. In this paper, some bibliometric methods are
utilized to analyse the status quo and emerging trends of
portfolio optimization research on various aspects such as
authors, countries and journals. Besides, ‘theories’, ‘models’ and
‘algorithms’, especially heuristic algorithms are identified as the
hotspots in the given periods. Furthermore, the evolutionary analysis tends to presents the dynamic changes of the cutting-edge
concepts of this research area in the time dimension. It is found
that more portfolio optimization studies were at an exploration
stage from mean-variance analysis to consideration of multiple
constraints. However, heuristic algorithms have become the driving force of portfolio optimization research in recent years. Multidisciplinary analyses and applications are also the main trends of
portfolio optimization research. By analysing the dynamic changes
and multi-dimensional evolution in recent decades, we contribute
to presenting some deep insights of the portfolio optimization
research directly, which assists researchers especially beginners to
comprehensively learn this research field
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