Cooperative co-evolution with graph-based differential grouping for large scale global optimization

Ling, Yingbiao; Li, Haijian; Cao, Bin

doi:10.1109/fskd.2016.7603157

Cited by 25 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Random Grouping [22] [35]: In contrast to the above three grouping techniques which are based on some heuristic strategies, differential grouping techniques take the variable interactions into consideration when performing grouping [25], where the interacting decision variables are divided into the same group. Without prior knowledge about the interactions among the decision variables or the number of groups, the performance of CC based algorithms can be influenced by the selection of different grouping techniques.…”

Section: B Grouping Techniques In CCmentioning

confidence: 99%

Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation

Tian

et al. 2019

IEEE Trans. Evol. Computat.

219

View full text Add to dashboard Cite

In this work, we propose a framework to accelerate the computational efficiency of evolutionary algorithms on largescale multi-objective optimization. The main idea is to track the Pareto optimal set directly via problem reformulation. To begin with, the algorithm obtains a set of reference directions in the decision space and associates them with a set of weight variables for locating the Pareto optimal set. Afterwards, the original large-scale multi-objective optimization problem is reformulated into a low-dimensional single-objective optimization problem. In the reformulated problem, the decision space is reconstructed by the weight variables and the objective space is reduced by an indicator function. Thanks to the low dimensionality of the weight variables and reduced objective space, a set of quasioptimal solutions can be obtained efficiently. Finally, a multiobjective evolutionary algorithm is used to spread the quasioptimal solutions over the approximate Pareto optimal front evenly. Experiments have been conducted on a variety of largescale multi-objective problems with up to 5000 decision variables. Four different types of representative algorithms are embedded into the proposed framework and compared with their original versions respectively. Furthermore, the proposed framework has been compared with two state-of-the-art algorithms for largescale multi-objective optimization. Experimental results have demonstrated the significant improvement benefited from the framework in terms of its performance and computational efficiency in large-scale multi-objective optimization.

show abstract

Section: B Grouping Techniques In CCmentioning

confidence: 99%

Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation

Tian

et al. 2019

IEEE Trans. Evol. Computat.

219

View full text Add to dashboard Cite

show abstract

“…Besides, XDG cannot model the decomposition problem formally, and it inherits the sensitivity issue of DG. To overcome these drawbacks, in 2016, Yingbiao et al [15] proposed another improvement of DG, called graphbased DG (gDG). This gDG starts by identifying all separable variables and allocating them into the same group.…”

Section: Review On Problem Decomposition Approaches Using CCmentioning

confidence: 99%

“…The performance of a CC algorithm may degrade significantly if the problem is non-separable, such as if the features interact with each other [9,13,14]. In the literature, it is suggested that the interdependency between the decomposed subproblems should be minimized because of the collaboration requirement of CC algorithms [15]. However, for real-world problems without any prior information about how the features in a dataset interact, it is difficult to find a suitable problem decomposition technique for feature selection.…”

mentioning

confidence: 99%

Cooperative co-evolution for feature selection in Big Data with random feature grouping

et al. 2020

View full text Add to dashboard Cite

A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity.

show abstract

“…Because more than one objective exists, the interactions among variables are obtained with respect to all the objectives by adopting the idea of gDG [40]. The diversity-related variables are separated into a single group.…”

Section: Variable Groupingmentioning

confidence: 99%

“…To optimize numerous variables, some promising approaches first separate the variables into groups and then optimize them in a cooperative coevolutionary (CC) [34] manner. For large-scale global optimization problems (LSGOPs), many grouping mechanisms have been applied, including fixed grouping [34], random grouping [35], the Delta method [36], dynamic grouping [37], differential grouping (DG) [38], global differential grouping (GDG) [39] and graph-based differential grouping (gDG) [40]. Antonio et al proposed the cooperative coevolutionary generalized differential evolution 3 (CCGDE3) method [41], which used fixed grouping.…”

Section: Introductionmentioning

confidence: 99%

Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

Cao

Zhao

Yang

et al. 2018

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

Using immune algorithms is generally a time-intensive process-especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large number of variables are divided into several groups. Finally, individual evaluations are allocated across many core processing units, and calculations are performed in parallel. Consequently, the computation time is greatly reduced. The proposed algorithm integrates the idea of immune algorithms, which tend to explore sparse areas in the objective space and use simulated binary crossover for mutation. The proposed algorithm is employed to optimize the 3D terrain

show abstract

Cooperative co-evolution with graph-based differential grouping for large scale global optimization

Cited by 25 publications

References 15 publications

Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation

Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation

Cooperative co-evolution for feature selection in Big Data with random feature grouping

Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

Contact Info

Product

Resources

About