Decomposition for Large-scale Optimization Problems with Overlapping Components

Sun, Yuan; Li, Xiaodong; Ernst, Andreas T.; Omidvar, Mohammad Nabi

doi:10.1109/cec.2019.8790204

Cited by 63 publications

(55 citation statements)

References 38 publications

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“…This new algorithm, RDG3, breaks the linkage at variables, which is shared by more than one component. The performance of RDG3 has been evaluated by overlapping benchmark problems, which confirmed its superiority over RDG and other decomposition algorithms [50]. Table 2 presents a summary of the papers reviewed on problem decomposition techniques with key features.…”

Section: Review On Problem Decomposition Approaches Using CCmentioning

confidence: 70%

“…By exploring the interdependency information in the problem, a number of detection-based static decomposition techniques proposed to make the near-optimal decomposition. Examples of such static decomposition methods include CCVIL [31], DG [44], XDG [45], GDG [46], RDG [10], D-GDG [48], RDG2 [49], and RDG3 [50]. These methods are also called automatic decomposition strategies as they undertake the variable interactions to group the variables [10].…”

Section: Review On Problem Decomposition Approaches Using CCmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Review On Problem Decomposition Approaches Using CCmentioning

confidence: 70%

Section: Review On Problem Decomposition Approaches Using CCmentioning

confidence: 99%

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

et al. 2020

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“…Later, Mei et al (2016) proposed a global DG strategy (GDG), which could improve decomposition accuracy by maintaining the global information, and also applied it to CMA-ES, forming CC-GDG-CMA-ES. Inspired by the DG2 and RDG strategy, Sun et al proposed two RDG variants which combined with CMA-ES, called RDG2 (Sun et al 2018b) and RDG3 (Sun et al 2019a), respectively, to better discover the relationship between decision variables and decompose the overlapping problems. In addition, RDG3 is the winning algorithm in the IEEE Congress on Evolutionary Computation (CEC) 2019 Competition.…”

Section: Reducing Problem Difficultymentioning

confidence: 99%

“…The new CC framework can not only save computational resources by checking whether a subpopulation is stagnant but also update the contribution of a subpopulation dynamically. In addition, some adaptive computation resource allocation strategies combined with CC methods have been proposed, including boosting CC method (Ren et al 2019), dynamic CC method (Zhang et al 2019c), and distributed CC method (Sun et al 2019a). Recently, Irawan et al (2020) proposed a two-stage CC method with CMA-ES to solve LSOP.…”

Section: Reducing Problem Difficultymentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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“…Mei et al proposed a global differential grouping (GDG) to alleviate the sensitivity of DG to threshold by setting a global threshold [26]. Sun et al proposed a recursive differential grouping (RDG) [27] and RDG's variants RDG2 [28] and RDG3 [29] to further improve the performance of the DG decomposition problems.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Coevolution with Two-Stage Decomposition for Large-Scale Global Optimization Problems

Yue

Sun

2021

Discrete Dynamics in Nature and Society

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Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems.

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Decomposition for Large-scale Optimization Problems with Overlapping Components

Abstract: This is a repository copy of Decomposition for Large-scale Optimization Problems with Overlapping Components.

Cited by 63 publications

References 38 publications

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

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

A survey on evolutionary computation for complex continuous optimization

Cooperative Coevolution with Two-Stage Decomposition for Large-Scale Global Optimization Problems

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