Simultaneous feature selection and weighting – An evolutionary multi-objective optimization approach

Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many recent real-world applications in which each entity has more than one label. To improve the performance of multi-label classification, feature selection plays an important role. It involves identifying and removing irrelevant and redundant features that unnecessarily increase the dimensions of the search space for the classification problems. However, classification may fail with an extreme decrease in the number of relevant features. Thus, minimizing the number of features and maximizing the classification accuracy are two desirable but conflicting objectives in multi-label feature selection. In this article, we introduce a multi-objective optimization algorithm customized for selecting the features of multi-label data. The proposed algorithm is an enhanced variant of a decomposition-based multi-objective optimization approach, in which the multi-label feature selection problem is divided into single-objective subproblems that can be simultaneously solved using an evolutionary algorithm. This approach leads to accelerating the optimization process and finding more diverse feature subsets. The proposed method benefits from a local search operator to find better solutions for each subproblem. We also define a pool of genetic operators to generate new feature subsets based on old generation. To evaluate the performance of the proposed algorithm, we compare it with two other multi-objective feature selection approaches on eight real-world benchmark datasets that are commonly used for multi-label classification. The reported results of multi-objective method evaluation measures, such as hypervolume indicator and set coverage, illustrate an improvement in the results obtained by the proposed method. Moreover, the proposed method achieved better results in terms of classification accuracy with fewer features compared with state-of-the-art methods.

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Multi-objective Optimization Approach to Meet Water, Energy, and Food Needs in an Arid Region Involving Security Assessment

ACS Sustainable Chemistry & Engineering ◽

10.1021/acssuschemeng.0c09322 ◽

2021 ◽

Vol 9 (13) ◽

pp. 4771-4790

Author(s):

Xate Geraldine Sánchez-Zarco ◽

Ramón González-Bravo ◽

José María Ponce-Ortega

Keyword(s):

Arid Region ◽

Optimization Approach ◽

Security Assessment ◽

Multi Objective Optimization ◽

Multi Objective ◽

Food Needs

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Climate adaptation planning for cultural heritages in coastal tourism destinations: A multi-objective optimization approach

Tourism Management ◽

10.1016/j.tourman.2021.104380 ◽

2022 ◽

Vol 88 ◽

pp. 104380

Author(s):

Peizhe Li ◽

Xiao Xiao ◽

Erin Seekamp

Keyword(s):

Climate Adaptation ◽

Adaptation Planning ◽

Optimization Approach ◽

Multi Objective Optimization ◽

Coastal Tourism ◽

Multi Objective ◽

Tourism Destinations ◽

Climate Adaptation Planning

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Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

Journal of Marine Science and Engineering ◽

10.3390/jmse9050478 ◽

2021 ◽

Vol 9 (5) ◽

pp. 478

Author(s):

Hao Chen ◽

Weikun Li ◽

Weicheng Cui ◽

Ping Yang ◽

Linke Chen

Keyword(s):

Design Optimization ◽

Optimization Algorithm ◽

Multidisciplinary Design Optimization ◽

Robotic Fish ◽

Multidisciplinary Design ◽

Optimization Approach ◽

Multi Objective Optimization ◽

Multi Objective ◽

Cfd Method ◽

The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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