A convergence-diversity balanced fitness evaluation mechanism for decomposition- based many-objective optimization algorithm

Liu, Junhua; Wang, Yuping; Fan, Ninglei; Wei, Shiwei; Wang, Tong

doi:10.3233/ica-180594

Cited by 28 publications

(4 citation statements)

References 59 publications

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“…e genetic algorithm (GA) is a bioinspired intelligence optimization algorithm. It is inspired by the process of a natural selection and belongs to one of evolutionary algorithms (EAs) [9][10][11][12][13]. It is commonly utilized to generate feasible solutions for optimization problems by performing the operators such as selection, crossover, and mutation [14][15][16].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Gene Selection and Classification of scRNA-seq Data Combining Information Gain Ratio and Genetic Algorithm with Dynamic Crossover

Feng

Niu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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Single-cell RNA sequencing (scRNA-seq) is emerging as a promising technology. There exist a huge number of genes in a scRNA-seq data. However, some genes are high quality genes, and some are noises and irrelevant genes because of unspecific technology reasons. These noises and irrelevant genes may have a strong influence on downstream data analyses, such as a cell classification, gene function analysis, and cancer biomarker detection. Therefore, it is very significant to obviate these irrelevant genes and choose high quality genes by gene selection methods. In this study, a novel gene selection and classification method is presented by combining the information gain ratio and the genetic algorithm with dynamic crossover (abbreviated as IGRDCGA). The information gain ratio (IGR) is employed to eliminate irrelevant genes roughly and obtain a preliminary gene subset, and then the genetic algorithm with a dynamic crossover (DCGA) is utilized to choose high quality genes finely from the preliminary gene subset. The main difference between the IGRDCGA and the existing methods is that the DCGA and IGR are integrated first and used to select genes from scRNA-seq data. We conduct the IGRDCGA and several competing methods on some real-world scRNA-seq datasets. The obtained results demonstrate that the IGRDCGA can choose high quality genes effectively and efficiently and outperforms the other several competing methods in terms of both the dimensionality reduction and the classification accuracy.

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Section: Genetic Algorithmmentioning

confidence: 99%

Gene Selection and Classification of scRNA-seq Data Combining Information Gain Ratio and Genetic Algorithm with Dynamic Crossover

Feng

Niu

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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“…Jiang and Yang [64] and Junhua and Yuping [65] investigated a novel combined fitness assignment mechanism in the decomposed objective space, and proposed SPEA/R and ISPEA/R. For convergence measurement, local strength and global strength are employed to assign fitness values by calculating the number of solution which are dominated or dominated.…”

Section: Other Scalarization Functionsmentioning

confidence: 99%

A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems

Guo

2022

IEEE Access

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The framework of decomposition-based multi-objective evolutionary algorithms(MOEA/D) has evolved for more than ten years, and it has become irreplaceable tool for solving multi-objective optimization problems. In recent years, many scholars have investigated improved strategies from different directions. This paper gives a systematic comparison of six different components for decomposition-based algorithms, including framework analysis, weight vector generation scheme, aggregation evaluation function construction, reproduction operator, individual selection and update strategy, and the characteristics and application scope of various algorithms are also analyzed in detail in the survey. Different from previous survey on decomposition-based multi-objective evolutionary algorithms, a more detailed classification and experimental comparison are elaborated in the proposed paper.

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“…Some authors proposed methods to manage the charge and discharge of electric vehicles in the context of the smart grids such as presented in [24], considering the use of a hybrid metaheuristic approach [25] with simulated annealing and ant colony optimization techniques to manage the energy resources in the virtual power player operating of a smart grid considering vehicle-to-grid (V2G) technology. Other technics such as the ones based on swarm optimization [26,27], Spiking Neural P System [28], decomposition techniques [29,30] or multi-objective functions [31,32], can also be used for the same purpose. Research on distributed computing and on on a cluster of workstations [33][34][35][36] can also be useful for the present application.…”

Section: Management Of Electric Vehicles In Hmsmentioning

confidence: 99%

Near real-time management of appliances, distributed generation and electric vehicles for demand response participation

Fernandes

Morais

Vale

2022

ICA

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Consumer-centric energy management approaches are emerging as a major solution for future power systems. In this context, intelligent home management systems should manage different kinds of devices existing in the houses assuring convenient comfort levels and understanding the users’ behaviour. At the same time, the home management systems should be able to interact with other actors such as energy communities, aggregators, and system operators. The main contribution of this work is a new methodology allowing intelligent management, in near real-time (1 minute), of different types of energy resources existing in a smart home. The energy resources include appliances and other loads, micro-generation, and electric vehicles. The proposed system includes a permanent evaluation of the operation state of each energy resource considering their functional model and the behaviour and comfort level defined by the users. Participation in demand response programs reducing the power consumption limits is also considered showing the advantage of the proposed approach. The case study contains two scenarios considering a demand response program of power limitation with 120 minutes duration. To guarantee participation in these demand response events, the system should evaluate the priority of each device according to its model. A domestic consumer with 45 energy resources (appliances, generation, and electric vehicles) is used for demonstration purposes.

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A convergence-diversity balanced fitness evaluation mechanism for decomposition- based many-objective optimization algorithm

Cited by 28 publications

References 59 publications

Gene Selection and Classification of scRNA-seq Data Combining Information Gain Ratio and Genetic Algorithm with Dynamic Crossover

Gene Selection and Classification of scRNA-seq Data Combining Information Gain Ratio and Genetic Algorithm with Dynamic Crossover

A Survey of Decomposition Based Evolutionary Algorithms for Many-Objective Optimization Problems

Near real-time management of appliances, distributed generation and electric vehicles for demand response participation

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