MOPSO for dynamic feature selection problem based big data fusion

Aboud, Ahlem; Fdhila, Raja; Alimi, Adel M.

doi:10.1109/smc.2016.7844846

Cited by 15 publications

(14 citation statements)

References 17 publications

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“…• Step 6: state the decision rule, according to nine degree of freedom and α= 0.05 is defined following to the Chi-Square (χ 2 ) table 1 . The critical value (CV) is equal to 16.92, if the computed χ 2 greater than CV we're going to reject the null hypothesis H 0 .…”

Section: A Procedures Of Friedman Two-way Analysis Of Variancementioning

confidence: 99%

“…The re-evaluation of objectives and constraints' values of certain percentage or randomly selected outdated population are very useful. Different response strategies using explicit actions like the re-initialization or hypermutation of populations is considered for ensuring fast convergence and diversity [1], [26]. PSO-based approaches are very considered for solving DSOP and a few methods are designed to solve DMOPs [2].…”

Section: Introductionmentioning

confidence: 99%

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DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

Aboud¹,

Fdhila²,

Hussain³

et al. 2022

Preprint

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Distributed architecture-based Particle Swarm Optimization is very useful for static optimization and not yet explored to solve complex dynamic multi-objective optimization problems. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm with two optimization levels. In the first level, all solutions are optimized in the same search space and the second level is based on a distributed architecture using the Pareto ranking operator for dynamic multi-swarm subdivision. The proposed approach adopts a dynamic handling strategy using a set of detectors to keep track of change in the objective function that is impacted by the problem’s time-varying parameters at each level. To ensure timely adaptation during the optimization process, a dynamic response strategy is considered for the reevaluation of all non-improved solutions, while the worst particles are replaced with a newly generated one. The convergence and<br>diversity performance of the DPb-MOPSO algorithm are proven through Friedman Analysis of Variance, and the Lyapunov theorem is used to prove stability analysis over the Inverted Generational Distance (IGD) and Hypervolume Difference (HVD) metrics. Compared to other evolutionary algorithms, the novel DPb-MOPSO is shown to be most robust for solving complex problems over a range of changes in both the Pareto Optimal Set and Pareto Optimal Front. <br>

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Section: A Procedures Of Friedman Two-way Analysis Of Variancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

Aboud¹,

Fdhila²,

Hussain³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Users identify a problem for properly representing and interpreting the same real-world objects recovered from different data sources. In this context, [154] presents an approach to solve the dynamic feature selection based on Big Data fusion with multi-objective particle swarm optimization. Another example is proposed by Dong et al in [155], in which authors determine security threats in power grid by making full use of heterogeneous data sources in power big data.…”

Section: Data Fusion and Bio-inspired Computationmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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“…Where; p, q, 0 and 1 are a real value, with ( 0 < 1 ) ∈ ℝ and is detailed in equation ( 4). However, the multi-dimensional version is provided in the mathematical definition (5) presenting product of the one-dimensional in (3).…”

Section: Initialization Of the Archive Of The Non-dominated Solutions: The Archive (A) Ismentioning

confidence: 99%

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

Aboud¹,

Rokbani²,

Alimi³

et al. 2021

Preprint

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<p>Dynamic multi-objective optimization problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization filed which have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the Crow Search Algorithm has not yet considered for both DMOP and MaOP. This paper proposes a Distributed <a>Bi-behaviors </a>Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. The DB-CSA approach is developed to solve several types of DMOPs and a set of MaOPs with 2, 3, 5, 7, 8, 10 and 15 objectives. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics are used to compare the DB-CSA approach to the state-of-the-art MOEAs. All quantitative results are analyzed using the nonparametric Wilcoxon signed rank test with 0.05 significance level which proving the efficiency of the proposed method for solving both 44 DMOPs and MaOPs utilized. </p>

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MOPSO for dynamic feature selection problem based big data fusion

Cited by 15 publications

References 17 publications

DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

A Distributed Bi-behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization

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