Special Issue "Recent Advances in Swarm Intelligence Algorithms and Their Applications"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 20 August 2022 | Viewed by 2414

Special Issue Editor

Prof. Dr. Jian Dong
E-Mail Website
Guest Editor
School of Computer Science and Engineering, Central South University, Changsha 410075, China
Interests: swarm intelligence; antenna theory and design; microwave remote sensing; array signal processing

Special Issue Information

Dear Colleagues, 

As a branch of artificial intelligence, swarm intelligence refers to the collective behavior of decentralized, self-organized systems. Swarm intelligence is mainly to attract, gather, and manage large-scale participants interacting locally with one another and with their environment. It aims to jointly cope with challenging tasks by means of competition, cooperation, and other independent or collaborative ways, especially the complex system decision-making tasks in the open environment, which leads to the emergence of intelligent global behavior, unknown to individuals.

In recent years, the research community has witnessed an explosion of swarm intelligence algorithms efficiently solving complex computation tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains, such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others.

This Special Issue provides a platform for researchers from academia and industry to present their new and unpublished work and to promote future studies in swarm intelligence and its combination with real-world problems and other fields, including but not limited to antenna design, vehicle scheduling, drug design and discovery, image segmentation, feature selection, data clustering, traveling salesman problems, etc.

Prof. Dr. Jian Dong
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • swarm intelligence
  • evolutionary algorithms
  • optimization
  • metaheuristics
  • surrogate modeling
  • differential evolution
  • real-world applications
  • machine learning
  • optimal design
  • benchmark functions

Published Papers (3 papers)

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Research

Article
Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction
Mathematics 2022, 10(8), 1344; https://doi.org/10.3390/math10081344 - 18 Apr 2022
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Abstract
Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing [...] Read more.
Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to improve the accuracy of relation extraction. However, most existing relation extraction models ignore the semantic guidance of contextual information to entity mentions and treat entity mentions in and the textual context of a sentence equally. This results in low-accuracy relation extractions. To address this problem, we propose a contextual semantic-guided entity-centric graph convolutional network (CEGCN) model that enables entity mentions to obtain semantic-guided contextual information for more accurate relational representations. This model develops a self-attention enhanced neural network to concentrate on the importance and relevance of different words to obtain semantic-guided contextual information. Then, we employ a dependency tree with entities as global nodes and add virtual edges to construct an entity-centric logical adjacency matrix (ELAM). This matrix can enable entities to aggregate the semantic-guided contextual information with a one-layer GCN calculation. The experimental results on the TACRED and SemEval-2010 Task 8 datasets show that our model can efficiently use semantic-guided contextual information to enrich semantic entity representations and outperform previous models. Full article
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Article
Usage of Selected Swarm Intelligence Algorithms for Piecewise Linearization
Mathematics 2022, 10(5), 808; https://doi.org/10.3390/math10050808 - 03 Mar 2022
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Abstract
The paper introduces a new approach to enhance optimization algorithms when solving the piecewise linearization problem of a given function. Eight swarm intelligence algorithms were selected to be experimentally compared. The problem is represented by the calculation of the distance between the original [...] Read more.
The paper introduces a new approach to enhance optimization algorithms when solving the piecewise linearization problem of a given function. Eight swarm intelligence algorithms were selected to be experimentally compared. The problem is represented by the calculation of the distance between the original function and the estimation from the piecewise linear function. Here, the piecewise linearization of 2D functions is studied. Each of the employed swarm intelligence algorithms is enhanced by a newly proposed automatic detection of the number of piecewise linear parts that determine the discretization points to calculate the distance between the original and piecewise linear function. The original algorithms and their enhanced variants are compared on several examples of piecewise linearization problems. The results show that the enhanced approach performs sufficiently better when it creates a very promising approximation of functions. Moreover, the degree of precision is slightly decreased by the focus on the speed of the optimization process. Full article
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Article
Hybridisation of Swarm Intelligence Algorithms with Multi-Criteria Ordinal Classification: A Strategy to Address Many-Objective Optimisation
Mathematics 2022, 10(3), 322; https://doi.org/10.3390/math10030322 - 20 Jan 2022
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Abstract
This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged [...] Read more.
This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Outage Performance Optimization for NOMA Cognitive Relay Network with RF Energy Harvesting Based on Improved Bat Algorithm
Authors: Yi Luo
Affiliation: School of Automation, Central South University, Changsha 410083, China

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