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Information Theory and Swarm Optimization in Decision and Control

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A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (19 July 2023) | Viewed by 8210

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Special Issue Editors

Prof. Dr. Ben Niu

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Guest Editor

College of Management, Shenzhen University, Shenzhen 518061, China
Interests: swarm intelligence; bionic management; information intelligence; optimal decision-making; deep mining

Dr. Shuang Geng

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Guest Editor

College of Management, Shenzhen University, Shenzhen 518061, China
Interests: information intelligence; optimal decision-making; recommender system; learning analytics; data mining

Dr. Rong Qu

E-Mail Website
Guest Editor

School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
Interests: hyper-heuristics; evolutionary algorithms; meta-heuristic; intelligent computing

Special Issue Information

Dear Colleagues,

With the evolvement of our natural and social environment, decision making and control for industrial and engineering systems are becoming increasing complex. While the incorporation of multi-source and multi-modal information can benefit the selection of optimal solutions, its efficiency is often hindered by the computational complexity and unguided solution searching process. Noise in the data and the existence of multiple sub-optimal areas in the solution space may pose greater challenges to the problem.

Swarm optimization algorithms are inspired by the motion of intelligent colonies in nature, which utilize information by continuously evaluating the environment to guide efficient searching processes for optimal locations. These streams of algorithms have the features of parallel computing, self-organization, and adaptability.

The application of swarm optimization for decision and control of novel complex systems—for instance, computer engineering, dynamic scheduling, bioinformatics, data mining, and design optimization—is an emerging trend that calls for more theoretical, methodological, and applicational research attention. Contributions addressing any of these issues are very welcome.

This Special Issue aims to serve as a forum for the presentation of new and improved techniques of multi-source and multi-modal information processing and swarm optimization for decision and control processes. In particular, the analysis and interpretation of such approaches in real-world natural and engineered environments falls within the scope of this Special Issue.

Prof. Dr. Ben Niu
Dr. Shuang Geng
Dr. Rong Qu
Guest Editors

Manuscript Submission Information

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Keywords

information science
swarm optimization
decision making
control process
data analysis
complex systems
algorithms
applications

Published Papers (5 papers)

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Research

23 pages, 693 KiB

Open AccessArticle

A Hybrid Particle Swarm Optimization Algorithm with Dynamic Adjustment of Inertia Weight Based on a New Feature Selection Method to Optimize SVM Parameters

by Jing Wang, Xingyi Wang, Xiongfei Li and Jiacong Yi

Entropy 2023, 25(3), 531; https://doi.org/10.3390/e25030531 - 19 Mar 2023

Cited by 14 | Viewed by 1968

Abstract

Support vector machine (SVM) is a widely used and effective classifier. Its efficiency and accuracy mainly depend on the exceptional feature subset and optimal parameters. In this paper, a new feature selection method and an improved particle swarm optimization algorithm are proposed to improve the efficiency and the classification accuracy of the SVM. The new feature selection method, named Feature Selection-score (FS-score), performs well on data sets. If a feature makes the class external sparse and the class internal compact, its FS-score value will be larger and the probability of being selected will be greater. An improved particle swarm optimization model with dynamic adjustment of inertia weight (DWPSO-SVM) is also proposed to optimize the parameters of the SVM. By improving the calculation method of the inertia weight of the particle swarm optimization (PSO), inertia weight can decrease nonlinearly with the number of iterations increasing. In particular, the introduction of random function brings the inertia weight diversity in the later stage of the algorithm and the global searching ability of the algorithm to avoid falling into local extremum. The experiment is performed on the standard UCI data sets whose features are selected by the FS-score method. Experiments demonstrate that our algorithm achieves better classification performance compared with other state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Information Theory and Swarm Optimization in Decision and Control)

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16 pages, 509 KiB

Open AccessArticle

Diversity-Aware Marine Predators Algorithm for Task Scheduling in Cloud Computing

by Dujing Chen and Yanyan Zhang

Entropy 2023, 25(2), 285; https://doi.org/10.3390/e25020285 - 02 Feb 2023

Cited by 4 | Viewed by 1292

Abstract

With the increase in cloud users and internet of things (IoT) applications, advanced task scheduling (TS) methods are required to reasonably schedule tasks in cloud computing. This study proposes a diversity-aware marine predators algorithm (DAMPA) for solving TS in cloud computing. In DAMPA, to enhance the premature convergence avoidance ability, the predator crowding degree ranking and comprehensive learning strategies were adopted in the second stage to maintain the population diversity and thereby inhibit premature convergence. Additionally, a stage-independent control of the stepsize-scaling strategy that uses different control parameters in three stages was designed to balance the exploration and exploitation abilities. Two case experiments were conducted to evaluate the proposed algorithm. Compared with the latest algorithm, in the first case, DAMPA reduced the makespan and energy consumption by 21.06% and 23.47% at most, respectively. In the second case, the makespan and energy consumption are reduced by 34.35% and 38.60% on average, respectively. Meanwhile, the algorithm achieved greater throughput in both cases. Full article

(This article belongs to the Special Issue Information Theory and Swarm Optimization in Decision and Control)

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17 pages, 2743 KiB

Open AccessArticle

Optimal Security Protection Strategy Selection Model Based on Q-Learning Particle Swarm Optimization

by Xin Gao, Yang Zhou, Lijuan Xu and Dawei Zhao

Entropy 2022, 24(12), 1727; https://doi.org/10.3390/e24121727 - 25 Nov 2022

Viewed by 1182

Abstract

With the rapid development of Industrial Internet of Things technology, the industrial control system (ICS) faces more and more security threats, which may lead to serious risks and extensive damage. Naturally, it is particularly important to construct efficient, robust, and low-cost protection strategies for ICS. However, how to construct an objective function of optimal security protection strategy considering both the security risk and protection cost, and to find the optimal solution, are all significant challenges. In this paper, we propose an optimal security protection strategy selection model and develop an optimization framework based on Q-Learning particle swarm optimization (QLPSO). The model performs security risk assessment of ICS by introducing the protection strategy into the Bayesian attack graph. The QLPSO adopts the Q-Learning to improve the local optimum, insufficient diversity, and low precision of the PSO algorithm. Simulations are performed on a water distribution ICS, and the results verify the validity and feasibility of our proposed model and the QLPSO algorithm. Full article

(This article belongs to the Special Issue Information Theory and Swarm Optimization in Decision and Control)

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22 pages, 1289 KiB

Open AccessArticle

An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection

by Lewang Zou, Shihua Zhou and Xiangjun Li

Entropy 2022, 24(8), 1065; https://doi.org/10.3390/e24081065 - 02 Aug 2022

Cited by 5 | Viewed by 1410

Abstract

To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems. Full article

(This article belongs to the Special Issue Information Theory and Swarm Optimization in Decision and Control)

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18 pages, 4338 KiB

Open AccessArticle

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

by Ye’e Zhang and Xiaoxia Song

Entropy 2022, 24(7), 890; https://doi.org/10.3390/e24070890 - 28 Jun 2022

Cited by 2 | Viewed by 1376

Abstract

In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-population parallel strategy is designed to improve population diversity and accelerate convergence. The population particle exchange and mutation are realized to ensure information sharing among the particles. Then, the global optimal value is added to velocity update to design a new velocity update strategy for improving the local search ability. The comprehensive learning strategy is employed to construct learning samples, so as to effectively promote the information exchange and avoid falling into local extrema. By linearly changing the learning factors, a new factor adjustment strategy is developed to enhance the global search ability, and a new adaptive inertia weight-adjustment strategy based on an S-shaped decreasing function is developed to balance the search ability. Finally, some benchmark functions and the parameter optimization of photovoltaics are selected. The proposed algorithm obtains the best performance on 6 out of 10 functions. The results show that the proposed algorithm has greatly improved diversity, solution accuracy, and search ability compared with some variants of particle swarm optimization and other algorithms. It provides a more effective parameter combination for the complex engineering problem of photovoltaics, so as to improve the energy conversion efficiency. Full article

(This article belongs to the Special Issue Information Theory and Swarm Optimization in Decision and Control)

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