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Entropy
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Swarm Models: From Biological and Social to Artificial Systems
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Swarm Models: From Biological and Social to Artificial Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: closed (20 September 2021) | Viewed by 18548

Special Issue Editors

Dr. Giulia De Masi

E-Mail Website
Guest Editor
Technology Innovation Institute, Zayed University, 4783 Dubai, UAE
Interests: complex systems; statistical physics; nonlinear dynamics
Dr. Eliseo Ferrante

E-Mail Website
Guest Editor
Vrije Universiteit Amsterdam, De Boelelaan 1081, 1081 HV, Amsterdam, the Netherlands
Interests: swarm robotics; complex systems; artificial intelligence; machine learning; evolutionary computation

Special Issue Information

In nature, many animals show collective behavior despite the absence of a central authority; insects (for example bees, ants, cockroaches), birds, fish, as well as groups of people show emerging collective choices as a result of their social interactions. A global decision is taken in response to a change or stimulus from the environment, occurring only through local communication between a few individuals and resulting in the appearance of coordinated and self-organized behavior. This behavior has been optimized by evolution in order to cater more efficiently to the special needs of the species (like foraging) or to react faster to sudden dangers. In robotics, a new field termed swarm robotics, that takes inspiration from these examples from biology and social sciences, has been used to design the collective decision making of robots in such applications where the use of a large number of robots is beneficial. Some examples are in the environmental monitoring of large areas, rescue missions in remote environments, and smart agriculture. In comparison to use of a single robot, the use of a swarm of robots guarantees flexibility, resiliency, and scalability.

This Special Issue aims to join together the different communities looking at collective behavior from their different perspectives, examining the spectrum from natural to artificial systems. We are looking for contributions on collective behavior from the point of view of statistical physics to study the dynamics of natural and social systems and/or to optimize the design of those that are artificial. Possible topics of interest include collective decision making in biological, social, and robotic systems; swarm robotics; self-organization; study of simple to complex interaction network topologies in swarms; application of information theory; entropy and other complexity metrics to study information transfer in swarms; and evolution of communication and coordination in self-organized swarms of agents or robots.

Dr. Giulia De Masi
Dr. Eliseo Ferrante
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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 models
  • collective decision making
  • collective behavior
  • flocking
  • biological inspired artificial swarms
  • complex networks
  • self-organization
  • information theory
  • entropy

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Published Papers (6 papers)

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Research

15 pages, 2069 KiB  
Article
Improved Particle Swarm Optimization Based on Entropy and Its Application in Implicit Generalized Predictive Control
by Jinfang Zhang, Yuzhuo Zhai, Zhongya Han and Jiahui Lu
Entropy 2022, 24(1), 48; https://doi.org/10.3390/e24010048 - 27 Dec 2021
Cited by 8 | Viewed by 2967
Abstract
Setting sights on the problem of input-output constraints in most industrial systems, an implicit generalized predictive control algorithm based on an improved particle swarm optimization algorithm (PSO) is presented in this paper. PSO has the advantages of high precision and fast convergence speed [...] Read more.
Setting sights on the problem of input-output constraints in most industrial systems, an implicit generalized predictive control algorithm based on an improved particle swarm optimization algorithm (PSO) is presented in this paper. PSO has the advantages of high precision and fast convergence speed in solving constraint problems. In order to effectively avoid the problems of premature and slow operation in the later stage, combined with the idea of the entropy of system (SR), a new weight attenuation strategy and local jump out optimization strategy are introduced into PSO. The velocity update mechanism is cancelled, and the algorithm is adjusted respectively in the iterative process and after falling into local optimization. The improved PSO is used to optimize the performance index in predictive control. The combination of PSO and gradient optimization for rolling-horizon improves the optimization effect of the algorithm. The simulation results show that the system overshoot is reduced by about 7.5% and the settling time is reduced by about 6% compared with the implicit generalized predictive control algorithm based on particle swarm optimization algorithm (PSO-IGPC). Full article
(This article belongs to the Special Issue Swarm Models: From Biological and Social to Artificial Systems)
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20 pages, 5235 KiB  
Article
A Particle Swarm Algorithm Based on a Multi-Stage Search Strategy
by Yong Shen, Wangzhen Cai, Hongwei Kang, Xingping Sun, Qingyi Chen and Haigang Zhang
Entropy 2021, 23(9), 1200; https://doi.org/10.3390/e23091200 - 11 Sep 2021
Cited by 10 | Viewed by 2191
Abstract
Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores of approaches have been used to improve the diversity, search accuracy, and results of PSO, but the balance between exploration and exploitation remains [...] Read more.
Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores of approaches have been used to improve the diversity, search accuracy, and results of PSO, but the balance between exploration and exploitation remains sub-optimal. Many scholars have divided the population into multiple sub-populations with the aim of managing it in space. In this paper, a multi-stage search strategy that is dominated by mutual repulsion among particles and supplemented by attraction was proposed to control the traits of the population. From the angle of iteration time, the algorithm was able to adequately enhance the entropy of the population under the premise of satisfying the convergence, creating a more balanced search process. The study acquired satisfactory results from the CEC2017 test function by improving the standard PSO and improved PSO. Full article
(This article belongs to the Special Issue Swarm Models: From Biological and Social to Artificial Systems)
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38 pages, 1116 KiB  
Article
Exploration and Exploitation Zones in a Minimalist Swarm Optimiser
by Mohammad Majid al-Rifaie
Entropy 2021, 23(8), 977; https://doi.org/10.3390/e23080977 - 29 Jul 2021
Cited by 2 | Viewed by 2338
Abstract
The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer [...] Read more.
The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the population’s dimensional behaviour in each iteration and each defined exploration-exploitation zone, and to subsequently offer improvements to the working of the optimiser. The derived variants, titled unified DFO or uDFO, are successfully applied to an extensive set of test functions, as well as high-dimensional tomographic reconstruction, which is an important inverse problem in medical and industrial imaging. Full article
(This article belongs to the Special Issue Swarm Models: From Biological and Social to Artificial Systems)
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24 pages, 13411 KiB  
Article
A Hybridization of Dragonfly Algorithm Optimization and Angle Modulation Mechanism for 0-1 Knapsack Problems
by Lin Wang, Ronghua Shi and Jian Dong
Entropy 2021, 23(5), 598; https://doi.org/10.3390/e23050598 - 12 May 2021
Cited by 20 | Viewed by 3091
Abstract
The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous functions and engineering problems. To make this algorithm work in the binary space, this paper introduces [...] Read more.
The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous functions and engineering problems. To make this algorithm work in the binary space, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems, and uses DA to optimize the coefficients of the trigonometric function. Further, to improve the algorithm stability and convergence speed, an improved AMDA, called IAMDA, is proposed by adding one more coefficient to adjust the vertical displacement of the cosine part of the original generating function. To test the performance of IAMDA and AMDA, 12 zero-one knapsack problems are considered along with 13 classic benchmark functions. Experimental results prove that IAMDA has a superior convergence speed and solution quality as compared to other algorithms. Full article
(This article belongs to the Special Issue Swarm Models: From Biological and Social to Artificial Systems)
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18 pages, 1321 KiB  
Article
Co-Evolution of Predator-Prey Ecosystems by Reinforcement Learning Agents
by Jeongho Park, Juwon Lee, Taehwan Kim, Inkyung Ahn and Jooyoung Park
Entropy 2021, 23(4), 461; https://doi.org/10.3390/e23040461 - 13 Apr 2021
Cited by 7 | Viewed by 4678
Abstract
The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their complex interactions, the study of population dynamics still [...] Read more.
The problem of finding adequate population models in ecology is important for understanding essential aspects of their dynamic nature. Since analyzing and accurately predicting the intelligent adaptation of multiple species is difficult due to their complex interactions, the study of population dynamics still remains a challenging task in computational biology. In this paper, we use a modern deep reinforcement learning (RL) approach to explore a new avenue for understanding predator-prey ecosystems. Recently, reinforcement learning methods have achieved impressive results in areas, such as games and robotics. RL agents generally focus on building strategies for taking actions in an environment in order to maximize their expected returns. Here we frame the co-evolution of predators and preys in an ecosystem as allowing agents to learn and evolve toward better ones in a manner appropriate for multi-agent reinforcement learning. Recent significant advancements in reinforcement learning allow for new perspectives on these types of ecological issues. Our simulation results show that throughout the scenarios with RL agents, predators can achieve a reasonable level of sustainability, along with their preys. Full article
(This article belongs to the Special Issue Swarm Models: From Biological and Social to Artificial Systems)
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19 pages, 533 KiB  
Article
A Graph-Transformational Approach to Swarm Computation
by Larbi Abdenebaoui, Hans-Jörg Kreowski and Sabine Kuske
Entropy 2021, 23(4), 453; https://doi.org/10.3390/e23040453 - 12 Apr 2021
Cited by 1 | Viewed by 2107
Abstract
In this paper, we propose a graph-transformational approach to swarm computation that is flexible enough to cover various existing notions of swarms and swarm computation, and it provides a mathematical basis for the analysis of swarms with respect to their correct behavior and [...] Read more.
In this paper, we propose a graph-transformational approach to swarm computation that is flexible enough to cover various existing notions of swarms and swarm computation, and it provides a mathematical basis for the analysis of swarms with respect to their correct behavior and efficiency. A graph transformational swarm consists of members of some kinds. They are modeled by graph transformation units providing rules and control conditions to specify the capability of members and kinds. The swarm members act on an environment—represented by a graph—by applying their rules in parallel. Moreover, a swarm has a cooperation condition to coordinate the simultaneous actions of the swarm members and two graph class expressions to specify the initial environments on one hand and to fix the goal on the other hand. Semantically, a swarm runs from an initial environment to one that fulfills the goal by a sequence of simultaneous actions of all its members. As main results, we show that cellular automata and particle swarms can be simulated by graph-transformational swarms. Moreover, we give an illustrative example of a simple ant colony the ants of which forage for food choosing their tracks randomly based on pheromone trails. Full article
(This article belongs to the Special Issue Swarm Models: From Biological and Social to Artificial Systems)
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