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Algorithms
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Swarm Intelligence Applications and Algorithms
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Swarm Intelligence Applications and Algorithms

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (1 May 2023) | Viewed by 24939

Special Issue Editors

Dr. Nebojsa Bacanin

E-Mail Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11010 Belgrade, Serbia
Interests: swarm intelligence; optimization metaheuristics; machine learning; deep learning; computer vision; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Eva Tuba

E-Mail Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Interests: Swarm Intelligence; Digital Image Processing; Machine Learning
Dr. Milan Tuba

E-Mail Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Interests: Artificial Intelligence; Swarm Intelligence; Digital Image Processing; Optimization Metaheuristics; Computer Networks
Dr. Ivana Strumberger

E-Mail Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Interests: artificial intelligence; swarm intelligence; optimization metaheuristics; computer networks; wireless sensor networks

Special Issue Information

Dear Colleagues,

Hard optimization problems that cannot be solved within acceptable computational time by deterministic mathematical methods have been successfully solved in recent years by population-based stochastic metaheuristics, among which swarm intelligence algorithms represent a prominent class. Swarm intelligence algorithms, along with evolutionary computation (EC) approaches, belong to a wider group of nature-inspired metaheuristics. Each nature-inspired method simulates some kind of natural phenomenon. For example, EC algorithms simulate the process of biological evolution, and one of the most significant EC algorithms, the genetic algorithm (GA), incorporates selection, crossover and mutation natural operators in its search process.

Swarm intelligence algorithms simulate organized and coordinated behaviour of groups of organisms, such as flock of birds, school of fish, colonies of ants, groups of bats, or herds of elephants. Despite the fact that the swarm consists of relatively unsophisticated individuals, swarm as a group exhibits intelligent behaviour by establishing direct and indirect forms of communication without the central component. In the literature, this characteristic is known as self-organization, and four basic principles of self-organization are positive and negative feed-back, multiple interactions and randomness.

Swarm intelligence metaheuristics conduct the search process by performing exploitation (intensification) and exploration (diversification). Exploitation and exploration meachnisms conduct local and global search, respectively. One of the most important challenges in the domain of swarm algorithms is to establish a proper balance adjustments (trade-off) between these two processes and many existing swarm approaches suffer from the inappropriate balance. If this balance is directed towards exploitation, the algorithm may suffer from the premature convergence and the optimal (or suboptimal) region of the search space may not be found. In the opposite case, the algorithm may find the optimal region, but could not perform a fine-tuned search around the promising solutions, which, as a consequence, may lead to a worse convergence.

In recent years, many hybrid swarm approaches combining the best features of two or more algorithms have been devised. For example, a hybrid algorithm may use an intensification equation from the first metaheuristics, and the diversification mechanism from the second approach. A number of hybridized swarm intelligence metaheuristics have been developed and implemented. Considering the fact that good hybrids are not created as a random combination of individual functional elements and procedures from diferent algorithms, but rather established on comprehensive analysis of the functional principles of the algorithms that are used in the process of hybridization, development of the hybrid approaches was preceded by thorough research of the advantages and disadvantages of each algorithm involved in order to determine the best combination that neutralizes disadvantages of one approach by incorporating the strengths of the other. However, when devising hybrid algorithms, according to the no free lunch theorem (NFLT), there always must be some kind of compromise and the researcher should be aware of this fact.

Some of the most prominent examples of state-of-the-art swarm algorithms include: particle swarm optimization (PSO), ant colony optimization (ACO), firefly algorithms (FA), bat algorithm (BA), artificial bee colony (ABC), fireworks algorithm (FWA), bacterial foraging optimization (BFO), elephant herding optimization (EHO), whale optimization algorithm (WAO), monarch butterfly optimization (MBO), brain storm optimization (BSO), etc. All these approaches in the original, modified/upgraded and hybridized versions have shown great potential when tackling many different types of NP real-world challenges.

Since the swarm approaches have proven to be robust optimizers of NP hard tasks, there is a logical assumption that many real-world NP hard challenges can be solved by using swarm intelligence algorithms in original, modified/upgraded and hybridized implementations. The most important goal of this Special Issue is to gather such research contributions.

However, despite the basic topic of this Special Issue, authors are also encouraged to submit manuscripts with theoretical discussion about the performance and behaviour of swarm approaches, as well as to present their EC applications to NP hard challenges.

Both original contributions and review articles will be considered, and we invite authors to submit their formal and technically sound manuscripts to cover (but not limited to) the following topics:

  • Swarm Intelligence Applications in Engineering
  • Swarm Intelligence Applications in Finance and Economics
  • Swarm Intelligence Applications in  Deep Learning 
  • Swarm Intelligence Applications in Wireless Sensor Networks (WSNs)
  • Swarm Intelligence Applications in Cloud Computing
  • Swarm Intelligence Applications in Internet of Things (IoT)
  • Swarm Intelligence Applications in Smart Cities
  • Swarm Intelligence Applications in Computer Vision and Image Processing
  • Swarm Intelligence Applications in Crowdsourcing
  • Swarm Intelligence Applications in Aerospace Science
  • Swarm Intelligence Applications to Automatic Data Clustering and Analysis
  • Swarm Intelligence Applications in Smart Logistics
  • Swarm Intelligence Applications in Cybersecurity
  • Hybrid Swarm Intelligence Algorithms
  • Memtic Swarm Algorithms
  • Parallel Swarm Algorithms
  • Distributed Swarm Algorithms
  • Swarm Intelligence Performance and Behaviour Analysis of Swarm algorithms
  • Lage-scale Global Optimization
  • Combinatorial Optimization
  • Multi-objective Optimization

Dr. Nebojsa Bacanin
Dr. Eva Tuba
Dr. Milan Tuba
Dr. Ivana Strumberger
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. Algorithms 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 1600 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 metaheurisitcs
  • Nature-inspired alogritthms
  • Stohastic optimization
  • EC algorithms
  • Real-world NP hard problems
  • Hybrid algorithms
  • Memetic algorithms
  • Parallel algorithms
  • Global optimization
  • Combinatorial optimization

Published Papers (6 papers)

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Research

21 pages, 3792 KiB  
Article
Adjustable Pheromone Reinforcement Strategies for Problems with Efficient Heuristic Information
by Nikola Ivković, Robert Kudelić and Marin Golub
Algorithms 2023, 16(5), 251; https://doi.org/10.3390/a16050251 - 12 May 2023
Cited by 2 | Viewed by 1182
Abstract
Ant colony optimization (ACO) is a well-known class of swarm intelligence algorithms suitable for solving many NP-hard problems. An important component of such algorithms is a record of pheromone trails that reflect colonies’ experiences with previously constructed solutions of the problem instance that [...] Read more.
Ant colony optimization (ACO) is a well-known class of swarm intelligence algorithms suitable for solving many NP-hard problems. An important component of such algorithms is a record of pheromone trails that reflect colonies’ experiences with previously constructed solutions of the problem instance that is being solved. By using pheromones, the algorithm builds a probabilistic model that is exploited for constructing new and, hopefully, better solutions. Traditionally, there are two different strategies for updating pheromone trails. The best-so-far strategy (global best) is rather greedy and can cause a too-fast convergence of the algorithm toward some suboptimal solutions. The other strategy is named iteration best and it promotes exploration and slower convergence, which is sometimes too slow and lacks focus. To allow better adaptability of ant colony optimization algorithms we use κ-best, max-κ-best, and 1/λ-best strategies that form the entire spectrum of strategies between best-so-far and iteration best and go beyond. Selecting a suitable strategy depends on the type of problem, parameters, heuristic information, and conditions in which the ACO is used. In this research, we use two representative combinatorial NP-hard problems, the symmetric traveling salesman problem (TSP) and the asymmetric traveling salesman problem (ATSP), for which very effective heuristic information is widely known, to empirically analyze the influence of strategies on the algorithmic performance. The experiments are carried out on 45 TSP and 47 ATSP instances by using the MAX-MIN ant system variant of ACO with and without local optimizations, with each problem instance repeated 101 times for 24 different pheromone reinforcement strategies. The results show that, by using adjustable pheromone reinforcement strategies, the MMAS outperformed in a large majority of cases the MMAS with classical strategies. Full article
(This article belongs to the Special Issue Swarm Intelligence Applications and Algorithms)
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18 pages, 597 KiB  
Article
Locating the Parameters of RBF Networks Using a Hybrid Particle Swarm Optimization Method
by Ioannis G. Tsoulos and Vasileios Charilogis
Algorithms 2023, 16(2), 71; https://doi.org/10.3390/a16020071 - 21 Jan 2023
Cited by 5 | Viewed by 1557
Abstract
In the present work, an innovative two-phase method is presented for parameter tuning in radial basis function artificial neural networks. These kinds of machine learning models find application in many scientific fields in classification problems or in function regression. In the first phase, [...] Read more.
In the present work, an innovative two-phase method is presented for parameter tuning in radial basis function artificial neural networks. These kinds of machine learning models find application in many scientific fields in classification problems or in function regression. In the first phase, a technique based on particle swarm optimization is performed to locate a promising interval of values for the network parameters. Particle swarm optimization was used as it is a highly reliable method for global optimization problems, and in addition, it is one of the fastest and most-flexible techniques of its class. In the second phase, the network was trained within the optimal interval using a global optimization technique such as a genetic algorithm. Furthermore, in order to speed up the training of the network and due to the use of a two-stage method, parallel programming techniques were utilized. The new method was applied to a number of famous classification and regression datasets, and the results were more than promising. Full article
(This article belongs to the Special Issue Swarm Intelligence Applications and Algorithms)
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14 pages, 1781 KiB  
Article
Self-Learning Salp Swarm Optimization Based PID Design of Doha RO Plant
by Naresh Patnana, Swapnajit Pattnaik, Tarun Varshney and Vinay Pratap Singh
Algorithms 2020, 13(11), 287; https://doi.org/10.3390/a13110287 - 10 Nov 2020
Cited by 5 | Viewed by 2577
Abstract
In this investigation, self-learning salp swarm optimization (SLSSO) based proportional- integral-derivative (PID) controllers are proposed for a Doha reverse osmosis desalination plant. Since the Doha reverse osmosis plant (DROP) is interacting with a two-input-two-output (TITO) system, a decoupler is designed to nullify the [...] Read more.
In this investigation, self-learning salp swarm optimization (SLSSO) based proportional- integral-derivative (PID) controllers are proposed for a Doha reverse osmosis desalination plant. Since the Doha reverse osmosis plant (DROP) is interacting with a two-input-two-output (TITO) system, a decoupler is designed to nullify the interaction dynamics. Once the decoupler is designed properly, two PID controllers are tuned for two non-interacting loops by minimizing the integral-square-error (ISE). The ISEs for two loops are obtained in terms of alpha and beta parameters to simplify the simulation. Thus designed ISEs are minimized using SLSSO algorithm. In order to show the effectiveness of the proposed algorithm, the controller tuning is also accomplished using some state-of-the-art algorithms. Further, statistical analysis is presented to prove the effectiveness of SLSSO. In addition, the time domain specifications are presented for different test cases. The step responses are also shown for fixed and variable reference inputs for two loops. The quantitative and qualitative results presented show the effectiveness of SLSSO for the DROP system. Full article
(This article belongs to the Special Issue Swarm Intelligence Applications and Algorithms)
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18 pages, 669 KiB  
Article
Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems
by Piotr Jedrzejowicz and Izabela Wierzbowska
Algorithms 2020, 13(6), 142; https://doi.org/10.3390/a13060142 - 12 Jun 2020
Cited by 6 | Viewed by 3146
Abstract
One of the possible approaches to solving difficult optimization problems is applying population-based metaheuristics. Among such metaheuristics, there is a special class where searching for the best solution is based on the collective behavior of decentralized, self-organized agents. This study proposes an approach [...] Read more.
One of the possible approaches to solving difficult optimization problems is applying population-based metaheuristics. Among such metaheuristics, there is a special class where searching for the best solution is based on the collective behavior of decentralized, self-organized agents. This study proposes an approach in which a swarm of agents tries to improve solutions from the population of solutions. The process is carried out in parallel threads. The proposed algorithm—based on the mushroom-picking metaphor—was implemented using Scala in an Apache Spark environment. An extended computational experiment shows how introducing a combination of simple optimization agents and increasing the number of threads may improve the results obtained by the model in the case of TSP and JSSP problems. Full article
(This article belongs to the Special Issue Swarm Intelligence Applications and Algorithms)
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33 pages, 977 KiB  
Article
Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics
by Nebojsa Bacanin, Timea Bezdan, Eva Tuba, Ivana Strumberger and Milan Tuba
Algorithms 2020, 13(3), 67; https://doi.org/10.3390/a13030067 - 17 Mar 2020
Cited by 87 | Viewed by 11027
Abstract
Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural [...] Read more.
Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming process and requires expertise. To approach this concern, we propose an automatic method for hyperparameters’ optimization and structure design by implementing enhanced metaheuristic algorithms. The aim of this paper is twofold. First, we propose enhanced versions of the tree growth and firefly algorithms that improve the original implementations. Second, we adopt the proposed enhanced algorithms for hyperparameters’ optimization. First, the modified metaheuristics are evaluated on standard unconstrained benchmark functions and compared to the original algorithms. Afterward, the improved algorithms are employed for the network design. The experiments are carried out on the famous image classification benchmark dataset, the MNIST dataset, and comparative analysis with other outstanding approaches that were tested on the same problem is conducted. The experimental results show that both proposed improved methods establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources. Full article
(This article belongs to the Special Issue Swarm Intelligence Applications and Algorithms)
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16 pages, 1700 KiB  
Article
Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem
by Hongchan Li, Haodong Zhu and Tianhua Jiang
Algorithms 2020, 13(2), 44; https://doi.org/10.3390/a13020044 - 20 Feb 2020
Cited by 17 | Viewed by 3774
Abstract
In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware [...] Read more.
In recent decades, workshop scheduling has excessively focused on time-related indicators, while ignoring environmental metrics. With the advent of sustainable manufacturing, the energy-aware scheduling problem has been attracting more and more attention from scholars and researchers. In this study, we investigate an energy-aware flexible job shop scheduling problem to reduce the total energy consumption in the workshop. For the considered problem, the energy consumption model is first built to formulate the energy consumption, such as processing energy consumption, idle energy consumption, setup energy consumption and common energy consumption. Then, a mathematical model is established with the criterion to minimize the total energy consumption. Secondly, a modified migrating birds optimization (MMBO) algorithm is proposed to solve the model. In the proposed MMBO, a population initialization scheme is presented to ensure the initial solutions with a certain quality and diversity. Five neighborhood structures are employed to create neighborhood solutions according to the characteristics of the problem. Furthermore, both a local search method and an aging-based re-initialization mechanism are developed to avoid premature convergence. Finally, the experimental results validate that the proposed algorithm is effective for the problem under study. Full article
(This article belongs to the Special Issue Swarm Intelligence Applications and Algorithms)
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