Special Issue "Swarm Intelligence Applications for NP Hard Challenges"

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 (15 June 2020).

Special Issue Editors

Dr. Nebojsa Bacanin
Website SciProfiles
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Interests: Artificial Intelligence; Swarm Intelligence; Digital Image Processing; Optimization Metaheuristics; Deep Learning
Dr. Eva Tuba
Website
Guest Editor
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
Interests: Swarm Intelligence; Digital Image Processing; Machine Learning
Dr. Milan Tuba
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
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 papers will be 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 1000 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 (3 papers)

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Research

Open AccessArticle
Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems
Algorithms 2020, 13(6), 142; https://doi.org/10.3390/a13060142 - 12 Jun 2020
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 for NP Hard Challenges)
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Open AccessArticle
Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics
Algorithms 2020, 13(3), 67; https://doi.org/10.3390/a13030067 - 17 Mar 2020
Cited by 2
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 for NP Hard Challenges)
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Open AccessArticle
Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem
Algorithms 2020, 13(2), 44; https://doi.org/10.3390/a13020044 - 20 Feb 2020
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 for NP Hard Challenges)
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