Swarm Intelligence and Evolutionary Algorithms for Real World Applications (3rd Edition)

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 823

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School of Computing & Mathematical Sciences, Faculty of Engineering and Science, University of Greenwich, London SE10 9LS, UK
Interests: swarm intelligence; evolutionary computation; tomographic reconstruction; computational creativity
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Special Issue Information

Dear Colleagues,

Swarm intelligence (SI) and evolutionary computation (EC) techniques have been thriving research topics, especially in areas where conventional methods fail to deal with the size and nature of the problem space.

The self-organising nature of swarm intelligence and evolutionary computation in both natural and computational models is key to the attractiveness of such techniques; they not only explain and reflect natural and social phenomena but also solve complex problems in many disciplines.

Additionally, noisy environments and/or incomplete data are often at the heart of real-world data, where search- and optimisation-related problems are among the core issues. Ever since the inception of SI and EC techniques, researchers have been drawn to the complex emergent behaviour, robustness, and easy-to-understand architecture of nature-inspired swarm intelligence and evolutionary algorithms. In challenging search environments, these methods have often proved more useful than conventional approaches.

This Special Issue will facilitate the discussion of emerging topics in this context and encourage PhD students, early-career researchers, and senior academics to engage in a dialogue surrounding the real-world applications of swarm intelligence and evolutionary computation techniques.

Dr. Mohammad Majid al-Rifaie
Guest Editor

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Keywords

  • swarm intelligence
  • evolutionary computation
  • large-scale optimisation
  • multi-objective optimisation
  • complex systems
  • hybridisation
  • premature convergence
  • stagnation
  • particle swarm optimisation
  • differential evolutionary
  • genetic algorithms
  • dispersive flies optimisation
  • ant colony optimisation

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Published Papers (2 papers)

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26 pages, 2255 KB  
Article
Distribution Network Planning Considering Harmonics Based on a Parallel Genetic Algorithm Using Message Passing Interface
by Vincent Roberge and Mohammed Tarbouchi
Algorithms 2026, 19(5), 365; https://doi.org/10.3390/a19050365 - 5 May 2026
Abstract
This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can [...] Read more.
This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster. Full article
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30 pages, 2535 KB  
Article
Optimizing the Permutation Flowshop Scheduling Problem with an Improved Sparrow Search Algorithm
by Maria Tsiftsoglou, Yannis Marinakis and Magdalene Marinaki
Algorithms 2026, 19(4), 283; https://doi.org/10.3390/a19040283 - 6 Apr 2026
Viewed by 396
Abstract
The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely [...] Read more.
The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely recognized Permutation Flowshop Scheduling Problem (PFSP) with the makespan criterion as the optimization target. Our study aims to assess the effectiveness and robustness of this cutting-edge metaheuristic through computational experiments and statistical analysis. The proposed SSA is a hybrid variant that incorporates the Variable Neighborhood Search (VNS) algorithm along with a Path Relinking Strategy. The effectiveness of the proposed method is evaluated through computational experiments on PFSP benchmark instances. The performance of the hybrid SSA is compared against several well-established swarm-intelligence metaheuristics, namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Tuna Swarm Optimization Algorithm (TSO), Particle Swarm Optimization Algorithm (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and the Artificial Bee Colony (ABC). To ensure fair comparison, all methods are implemented within the same computational framework as the hybrid SSA. The experimental results show that the proposed hybrid SSA achieves the lowest average mean error compared with the competing methods in solving the PFSP. The results were further validated through a comprehensive non-parametric statistical analysis using Friedman, Aligned Friedman, and Quade tests, followed by post-hoc analysis with p-adjusted values, as well as Kruskal–Wallis and Wilcoxon post-hoc tests. Full article
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