Evolutionary Computation and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 2226

Special Issue Editor


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Guest Editor
Centro de Tecnologías de la Imagen (CTIM), University of Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain
Interests: computer vision; machine learning; optimization

Special Issue Information

Dear Colleagues,

Evolutionary computing has become a focus of attention during the last decades. Many interesting biologically inspired methods have been proposed to solve a wide variety of complex optimization problems. This rapidly evolving field has made tackling new applications possible in different areas of science, engineering, finance, or economics.

Optimization is present in many real-life problems. Thanks to the capabilities of modern computer systems, researchers face new challenges that require innovative technologies that go beyond the current state-of-the-art methods. A large number of methods, ranging from genetic and evolutionary algorithms to bio-inspired and multi-objective optimization, swarm intelligence, or metaheuristics, permit the solution of many types of problems in multiple areas. It is necessary to improve traditional methods or to propose new algorithms in order to cope with such demands. Focusing on applications is important for driving progress in this discipline and for solving increasingly complex and time-consuming problems.

The aim of this Special Issue is to bring together original contributions on the latest theories and applications of evolutionary computing. Articles with solid theoretical and practical contributions and with a focus on innovative applications in different areas of science, engineering, and economics are welcome.

Dr. Javier Sánchez
Guest Editor

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Keywords

  • genetic algorithms
  • evolutionary algorithms
  • genetic programming
  • evolutionary programming
  • multi-objective optimization
  • combinatorial optimization
  • bio-inspired optimization
  • swarm intelligence
  • differential evolution
  • metaheuristics

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Published Papers (2 papers)

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Research

15 pages, 1368 KiB  
Article
Improved Dual-Center Particle Swarm Optimization Algorithm
by Zhouxi Qin and Dazhi Pan
Mathematics 2024, 12(11), 1698; https://doi.org/10.3390/math12111698 - 30 May 2024
Cited by 1 | Viewed by 722
Abstract
This paper proposes an improved dual-center particle swarm optimization (IDCPSO) algorithm which can effectively improve some inherent defects of particle swarm optimization algorithms such as being prone to premature convergence and low optimization accuracy. Based on the in-depth analysis of the velocity updating [...] Read more.
This paper proposes an improved dual-center particle swarm optimization (IDCPSO) algorithm which can effectively improve some inherent defects of particle swarm optimization algorithms such as being prone to premature convergence and low optimization accuracy. Based on the in-depth analysis of the velocity updating formula, the most innovative feature is the vectorial decomposition of the velocity update formula of each particle to obtain three different flight directions. After combining these three directions, six different flight paths and eight intermediate positions can be obtained. This method allows the particles to search for the optimal solution in a wider space, and the individual extreme values are greatly improved. In addition, in order to improve the global extreme value, it is designed to construct the population virtual center and the optimal individual virtual center by using the optimal position and the current position searched by the particle. Combining the above strategies, an adaptive mutation factor that accumulates the coefficient of mutation according to the number of iterations is added to make the particle escape from the local optimum. By running the 12 typical test functions independently 50 times, the results show an average improvement of 97.9% for the minimum value and 97.7% for the average value. The IDCPSO algorithm in this paper is better than other improved particle swarm optimization algorithms in finding the optimum. Full article
(This article belongs to the Special Issue Evolutionary Computation and Applications)
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29 pages, 749 KiB  
Article
Escaping Stagnation through Improved Orca Predator Algorithm with Deep Reinforcement Learning for Feature Selection
by Rodrigo Olivares, Camilo Ravelo, Ricardo Soto and Broderick Crawford
Mathematics 2024, 12(8), 1249; https://doi.org/10.3390/math12081249 - 20 Apr 2024
Cited by 1 | Viewed by 978
Abstract
Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization [...] Read more.
Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique’s performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy’s capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Evolutionary Computation and Applications)
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