In recent years, Parameter Estimation (PE) has become a topic of growing interest due to its broad applications in science and engineering. An important application is the identification of Chaotic Systems (CS), which enables synchronization and control of chaotic behavior. However, the parameter estimation of CS is a highly nonlinear and multidimensional optimization problem where traditional approaches are often unsuitable. To overcome these limitations, Evolutionary Computation Techniques (ECT) have been widely adopted to tackle complex nonlinear optimization tasks. Recently, classical and modern ECT methods have been proposed for estimating the parameters of chaotic systems. However, mos... More >

Metaheuristic Algorithms (MAs) are commonly used in the scope of digital image processing, in particular, image segmentation processes. This is evident in Multilevel Thresholding (MTH) methods, where the optimal threshold configuration must be found to produce high-quality segmented images. Minimum Cross-Entropy (MCE) is one of the most prominent techniques for MTH due to its simplicity and efficiency. This article proposes a comparison of recent MAs that have not yet been implemented for image segmentation. Six recently published MAs were implemented and tested on nine complicated images selected from the BSDS300 dataset. Analyzing the results reveals the best algorithm when MCE is used as... More >

This study investigates the sentiment polarity (positive, negative, neutral) and specific emotions (joy, sadness, anger, surprise, trust, anticipation, disgust, and fear) expressed by Generation Z in digital platform comments regarding seven female duets with famous male singer. A dataset of 500 digital comments (250 from YouTube, 125 from Twitter, 125 from Instagram) was collected. The sample was then refined to include comments from 100 individuals (50 men, 50 women) affiliated with a private university in Mexico City, ensuring gender balance. Sentiment polarity was classified using a Bidirectional Encoder Representations from Transformers (BERT) model, with its hyperparameters (learning r... More >

Swarm and evolutionary computation topics has demonstrated remarkable effectiveness in solving complex optimization problems across various scientific and engineering fields. However, as these methods are increasingly used in high-risk applications such as healthcare, finance, and autonomous systems, there is a growing need to address their ethical, interpretability, and social implications. This editorial outlines key guidelines for creating bio-inspired computing systems that are responsible and transparent, highlighting the fundamental role of ethics and explainability in shaping the future of evolutionary and swarm learning. More >

This editorial highlights the principles, current applications, and future potential of swarm intelligence as a decentralized, self-organizing paradigm driving innovation across robotics, optimization, and AI-integrated systems. More >

The Differential Evolution (DE) has stood as a cornerstone of Evolutionary Computation (EC), inspiring numerous approaches. Despite its foundational role, the selection stage of DE has received little attention, with only 2% of documented modifications in the literature on this aspect. Recent research has underscored the potential for significant algorithmic improvement through thoughtful modifications to this critical stage, particularly in accelerating the exploitation phase. This study introduces a novel EC strategy rooted in DE principles. To enhance algorithmic exploration, a systematic decision-making process regarding function evaluations is employed to select between two of the most... More >

Farmers sometimes grow crops with low yields, wasting land, labor, and time—especially in developing countries where demand for food is increasing. A Crop Recommendation System (CRS) can help by using precision farming techniques that analyze soil and environmental data to suggest the most suitable crops. This study proposes a CRS using a Modified Salp Swarm Algorithm (MSSA) for feature selection and an Adaptive Weighted Bi-directional Long Short-Term Memory (AWBiLSTM) ensemble for prediction. MSSA enhances the original algorithm by improving local search and convergence speed, addressing SSA’s limitations. Climate data is pre-processed and relevant features are selected using MSSA. AWBi... More >

The ICCK Transactions on Swarm and Evolutionary Learning (TSEL) is a new journal focused on nature-inspired computation. This journal is launched at a time when swarm and evolutionary algorithms have impacted different areas of research. Their progress is not only theoretical but also applications in complex tasks, supporting their adaptability and popularity. In this sense, TSEL aims to be at the forefront of the rapidly evolving landscape of these interdisciplinary fields. On behalf of the editorial team, I warmly welcome scholars, experts, researchers, and readers who support and follow our journal. More >