Emerging Research in Optimization Algorithms in the Era of Big Data

Dear Colleagues,

In the era of big data, where the volume, velocity, and variety of data are increasing exponentially, the development of efficient optimization algorithms is an important goal. This Special Issue explores the dynamic landscape of optimization algorithms tailored to the challenges of big data analytics and presents the latest advances and emerging trends. From traditional optimization techniques to cutting-edge machine learning algorithms and metaheuristic approaches, the range of contributions highlights the diversity and richness of current research efforts. The convergence of computing power, advanced algorithms, and huge datasets has led to a renaissance of optimization methods and produced innovative approaches for solving complex optimization problems in big data applications. This collection covers a wide range of topics, including evolutionary algorithms, genetic programming, swarm intelligence, nature-inspired optimization techniques, parallel and distributed optimization algorithms optimized for the Cloud, deep learning-based optimization strategies, hybrid optimization frameworks, and optimization algorithms for real-time processing of big data and streaming analytics. In addition, applications of optimization algorithms are explored in various areas, such as healthcare, finance, transportation, and cybersecurity, incorporating advances in generative AI to improve optimization capabilities in cloud-based environments. Through this compilation, researchers and practitioners will gain insights into the latest methodologies, challenges, and opportunities in the field of optimization algorithms for big data analytics that drive innovation and enable transformative breakthroughs in data-driven decision making. The interdisciplinary nature of these contributions emphasizes the collaboration between computer science, mathematics, engineering, and various domain-specific disciplines. This Special Issue is a testament to the vibrant research community dedicated to advancing optimization algorithms in the context of big data analytics.

Dr. Marko Gulić
Prof. Dr. Heming Jia
Prof. Dr. Antonio Jiménez-Martín
Guest Editors

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• optimization algorithms
• big data analytics
• machine learning algorithms
• metaheuristic approaches
• evolutionary algorithms
• genetic programming
• swarm intelligence
• nature-inspired optimization techniques
• parallel and distributed optimization algorithms

Title: Analysis of Voltage Deviation Improvement in Microgrid Operation through Demand Response using Imperialist Competitive and Genetic Algorithms
Authors: Mahdi Ghaffari and Hamed H. Aly
Affiliation: Dalhousie University.
Abstract: In recent decades, with the expansion of distributed energy production technologies and increasing needs for more flexibility and efficiency in energy distribution systems, microgrids have a good innovative solution for local energy supply and enhancing resilience against network fluctuations. One of the basic challenges in the operation of microgrids is the optimal management of voltage and frequency in the network, which has been the subject to extensive research in the field of microgrid operational optimization. Additionally, since energy demand changes over time, the use of demand response strategies is of great importance. These strategies enable better energy management in microgrids, thereby improving system efficiency and stability. Given the complexity of optimization problems related to microgrid management, evolutionary optimization algorithms such as the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm have gained attention. These algorithms enable solving high-complexity optimization problems by considering various constraints and multiple objectives. In this paper individual and hybrid of ICA and GA are used to check the effects of voltage division optimization in microgrids based on demand response strategies.