Differential Evolution: Enhancements, Applications, Trends

Lina Bergström

doi:10.37421/2229-8711.2025.16.465

Brief Report - (2025) Volume 16, Issue 5

Differential Evolution: Enhancements, Applications, Trends

Lina Bergström^*

^*Correspondence: Lina BergstrÃ¶m, Department of Space Physics and Atmospheric Technologies,, Uppsala University, 751 05 Uppsala, Sweden, Sweden, Email:

Author information

Department of Space Physics and Atmospheric Technologies,, Uppsala University, 751 05 Uppsala, Sweden, Sweden

Received: 30-Sep-2025, Manuscript No. gjto-25-176017; Editor assigned: 02-Oct-2025, Pre QC No. P-176017; Reviewed: 14-Oct-2025, QC No. QC-176017; Revised: 21-Oct-2025, Manuscript No. R-176017; Published: 29-Oct-2025 , DOI: 10.37421/2229-8711.2025.16.465
Citation: Bergström, Lina. ”Differential Evolution: Enhancements, Applications, Trends.” Global J Technol Optim 16 (2025):465.
Copyright: © 2025 Bergström L. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

Abstract

Introduction

Differential Evolution (DE) has garnered significant attention within the computational intelligence community as a potent and adaptable optimization algorithm. Its widespread application across various scientific and engineering disciplines underscores its effectiveness in solving complex problems. Understanding its foundations, advancements, and diverse applications is essential for researchers and practitioners in the field. A comprehensive overview details Differential Evolution's fundamental principles, various algorithmic enhancements, and diverse applications. This work highlights the algorithm's evolution, pinpointing key challenges and future research directions in optimization [1]. Building on these principles, one study introduces a hybrid Differential Evolution algorithm that integrates improved opposition-based learning. This approach aims to enhance the feature selection process in machine learning, demonstrating DE's adaptability in complex data processing tasks by optimizing subset selection for better classification accuracy [2].

Another research initiative proposes a self-adaptive Differential Evolution variant designed to improve global optimization performance. This method incorporates a random parameter adjustment mechanism, allowing for dynamic adaptation of control parameters. This directly addresses the challenge of parameter tuning and showcases improved convergence and solution quality across various benchmark functions [3]. Practical engineering applications also benefit from DE's capabilities. For instance, an improved Differential Evolution algorithm has been applied to optimize the parameters of a Proportional-Integral-Derivative (PID) controller for an automatic voltage regulator system. This work effectively demonstrates DE's prowess in achieving robust control performance through efficient tuning of controller gains, which is crucial for real-world engineering scenarios [4].

Further expanding its utility, a novel hybrid multi-objective Differential Evolution algorithm is presented, specifically designed to tackle constrained optimization problems. This algorithm integrates DE with other techniques to efficiently explore the search space and maintain population diversity, thereby providing a robust solution for complex problems characterized by multiple conflicting objectives [5]. In the domain of power systems, a hybrid multi-objective optimization algorithm combines Differential Evolution and Particle Swarm Optimization to address optimal power flow problems, particularly those incorporating renewable energy sources. The method aims to minimize generation costs and emissions while simultaneously maximizing system stability, illustrating DE's value in modern power system management [6]. For challenges related to scale, an enhanced self-adaptive Differential Evolution algorithm has been specifically tailored for large-scale global optimization problems. This study addresses the scalability issues inherent in traditional DE by incorporating mechanisms to manage increasing dimensionality, showing improved performance on high-dimensional benchmark functions [7].

Beyond engineering and system optimization, DE also finds application in medical imaging. A modified Differential Evolution algorithm is presented for enhanced medical image segmentation. This approach effectively optimizes thresholding levels, which are critical for accurate object delineation in medical scans, highlighting DE's potential in improving diagnostic tools and image analysis within healthcare [8]. In the realm of renewable energy, a novel hybrid Differential Evolution algorithm is proposed for accurately estimating parameters of solar photovoltaic models. This method adeptly addresses the challenges posed by nonlinearity and multiple local optima, offering a reliable and efficient tool for optimizing solar energy system performance and design [9]. Finally, a recent survey provides an updated overview of Differential Evolution algorithms, systematically categorizing various variants and discussing their respective strengths and weaknesses across different application domains. This valuable resource highlights recent advancements and emerging trends, offering critical insights into the evolving landscape of DE for both researchers and practitioners [10].

Description

Differential Evolution (DE) stands out as a robust and versatile evolutionary algorithm, particularly effective in continuous optimization. Its core principles, which involve mutation, crossover, and selection operations, have been extensively reviewed, providing a deep understanding of its mechanisms and evolution over time. This foundational work also identifies key challenges and future directions, guiding further research in optimization [1]. The continuous drive to enhance DE’s performance has led to numerous algorithmic innovations. For instance, one significant enhancement involves integrating improved opposition-based learning into a hybrid DE framework, specifically targeting feature selection in machine learning. This approach aims to refine subset selection, thereby improving classification accuracy and showcasing DE’s capacity to adapt to complex data processing demands [2]. Furthermore, addressing the critical issue of parameter tuning, a self-adaptive DE variant introduces a random parameter adjustment mechanism. This allows the algorithm to dynamically adapt its control parameters, resulting in superior global optimization performance, better convergence, and higher solution quality across a range of benchmark functions [3].

The application of Differential Evolution extends significantly into practical engineering challenges, demonstrating its utility in real-world control systems. An improved DE algorithm has been successfully applied to optimize Proportional-Integral-Derivative (PID) controller parameters for automatic voltage regulator systems. This application underscores DE’s effectiveness in achieving stable and robust control performance through precise tuning of controller gains, which is vital for system stability and efficiency [4]. Beyond single-objective problems, DE has been adapted for multi-objective scenarios, particularly those involving constraints. A novel hybrid multi-objective DE algorithm specifically targets constrained optimization problems. By integrating DE with other sophisticated techniques, it efficiently navigates the search space and actively maintains population diversity, offering a powerful solution for problems with conflicting objectives and complex constraints [5].

In the energy sector, DE demonstrates its capability to manage intricate systems. A hybrid multi-objective optimization algorithm combines the strengths of Differential Evolution and Particle Swarm Optimization to tackle optimal power flow problems. This innovative approach is particularly relevant when incorporating renewable energy sources, as it strives to simultaneously minimize generation costs and environmental emissions while maximizing system stability. This illustrates DE’s critical role in modern power system management and the transition to sustainable energy [6]. The challenge of scalability in optimization, particularly for high-dimensional problems, is also addressed by advancements in DE. An enhanced self-adaptive DE algorithm has been developed to handle large-scale global optimization problems. This variant incorporates specialized mechanisms to manage increasing dimensionality, significantly improving performance on high-dimensional benchmark functions where traditional methods often struggle [7].

Differential Evolution's versatility also reaches into specialized fields like medical imaging and renewable energy technology. In medical imaging, a modified DE algorithm has been instrumental in enhancing image segmentation. It efficiently optimizes thresholding levels, which are crucial for accurate object delineation in medical scans. This development highlights DE’s potential to improve diagnostic tools and analytical processes within healthcare [8]. For renewable energy, a novel hybrid DE algorithm provides a reliable and efficient method for accurately estimating parameters of solar photovoltaic models. This is particularly valuable as it effectively addresses the inherent challenges of nonlinearity and multiple local optima present in such models, thereby optimizing solar energy system performance and design [9].

The continuous evolution and widespread adoption of Differential Evolution algorithms are regularly assessed and documented through comprehensive surveys. These surveys categorize various DE variants, analyze their strengths and weaknesses across different application domains, and provide valuable insights into recent advancements and emerging trends. Such reviews are indispensable resources for researchers and practitioners, offering a current perspective on the evolving landscape of DE and its applications, cementing its position as a dynamic and continuously developing optimization paradigm [10].

Conclusion

Differential Evolution (DE) has emerged as a powerful optimization algorithm, continually evolving to tackle increasingly complex problems. A foundational paper provides a comprehensive review of DE, detailing its core principles, algorithmic improvements, and varied applications, identifying challenges and future directions. Researchers have introduced several enhancements, including hybrid DE algorithms that integrate improved opposition-based learning for feature selection, demonstrating its adaptability in machine learning tasks. Further developments include self-adaptive DE variants incorporating random parameter adjustments, which enhance global optimization performance by dynamically tuning control parameters for better convergence and solution quality across benchmarks. The algorithm's practical utility extends to diverse engineering applications.

For instance, an improved DE algorithm optimizes Proportional-Integral-Derivative (PID) controller parameters for automatic voltage regulators, achieving robust control. Hybrid multi-objective DE algorithms are designed for constrained optimization problems, efficiently exploring search spaces and maintaining population diversity. Another hybrid approach combines DE with Particle Swarm Optimization to solve optimal power flow problems, integrating renewable energy sources while minimizing costs and emissions. For large-scale challenges, enhanced self-adaptive DE algorithms manage high dimensionality, improving performance on complex functions. DE also finds application in medical image segmentation by optimizing thresholding levels for accurate object delineation, and in accurately estimating parameters for solar photovoltaic models. Recent surveys continue to categorize and discuss DE variants, emphasizing recent advancements and emerging trends, cementing its role as a versatile tool for optimization.