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Advancements in Evolutionary Optimization Approaches: Theory and Applications

Submission Deadline: 28 February 2026 View: 3694 Submit to Special Issue

Guest Editors

Assistant Prof. Rui Zhong

Email: zhongrui@iic.hokudai.ac.jp

Affiliation: Information Initiative Center, Hokkaido University, Sapporo, 060-0808, Japan

Homepage:

Research Interests: Evolutionary Computation, Metaheuristics, Hyper-heuristics

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Assistant Prof. Jun Yu

Email: yujun@ie.niigata-u.ac.jp

Affiliation: Institute of Science and Technology, Niigata University, Niigata, 950-2181, Japan

Homepage:

Research Interests: Artificial Intelligence, Machine Learning, Deep Learning, Evolutionary Computation

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Summary

Evolutionary algorithms (EAs) have become a cornerstone of optimization methodology, demonstrating remarkable versatility in addressing complex, high-dimensional, and nonlinear optimization problems across disciplines. From addressing real-world engineering challenges to exploring theoretical frontiers, EAs and their variants? Such as genetic algorithms, differential evolution, and swarm intelligence. Continue to expand the boundaries of problem-solving capabilities.

This Special Issue aims to unite innovative contributions that delve into theoretical advancements and practical applications of evolutionary optimization. It seeks to provide a comprehensive platform for researchers and practitioners to share insights, methodologies, and breakthrough findings that propel the field forward.

We invite submissions to this Special Issue on topics including, but not limited to:

· Convergence analysis and stability of evolutionary algorithms.

· Novel operators, representations, and hybridization techniques.

· Benchmarking frameworks and performance evaluation.

· Large-scale and high-dimensional optimization.

· Emerging trends in bio-inspired algorithms.

· Advances in multi-objective optimization.

· Adaptive and self-adaptive mechanisms in EAs.

· Machine learning techniques in EAs.

· Engineering design optimization.

· Healthcare and biomedical applications.

· Industrial optimization and automation.

· Evolutionary optimization in big data and AI.

Keywords

Metaheuristics, Hyperheuristics, Performance Benchmarking, Real-World Applications, Large-scale Optimization, Multi-objective Optimization, Advanced Techniques in Optimization

Published Papers

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Open Access

ARTICLE

Constraint Intensity-Driven Evolutionary Multitasking for Constrained Multi-Objective Optimization
Leyu Zheng, Mingming Xiao, Yi Ren, Ke Li, Chang Sun
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072036
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract In a wide range of engineering applications, complex constrained multi-objective optimization problems (CMOPs) present significant challenges, as the complexity of constraints often hampers algorithmic convergence and reduces population diversity. To address these challenges, we propose a novel algorithm named Constraint Intensity-Driven Evolutionary Multitasking (CIDEMT), which employs a two-stage, tri-task framework to dynamically integrates problem structure and knowledge transfer. In the first stage, three cooperative tasks are designed to explore the Constrained Pareto Front (CPF), the Unconstrained Pareto Front (UPF), and the -relaxed constraint boundary, respectively. A CPF-UPF relationship classifier is employed to construct a problem-type-aware… More >

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ARTICLE

An Improved Variant of Multi-Population Cooperative Constrained Multi-Objective Optimization (MCCMO) for Multi-Objective Optimization Problem
Muhammad Waqar Khan, Adnan Ahmed Siddiqui, Syed Sajjad Hussain Rizvi
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070858
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract The multi-objective optimization problems, especially in constrained environments such as power distribution planning, demand robust strategies for discovering effective solutions. This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization (MCCMO) Algorithm, termed Adaptive Diversity Preservation (ADP). This enhancement is primarily focused on the improvement of constraint handling strategies, local search integration, hybrid selection approaches, and adaptive parameter control. The improved variant was experimented on with the RWMOP50 power distribution system planning benchmark. As per the findings, the improved variant outperformed the original MCCMO across the eleven performance metrics, particularly in terms… More >

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27
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ARTICLE

Multi-Objective Evolutionary Framework for High-Precision Community Detection in Complex Networks
Asal Jameel Khudhair, Amenah Dahim Abbood
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-31, 2026, DOI:10.32604/cmc.2025.068553
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities. Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can. Therefore, we utilized two objective functions… More >

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ARTICLE

Cooperative Metaheuristics with Dynamic Dimension Reduction for High-Dimensional Optimization Problems
Junxiang Li, Zhipeng Dong, Ben Han, Jianqiao Chen, Xinxin Zhang
CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070816
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract Owing to their global search capabilities and gradient-free operation, metaheuristic algorithms are widely applied to a wide range of optimization problems. However, their computational demands become prohibitive when tackling high-dimensional optimization challenges. To effectively address these challenges, this study introduces cooperative metaheuristics integrating dynamic dimension reduction (DR). Building upon particle swarm optimization (PSO) and differential evolution (DE), the proposed cooperative methods C-PSO and C-DE are developed. In the proposed methods, the modified principal components analysis (PCA) is utilized to reduce the dimension of design variables, thereby decreasing computational costs. The dynamic DR strategy implements periodic… More >

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Open Access

REVIEW

A Review of the Evolution of Multi-Objective Evolutionary Algorithms
Thomas Hanne, Mohammad Jahani Moghaddam
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4203-4236, 2025, DOI:10.32604/cmc.2025.068087
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract Multi-Objective Evolutionary Algorithms (MOEAs) have significantly advanced the domain of Multi-Objective Optimization (MOO), facilitating solutions for complex problems with multiple conflicting objectives. This review explores the historical development of MOEAs, beginning with foundational concepts in multi-objective optimization, basic types of MOEAs, and the evolution of Pareto-based selection and niching methods. Further advancements, including decom-position-based approaches and hybrid algorithms, are discussed. Applications are analyzed in established domains such as engineering and economics, as well as in emerging fields like advanced analytics and machine learning. The significance of MOEAs in addressing real-world problems is emphasized, highlighting their More >

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705
Open Access

REVIEW

A Comprehensive Review of Dynamic Community Detection: Taxonomy, Challenges, and Future Directions
Hiba Sameer Saeed, Amenah Dahim Abbood
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4375-4405, 2025, DOI:10.32604/cmc.2025.067783
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract In recent years, the evolution of the community structure in social networks has gained significant attention. Due to the rapid and continuous evolution of real-world networks over time. This makes the process of identifying communities and tracking their topology changes challenging. To tackle these challenges, it is necessary to find efficient methodologies for analyzing the behavior patterns of dynamic communities. Several previous reviews have introduced algorithms and models for community detection. However, these methods have not been very accurate in identifying communities. Moreover, none of the reviewed papers made an apparent effort to link algorithms… More >

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ARTICLE

Differential Evolution with Improved Equilibrium Optimizer for Combined Heat and Power Economic Dispatch Problem
Yuanfei Wei, Panpan Song, Qifang Luo, Yongquan Zhou
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1235-1265, 2025, DOI:10.32604/cmc.2025.066527
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract The combined heat and power economic dispatch (CHPED) problem is a highly intricate energy dispatch challenge that aims to minimize fuel costs while adhering to various constraints. This paper presents a hybrid differential evolution (DE) algorithm combined with an improved equilibrium optimizer (DE-IEO) specifically for the CHPED problem. The DE-IEO incorporates three enhancement strategies: a chaotic mechanism for initializing the population, an improved equilibrium pool strategy, and a quasi-opposite based learning mechanism. These strategies enhance the individual utilization capabilities of the equilibrium optimizer, while differential evolution boosts local exploitation and escape capabilities. The IEO enhances… More >

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Open Access

ARTICLE

Optimizing Microgrid Energy Management via DE-HHO Hybrid Metaheuristics
Jingrui Liu, Zhiwen Hou, Boyu Wang, Tianxiang Yin
CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4729-4754, 2025, DOI:10.32604/cmc.2025.066138
（This article belongs to the Special Issue: Advancements in Evolutionary Optimization Approaches: Theory and Applications)
Abstract In response to the increasing global energy demand and environmental pollution, microgrids have emerged as an innovative solution by integrating distributed energy resources (DERs), energy storage systems, and loads to improve energy efficiency and reliability. This study proposes a novel hybrid optimization algorithm, DE-HHO, combining differential evolution (DE) and Harris Hawks optimization (HHO) to address microgrid scheduling issues. The proposed method adopts a multi-objective optimization framework that simultaneously minimizes operational costs and environmental impacts. The DE-HHO algorithm demonstrates significant advantages in convergence speed and global search capability through the analysis of wind, solar, micro-gas turbine, More >

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Advancements in Evolutionary Optimization Approaches: Theory and Applications

Guest Editors

Summary

Keywords

Published Papers

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95

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19

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132

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27

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348

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127

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629

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178

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1755

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705

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626

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148

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2604

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2126

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1511

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708

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