Junxiang Li^1,2, Zhipeng Dong², Ben Han³, Jianqiao Chen³, Xinxin Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070816 - 10 November 2025

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 >

Raja Masadeh¹, Omar Almomani^2,*, Abdullah Zaqebah¹, Shayma Masadeh³, Kholoud Alshqurat³, Ahmad Sharieh⁴, Nesreen Alsharman⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3709-3737, 2025, DOI:10.32604/cmc.2025.066797 - 23 September 2025

Abstract This research presents a novel nature-inspired metaheuristic optimization algorithm, called the Narwhale Optimization Algorithm (NWOA). The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals, “unicorns of the sea”, particularly the use of their distinctive spiral tusks, which play significant roles in hunting, searching prey, navigation, echolocation, and complex social interaction. Particularly, the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the… More >

Tareq Hamadneh¹, Belal Batiha², Omar Alsayyed³, Widi Aribowo⁴, Zeinab Montazeri⁵, Mohammad Dehghani^5,*, Frank Werner^6,*, Haider Ali⁷, Riyadh Kareem Jawad⁸, Ibraheem Kasim Ibraheem⁹, Kei Eguchi¹⁰

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2677-2718, 2025, DOI:10.32604/cmc.2025.064087 - 16 April 2025

Abstract In this study, a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm (BaOA). Inspired by the human interactions between barbers and customers, BaOA captures two key processes: the customer’s selection of a hairstyle and the detailed refinement during the haircut. These processes are translated into a mathematical framework that forms the foundation of BaOA, consisting of two critical phases: exploration, representing the creative selection process, and exploitation, which focuses on refining details for optimization. The performance of BaOA is evaluated using 52 standard… More >

Miloš Sedak^*, Maja Rosić, Božidar Rosić

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 2111-2145, 2025, DOI:10.32604/cmes.2025.059319 - 27 January 2025

Abstract This paper introduces a hybrid multi-objective optimization algorithm, designated HMODESFO, which amalgamates the exploratory prowess of Differential Evolution (DE) with the rapid convergence attributes of the Sailfish Optimization (SFO) algorithm. The primary objective is to address multi-objective optimization challenges within mechanical engineering, with a specific emphasis on planetary gearbox optimization. The algorithm is equipped with the ability to dynamically select the optimal mutation operator, contingent upon an adaptive normalized population spacing parameter. The efficacy of HMODESFO has been substantiated through rigorous validation against established industry benchmarks, including a suite of Zitzler-Deb-Thiele (ZDT) and Zeb-Thiele-Laumanns-Zitzler (DTLZ) More >

Tareq Hamadneh¹, Khalid Kaabneh², Ibraheem AbuFalahah³, Gulnara Bektemyssova^4,*, Galymzhan Shaikemelev⁴, Dauren Umutkulov⁴, Sayan Omarov⁵, Zeinab Monrazeri⁶, Frank Werner⁷, Mohammad Dehghani^6,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2721-2741, 2024, DOI:10.32604/cmc.2024.054317 - 15 August 2024

Abstract This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization (MFO), inspired by the unique behaviors observed in magnificent frigatebirds in their natural habitats. The foundation of MFO is based on the kleptoparasitic behavior of these birds, where they steal prey from other seabirds. In this process, a magnificent frigatebird targets a food-carrying seabird, aggressively pecking at it until the seabird drops its prey. The frigatebird then swiftly dives to capture the abandoned prey before it falls into the water. The theoretical framework of MFO is thoroughly detailed and mathematically represented, mimicking the frigatebird’s… More >

Weiwei Zhang¹, Jiaqiang Li¹, Chao Wang², Meng Li³, Zhi Rao^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4237-4257, 2024, DOI:10.32604/cmc.2024.050430 - 20 June 2024

Abstract In practical engineering, multi-objective optimization often encounters situations where multiple Pareto sets (PS) in the decision space correspond to the same Pareto front (PF) in the objective space, known as Multi-Modal Multi-Objective Optimization Problems (MMOP). Locating multiple equivalent global PSs poses a significant challenge in real-world applications, especially considering the existence of local PSs. Effectively identifying and locating both global and local PSs is a major challenge. To tackle this issue, we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded, promising regions and regulate the number of offspring in areas… More >

Yu Cheng^1,2,5, Yajun Huang³, Shuai Li⁴, Zhongbin Zhou⁵, Xiaohui Yuan^1,2,*, Yanming Xu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1935-1960, 2024, DOI:10.32604/cmes.2023.045668 - 29 January 2024

Abstract A new approach for flexoelectric material shape optimization is proposed in this study. In this work, a proxy model based on artificial neural network (ANN) is used to solve the parameter optimization and shape optimization problems. To improve the fitting ability of the neural network, we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training. The isogeometric analysis-finite element method (IGA-FEM) is used to discretize the flexural theoretical formulas and obtain samples, which helps ANN to build a proxy model from the model shape to More >

Elif Varol Altay, Osman Altay, Yusuf Özçevik^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1039-1094, 2024, DOI:10.32604/cmes.2023.029404 - 30 December 2023

Abstract Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve. Such design problems are widely experienced in many engineering fields, such as industry, automotive, construction, machinery, and interdisciplinary research. However, there are established optimization techniques that have shown effectiveness in addressing these types of issues. This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues. The algorithms used in the study are listed as: transient search optimization (TSO), equilibrium optimizer (EO), grey wolf optimizer… More >

Zhenyu Yan^1,*, Haotian Bian²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2241-2257, 2023, DOI:10.32604/csse.2023.040603 - 28 July 2023

Abstract As millimeter waves will be widely used in the Internet of Things (IoT) and Telematics to provide high bandwidth communication and mass connectivity, the coverage optimization of base stations can effectively improve the quality of communication services. How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers. This paper proposes the Muti-Fusion Sparrow Search Algorithm (MFSSA) optimize the situation to address the problem of discrete coverage maximization and rapid convergence. Firstly, the initial swarm diversity is enriched using a sine chaotic map, and dynamic adaptive weighting is… More >

Hao Cui, Yanling Guo^*, Yaning Xiao, Yangwei Wang^*, Jian Li, Yapeng Zhang, Haoyu Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1635-1675, 2023, DOI:10.32604/cmes.2023.026019 - 26 June 2023

Abstract Harris Hawks Optimization (HHO) is a novel meta-heuristic algorithm that imitates the predation characteristics of Harris Hawk and combines Lévy flight to solve complex multidimensional problems. Nevertheless, the basic HHO algorithm still has certain limitations, including the tendency to fall into the local optima and poor convergence accuracy. Coot Bird Optimization (CBO) is another new swarm-based optimization algorithm. CBO originates from the regular and irregular motion of a bird called Coot on the water’s surface. Although the framework of CBO is slightly complicated, it has outstanding exploration potential and excellent capability to avoid falling into… More > Graphic Abstract