Zijian Liu¹, Yong Shi², Chuanqi Li¹, Xiliang Zhang^3,*, Jian Zhou¹, Manoj Khandelwal^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 153-183, 2025, DOI:10.32604/cmes.2025.062426 - 11 April 2025

Abstract Given the growing concern over global warming and the critical role of carbon dioxide (CO₂) in this phenomenon, the study of CO₂-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration. A large number of experiments have proved that CO₂ interaction time (T), saturation pressure (P) and other parameters have significant effects on coal strength. However, accurate evaluation of CO₂-induced alterations in coal strength is still a difficult problem, so it is particularly important to establish accurate and efficient prediction models. This study explored the application of advanced machine learning (ML)… More >

Hadi Fattahi¹, Hossein Ghaedi¹, Danial Jahed Armaghani^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 747-766, 2025, DOI:10.32604/cmes.2025.062390 - 11 April 2025

Abstract In contemporary geotechnical projects, various approaches are employed for forecasting the settlement of shallow foundations (S_m). However, achieving precise modeling of foundation behavior using certain techniques (such as analytical, numerical, and regression) is challenging and sometimes unattainable. This is primarily due to the inherent nonlinearity of the model, the intricate nature of geotechnical materials, the complex interaction between soil and foundation, and the inherent uncertainty in soil parameters. Therefore, these methods often introduce assumptions and simplifications, resulting in relationships that deviate from the actual problem’s reality. In addition, many of these methods demand significant investments of… More >

Sung-Jung Hsiao¹, Wen-Tsai Sung^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3891-3905, 2025, DOI:10.32604/cmc.2025.060056 - 06 March 2025

Abstract For optimization algorithms, the most important consideration is their global optimization performance. Our research is conducted with the hope that the algorithm can robustly find the optimal solution to the target problem at a lower computational cost or faster speed. For stochastic optimization algorithms based on population search methods, the search speed and solution quality are always contradictory. Suppose that the random range of the group search is larger; in that case, the probability of the algorithm converging to the global optimal solution is also greater, but the search speed will inevitably slow. The smaller… More >

Laith Abualigah^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1539-1592, 2025, DOI:10.32604/cmc.2025.060765 - 17 February 2025

Abstract Particle Swarm Optimization (PSO) has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields. This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications, but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms. Covering six strategic areas, which include Data Mining, Machine Learning, Engineering Design, Energy Systems, Healthcare, and Robotics, the study demonstrates the versatility and effectiveness of the PSO. Experimental results are, however, used to show the strong and More >

Robertas Damasevicius^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1493-1538, 2025, DOI:10.32604/cmc.2024.057431 - 17 February 2025

Abstract Heuristic optimization algorithms have been widely used in solving complex optimization problems in various fields such as engineering, economics, and computer science. These algorithms are designed to find high-quality solutions efficiently by balancing exploration of the search space and exploitation of promising solutions. While heuristic optimization algorithms vary in their specific details, they often exhibit common patterns that are essential to their effectiveness. This paper aims to analyze and explore common patterns in heuristic optimization algorithms. Through a comprehensive review of the literature, we identify the patterns that are commonly observed in these algorithms, including… More >

Ji Zhou^1,2, Yijun Lu³, Qiong Tian^1,2, Haichuan Liu³, Mahdi Hasanipanah^4,5,*, Jiandong Huang^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1595-1617, 2024, DOI:10.32604/cmes.2024.048398 - 20 May 2024

Abstract Blasting in surface mines aims to fragment rock masses to a proper size. However, flyrock is an undesirable effect of blasting that can result in human injuries. In this study, support vector regression (SVR) is combined with four algorithms: gravitational search algorithm (GSA), biogeography-based optimization (BBO), ant colony optimization (ACO), and whale optimization algorithm (WOA) for predicting flyrock in two surface mines in Iran. Additionally, three other methods, including artificial neural network (ANN), kernel extreme learning machine (KELM), and general regression neural network (GRNN), are employed, and their performances are compared to those of four More >

Liang Chen¹, Jingbo Zhang¹, Linjie Wu¹, Xingjuan Cai^1,2,*, Yubin Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 363-383, 2024, DOI:10.32604/cmes.2024.049044 - 16 April 2024

Abstract The large-scale multi-objective optimization algorithm (LSMOA), based on the grouping of decision variables, is an advanced method for handling high-dimensional decision variables. However, in practical problems, the interaction among decision variables is intricate, leading to large group sizes and suboptimal optimization effects; hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables (MOEAWOD) is proposed in this paper. Initially, the decision variables are perturbed and categorized into convergence and diversity variables; subsequently, the convergence variables are subdivided into groups based on the interactions among different decision variables. If the size of… More >

Junjie Zhao, Diyuan Li^*, Jingtai Jiang, Pingkuang Luo

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 275-304, 2024, DOI:10.32604/cmes.2024.046960 - 16 April 2024

Abstract Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and time-consuming. There is a pressing need for more effective methods to determine rock UCS, especially in deep mining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rock material in deep mining environments by combining three boosting-based machine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables,… More > Graphic Abstract

Mani Parimala¹, Karthikeyan Prakash¹, Ashraf Al-Quran^2,*, Muhammad Riaz³, Saeid Jafari⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1095-1118, 2024, DOI:10.32604/cmes.2023.031193 - 30 December 2023

Abstract The idea of linear Diophantine fuzzy set (LDFS) theory with its control parameters is a strong model for machine learning and optimization under uncertainty. The activity times in the critical path method (CPM) representation procedures approach are initially static, but in the Project Evaluation and Review Technique (PERT) approach, they are probabilistic. This study proposes a novel way of project review and assessment methodology for a project network in a linear Diophantine fuzzy (LDF) environment. The LDF expected task time, LDF variance, LDF critical path, and LDF total expected time for determining the project network… More > Graphic Abstract

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 >