Zhiwei Ye^1,2,3, Dingfeng Song¹, Haitao Xie^1,2,3,*, Jixin Zhang^1,2, Wen Zhou^1,2, Mengya Lei^1,2, Xiao Zheng^1,2, Jie Sun¹, Jing Zhou¹, Mengxuan Li¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5509-5530, 2025, DOI:10.32604/cmc.2025.067342 - 23 October 2025

Abstract The Multilayer Perceptron (MLP) is a fundamental neural network model widely applied in various domains, particularly for lightweight image classification, speech recognition, and natural language processing tasks. Despite its widespread success, training MLPs often encounter significant challenges, including susceptibility to local optima, slow convergence rates, and high sensitivity to initial weight configurations. To address these issues, this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer (LOEV-APO), which enhances both global exploration and local exploitation simultaneously. LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling (LHS) with Opposition-Based Learning (OBL), thus… More >

Sumika Chauhan¹, Govind Vashishtha^1,*, Riya Singh², Divesh Bharti³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 309-343, 2025, DOI:10.32604/cmes.2025.068628 - 31 July 2025

Abstract This paper introduces a novel optimization approach called Recuperated Seed Search Optimization (RSSO), designed to address challenges in solving mechanical engineering design problems. Many optimization techniques struggle with slow convergence and suboptimal solutions due to complex, nonlinear natures. The Sperm Swarm Optimization (SSO) algorithm, which mimics the sperm’s movement to reach an egg, is one such technique. To improve SSO, researchers combined it with three strategies: opposition-based learning (OBL), Cauchy mutation (CM), and position clamping. OBL introduces diversity to SSO by exploring opposite solutions, speeding up convergence. CM enhances both exploration and exploitation capabilities throughout More >

Jeng-Shyang Pan^1,2, Na Yu¹, Shu-Chuan Chu^1,*, An-Ning Zhang¹, Bin Yan³, Junzo Watada⁴

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2495-2520, 2025, DOI:10.32604/cmc.2024.058450 - 17 February 2025

Abstract The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s… More >

Feyza Altunbey Özbay¹, Erdal Özbay², Farhad Soleimanian Gharehchopogh^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1067-1110, 2024, DOI:10.32604/cmes.2024.054334 - 27 September 2024

Abstract Artificial rabbits optimization (ARO) is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature. However, for solving optimization problems, the ARO algorithm shows slow convergence speed and can fall into local minima. To overcome these drawbacks, this paper proposes chaotic opposition-based learning ARO (COARO), an improved version of the ARO algorithm that incorporates opposition-based learning (OBL) and chaotic local search (CLS) techniques. By adding OBL to ARO, the convergence speed of the algorithm increases and it explores the search space better. Chaotic maps in CLS… More > Graphic Abstract

Farouq Zitouni^1,*, Saad Harous², Abdulaziz S. Almazyad³, Ali Wagdy Mohamed^4,5, Guojiang Xiong⁶, Fatima Zohra Khechiba¹, Khadidja Kherchouche¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 219-265, 2024, DOI:10.32604/cmes.2024.052001 - 20 August 2024

Abstract Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to… More >

Chen Zhang¹, Liming Liu¹, Yufei Yang¹, Yu Sun¹, Jiaxu Ning², Yu Zhang³, Changsheng Zhang^1,4,*, Ying Guo⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5201-5223, 2024, DOI:10.32604/cmc.2024.050863 - 20 June 2024

Abstract The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population’s crowding degree More >

Yingui Qiu¹, Shuai Huang¹, Danial Jahed Armaghani², Biswajeet Pradhan³, Annan Zhou⁴, Jian Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2873-2897, 2024, DOI:10.32604/cmes.2023.029938 - 15 December 2023

Abstract As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule. The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications. The previously-proposed intelligent techniques in this field… More >

Laith Abualigah^{1,2,3,4,5,6,*}, Serdar Ekinci⁷, Davut Izci^7,8, Raed Abu Zitar⁹

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 169-183, 2023, DOI:10.32604/iasc.2023.040291 - 05 February 2024

Abstract Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability. This study proposes a novel approach for designing a fractional order proportional-integral-derivative (FOPID) controller that utilizes a modified elite opposition-based artificial hummingbird algorithm (m-AHA) for optimal parameter tuning. Our approach outperforms existing optimization techniques on benchmark functions, and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision. Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving More >

Xiang Wang¹, Liangsa Wang^2,*, Han Li¹, Yibin Guo¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2935-2969, 2023, DOI:10.32604/cmc.2023.044948 - 26 December 2023

Abstract The original whale optimization algorithm (WOA) has a low initial population quality and tends to converge to local optimal solutions. To address these challenges, this paper introduces an improved whale optimization algorithm called OLCHWOA, incorporating a chaos mechanism and an opposition-based learning strategy. This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase, thereby enhancing the quality of the initial whale population. Additionally, including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations. The work and contributions of this paper are primarily reflected in two aspects.… More >

Weizhi Liao¹, Xiaoyun Xia^1,3, Xiaojun Jia¹, Shigen Shen^2,*, Helin Zhuang^4,*, Xianchao Zhang¹

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3297-3323, 2023, DOI:10.32604/cmc.2023.040967 - 08 October 2023

Abstract As a new bionic algorithm, Spider Monkey Optimization (SMO) has been widely used in various complex optimization problems in recent years. However, the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant. Thus, this paper focuses on how to reconstruct SMO to improve its performance, and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design (SMO³) is developed. A position updating method based on the historical optimal domain and particle swarm for Local Leader Phase (LLP) and Global Leader Phase (GLP) is… More >