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Search Results (9)
  • Open Access

    ARTICLE

    Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

    Laith Abualigah1,2,3,4,5,6,*, Serdar Ekinci7, Davut Izci7,8, Raed Abu Zitar9

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

    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 >

  • Open Access

    ARTICLE

    An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction

    Xiang Wang1, Liangsa Wang2,*, Han Li1, Yibin Guo1

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

    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 >

  • Open Access

    ARTICLE

    An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

    Yingui Qiu1, Shuai Huang1, Danial Jahed Armaghani2, Biswajeet Pradhan3, Annan Zhou4, Jian Zhou1,*

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

    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 >

  • Open Access

    ARTICLE

    A Spider Monkey Optimization Algorithm Combining Opposition-Based Learning and Orthogonal Experimental Design

    Weizhi Liao1, Xiaoyun Xia1,3, Xiaojun Jia1, Shigen Shen2,*, Helin Zhuang4,*, Xianchao Zhang1

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

    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 >

  • Open Access

    ARTICLE

    Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization for Solving Continuous Numerical Optimization Problems

    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

    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

    Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization for Solving Continuous Numerical Optimization Problems

  • Open Access

    ARTICLE

    Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

    Jianguo Zheng, Shuilin Chen*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3619-3635, 2023, DOI:10.32604/csse.2023.038025

    Abstract To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At More >

  • Open Access

    ARTICLE

    An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

    Yaning Xiao, Xue Sun*, Yanling Guo, Sanping Li, Yapeng Zhang, Yangwei Wang

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 815-850, 2022, DOI:10.32604/cmes.2022.019198

    Abstract Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting… More >

  • Open Access

    ARTICLE

    A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

    Milan Tair1, Nebojsa Bacanin1, Miodrag Zivkovic1, K. Venkatachalam2,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 959-982, 2022, DOI:10.32604/cmc.2022.024989

    Abstract There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted More >

  • Open Access

    ARTICLE

    Strengthened Initialization of Adaptive Cross-Generation Differential Evolution

    Wei Wan1, Gaige Wang1,2,3,*, Junyu Dong1

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1495-1516, 2022, DOI:10.32604/cmes.2021.017987

    Abstract Adaptive Cross-Generation Differential Evolution (ACGDE) is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms (EAs). However, its convergence and diversity are not satisfactory compared with the latest algorithms. In order to adapt to the current environment, ACGDE requires improvements in many aspects, such as its initialization and mutant operator. In this paper, an enhanced version is proposed, namely SIACGDE. It incorporates a strengthened initialization strategy and optimized parameters in contrast to its predecessor. These improvements make the direction of crossgeneration mutation more clearly and the ability of searching More >

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