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  • Open Access


    Research on Coordinated Development and Optimization of Distribution Networks at All Levels in Distributed Power Energy Engineering

    Zhuohan Jiang1, Jingyi Tu1, Shuncheng Liu1, Jian Peng1, Guang Ouyang2,*

    Energy Engineering, Vol.120, No.7, pp. 1655-1666, 2023, DOI:10.32604/ee.2023.026981

    Abstract The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels. This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions. The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales. More >

  • Open Access


    Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

    Dongyang Li1, Shiyu Du2,*, Yiming Zhang2, Meiting Zhao3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2775-2803, 2023, DOI:10.32604/cmc.2023.035911

    Abstract Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s… More >

  • Open Access


    A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

    Abdelazim G. Hussien1,2, Guoxi Liang3, Huiling Chen4,*, Haiping Lin5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2267-2289, 2023, DOI:10.32604/cmes.2023.024247

    Abstract Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution, so more new techniques and methods are needed to solve such challenges. Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure. Sine Cosine Algorithm (SCA) is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine & Cosine. However, like all other metaheuristic algorithms, SCA has a slow convergence and may fail in sub-optimal regions. In this study, an enhanced version of SCA named RDSCA is suggested… More >

  • Open Access


    Hybrid Global Optimization Algorithm for Feature Selection

    Ahmad Taher Azar1,2,*, Zafar Iqbal Khan2, Syed Umar Amin2, Khaled M. Fouad1,3

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2021-2037, 2023, DOI:10.32604/cmc.2023.032183

    Abstract This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm (PLTVACIW-PSO). Its designed has introduced the benefits of Parallel computing into the combined power of TVAC (Time-Variant Acceleration Coefficients) and IW (Inertial Weight). Proposed algorithm has been tested against linear, non-linear, traditional, and multiswarm based optimization algorithms. An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO. Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIW-PSO vs. IW based Particle Swarm Optimization (PSO) algorithms, TVAC based PSO algorithms, traditional PSO, Genetic algorithms (GA),… More >

  • Open Access


    Improved Harmony Search with Optimal Deep Learning Enabled Classification Model

    Mahmoud Ragab1,2,3,*, Adel A. Bahaddad4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1783-1797, 2022, DOI:10.32604/cmc.2022.028055

    Abstract Due to drastic increase in the generation of data, it is tedious to examine and derive high level knowledge from the data. The rising trends of high dimension data gathering and problem representation necessitates feature selection process in several machine learning processes. The feature selection procedure establishes a generally encountered issue of global combinatorial optimization. The FS process can lessen the number of features by the removal of unwanted and repetitive data. In this aspect, this article introduces an improved harmony search based global optimization for feature selection with optimal deep learning (IHSFS-ODL) enabled classification model. The proposed IHSFS-ODL technique… More >

  • Open Access


    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 trapped in the local optimum,… More >

  • Open Access


    Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

    Betania Hernández-Ocaña, Adrian García-López, José Hernández-Torruco, Oscar Chávez-Bosquez*

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1797-1813, 2022, DOI:10.32604/iasc.2022.023570

    Abstract The Bacterial Foraging Algorithm (BFOA) is a well-known swarm collective intelligence algorithm used to solve a variety of constraint optimization problems with wide success. Despite its universality, implementing the BFOA may be complex due to the calibration of multiple parameters. Moreover, the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the BFOA which may lead to solutions close to the optimal but with more parameters than the original BFOA. That is why in this paper we present the design using the Unified Modeling Language (UML) and the implementation in the MATLAB platform of a front-end for… More >

  • Open Access


    A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems

    Andi Tang, Huan Zhou*, Tong Han, Lei Xie

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 331-364, 2022, DOI:10.32604/cmes.2021.017310

    Abstract The sparrow search algorithm (SSA) is a newly proposed meta-heuristic optimization algorithm based on the sparrow foraging principle. Similar to other meta-heuristic algorithms, SSA has problems such as slow convergence speed and difficulty in jumping out of the local optimum. In order to overcome these shortcomings, a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy (CLSSA) is proposed in this paper. Firstly, in order to balance the exploration and exploitation ability of the algorithm, chaotic mapping is introduced to adjust the main parameters of SSA. Secondly, in order to improve the diversity of the population… More >

  • Open Access


    IRKO: An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

    R. Manjula Devi1, M. Premkumar2, Pradeep Jangir3, Mohamed Abdelghany Elkotb4,5, Rajvikram Madurai Elavarasan6, Kottakkaran Sooppy Nisar7,*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4803-4827, 2022, DOI:10.32604/cmc.2022.020847

    Abstract Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost, gains, energy, mass, and so on. In order to solve optimization problems, metaheuristic algorithms are essential. Most of these techniques are influenced by collective knowledge and natural foraging. There is no such thing as the best or worst algorithm; instead, there are more effective algorithms for certain problems. Therefore, in this paper, a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization (RKO) algorithm, called Improved Runge-Kutta Optimization (IRKO) algorithm, is suggested for solving optimization problems. The IRKO is formulated using the basic RKO and… More >

  • Open Access


    Swarm-Based Extreme Learning Machine Models for Global Optimization

    Mustafa Abdul Salam1,*, Ahmad Taher Azar2, Rana Hussien2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6339-6363, 2022, DOI:10.32604/cmc.2022.020583

    Abstract Extreme Learning Machine (ELM) is popular in batch learning, sequential learning, and progressive learning, due to its speed, easy integration, and generalization ability. While, Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence, high time and space complexity. In ELM, the hidden layer typically necessitates a huge number of nodes. Furthermore, there is no certainty that the arrangement of weights and biases within the hidden layer is optimal. To solve this problem, the traditional ELM has been hybridized with swarm intelligence optimization techniques. This paper displays five proposed hybrid Algorithms “Salp Swarm Algorithm (SSA-ELM), Grasshopper… More >

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