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ICSOMPA: A novel improved hybrid algorithm for global optimisation

EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 3337-3440页

作者： Mohammed, Usman Karataev, Tologon Oshiga, Omotayo Oghorada, Oghenewvogaga Adeshina, Steve A. Hussein, Suleiman U. Abualigah, Laith Galadima, Ahmadu Abubakar, Dayyabu Sanusi, Jaafaru Nile Univ Nigeria Elect & Elect Engn Dept Abuja Nigeria Nile Univ Nigeria Comp Engn Dept Abuja Nigeria Nile Univ Nigeria Civil Engn Dept Abuja Nigeria Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Ctr Tabuk 71491 Saudi Arabia Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia

The Marine Predators algorithm (MPA) is among the recently proposed metaheuristic algorithms (MAs), and it got its inspiration from the ocean predators' foraging behaviour based on Brownian and Levy motions. Good exploration, convergence accuracy, ease of implementation, easy parameter settings, fewer parameters, etc., are some of its strengths. Nevertheless, it experiences premature convergence and local optima trapping sometimes. The Competitive Swarm Optimiser (CSO) is a Particle Swarm Optimiser (PSO) variant. It got its inspiration from the social groups' collective decision-making and social behaviour. Good exploitation, a balance between exploitation and exploration, low premature convergence, algorithmic simplicity, etc., are some of its strengths. However, it has a loss of diversity and premature convergence. Aiming at solving the MPA's weaknesses and utilising the complementary strengths of MPA and CSO, an improved hybrid MPA has been proposed and it's named ICSOMPA. The MPA was first improved by utilising a chaotic mapping strategy for the MPA initialisation, utilising an adaptive convergence factor (CF) for step size control aiming at striking a balance between local exploitation and global exploration, utilising the Weibull distribution in place of Brownian motion aiming at preventing algorithm local trapping, and utilising chaotic sequences in the MPA's early stages as opposed to using random numbers to avoid overlap and uneven agent distribution. The improved MPA was then hybridised with the CSO aiming at leveraging the MPA's and CSO's strengths to provide higher convergence accuracy, convergence speed, and avoiding local optima trapping. The proposed algorithm's performance was tested and validated using the Congress on Evolutionary Computation (CEC) suites and engineering design problems. The CEC2014, CEC2017, CEC2020, CEC2022, and the 3 most employed engineering design problems have been utilised. Six different sets of experiments have been conducte

关键词： CSO hybrid ICSOMPA Improved MPA algorithm

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New hybrid FDTD algorithm for electromagnetic problem analysis

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Chinese Physics B 2019年第7期28卷 211-215页

作者： Xin-Bo He Bing Wei Kai-Hang Fan Yi-Wen Li Xiao-Long Wei School of Physics and Optoelectronic Engineering Xidian University Xi'an 710071 China Collaborative Innovation Center of Information Sensing and Understanding at Xidian University Xi'an 710071 China Science and Technology on Plasma Dynamic Laboratory Airforce Engineering University Xi'an 710038 China

Since the time step of the traditional finite-difference time-domain(FDTD) method is limited by the small grid size, it is inefficient when dealing with the electromagnetic problems of multi-scale ***, the explicit and unconditionally stable FDTD(US-FDTD) approach has been developed to break through the limitation of Courant–Friedrich–Levy(CFL) ***, the eigenvalues and eigenvectors of the system matrix must be calculated before the time iteration in the explicit ***, the eigenvalue decomposition is also time consuming, especially for complex electromagnetic problems in practical *** addition, compared with the traditional FDTD method, the explicit US-FDTD method is more difficult to introduce the absorbing boundary and plane *** solve the drawbacks of the traditional FDTD and the explicit US-FDTD, a new hybrid FDTD algorithm is proposed in this *** combines the explicit US-FDTD with the traditional FDTD, which not only overcomes the limitation of CFL condition but also reduces the system matrix dimension, and introduces the plane wave and the perfectly matched layer(PML) absorption boundary *** the hybrid algorithm, the calculation of the eigenvalues is only required in the fine mesh region and adjacent coarse mesh ***, the calculation efficiency is greatly ***, the plane wave and the absorption boundary introduction of the traditional FDTD method can be directly *** results demonstrate the effectiveness, accuracy, stability, and convenience of this hybrid algorithm.

关键词： unconditionally stable hybrid FDTD algorithm electromagnetic problem

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Human Face Super-Resolution Based on hybrid algorithm

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Advances in Molecular Imaging 2018年第4期8卷 39-47页

作者： Jinfeng Xia Zhizheng Yang Fang Li Yuanda Xu Nan Ma Chunxing Wang Shandong Normal University Jinan China

Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced;the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.

关键词： Face Hallucination Super Resolution Convolutional Network hybrid algorithm

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Multiple-Objective Optimization and Design of Series-Parallel Systems Using Novel hybrid Genetic algorithm Meta-Heuristic Approach

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World Journal of Engineering and Technology 2018年第3期6卷 532-555页

作者： Essa Abrahim Abdulgader Saleem Thien-My Dao Zhaoheng Liu Department of Mechanical/Manufacturing Engineering école de Technologie Supérieure Montreal Canada

In this study, we develop a new meta-heuristic-based approach to solve a multi-objective optimization problem, namely the reliability-redundancy allocation problem (RRAP). Further, we develop a new simulation process to generate practical tools for designing reliable series-parallel systems. Because the?RRAP is an NP-hard problem, conventional techniques or heuristics cannot be used to find the optimal solution. We propose a genetic algorithm (GA)-based hybrid meta-heuristic algorithm, namely the hybrid genetic algorithm (HGA), to find the optimal solution. A simulation process based on the HGA is developed to obtain different alternative solutions that are required to generate application tools for optimal design of reliable series-parallel systems. Finally, a practical case study regarding security control of a gas turbine in the overspeed state is presented to validate the proposed algorithm.

关键词： Multi-Objective Optimization Reliability-Redundancy Allocation Overspeed Gas Turbine hybrid Genetic algorithm

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A hybrid Clustering algorithm for Identifying High Density Clusters

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2017年

作者： m. anthony wong

A hybrid Clustering algorithm for Identifying High Density Clusters by Wong, M. Anthony; published by Cambridge, Mass. : Massachusetts Institute of Technology, Alfred P. Sloan School of Management

关键词： algorithm clustering clusters density hybrid

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Advanced genetic algorithm (GA)-independent component analysis (ICA) ensemble model for predicting trapped humans through hybrid dimensionality reduction

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SCIENTIFIC AFRICAN 2025年 27卷

作者： Jiya, Enoch Adama Oluwafemi, Ilesanmi B. Landmark Univ Dept Elect & Informat Engn Omu Aran Nigeria Ekiti State Univ Dept Elect & Elect Engn Ado Ekiti Nigeria

The collapse of man-made structures, which frequently bury people beneath rubble, is one of the main causes of death worldwide. Natural disasters and human inefficiency, degradation, and decay are the main causes of this. So, in these urgent situations, a quick and effective deployment is crucial. No-line-of-sight (NLOS) signal analysis, which is the fundamental of seeing through the wall of critical information that is not visible, is disclosed through analysis, and developmental systems can be seen for identifying imprisoned human victims and detecting differences in disaster scenarios. This technology is essential for locating stranded people and evaluating different crises. Ultra-wideband (UWB) signal data, augmented by machine learning techniques, provides a large and quantified output that is useful for applications including engineering, scientific research, and Search and Rescue (SAR) operations. However, this method's primary drawbacks are its large dimensionality, infrequency, and noise, which makes catastrophic scenario prediction difficult. The curse of dimensionality has been addressed in a variety of ways, but issues with accuracy, dependability, and scalability still exist. To choose relevant subset features from the data and for better generalization in various contexts, this work uses an adaptive human presence detector algorithm that hybridizes dimensionality reduction techniques genetic algorithm (GA), which maximizes feature selection, and independent component analysis (ICA), which lowers the dimensionality of the chosen features. The features are sent into the classifiers for technique analysis based on their class variants. A bagged ensemble machine learning classifier was used to assess the reduced dataset, and among other performance measures, The findings with accuracy are 85.69 %, sensitivity of 79.30, and specificity of 91.67 %. These outcomes show how the suggested hybrid strategy can be used to efficiently pick and classify features in

关键词： Classification Dimensionality reduction hybrid No-line-of-sight Features algorithm

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A hybrid Sperm Swarm Optimization and Genetic algorithm for Unimodal and Multimodal Optimization Problems

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IEEE ACCESS 2022年 10卷 109580-109596页

作者： Raj, Bryan Ahmedy, Ismail Idris, Mohd Yamani Idna Noor, Rafidah Md Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Ctr Mobile Cloud Comp Kuala Lumpur 50603 Malaysia

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic algorithm (SAGA), hybrid Particle Swarm Optimization and Genetic algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related hybrid Sperm Swarm Optimization and Gravitational Search algorithm (HSSOGSA). The results are evaluated in terms of each method's best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

关键词： Genetic algorithms Metaheuristics Particle swarm optimization Convergence Standards Simulated annealing Genetics algorithm design and analysis Optimization algorithm genetic algorithm hybrid metaheuristic multimodal optimization sperm swarm optimization unimodal

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A New Metaheuristic Approach to Solving Benchmark Problems: hybrid Salp Swarm Jaya algorithm

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Computers, Materials & Continua 2022年第5期71卷 2923-2941页

作者： Erkan Erdemir Adem Alpaslan Altun Department of Information Technologies Tokat Vocational and Technical Anatolian High SchoolMerkez/Tokat60030Turkey Department of Computer Engineering Faculty of TechnologyKonya Selcuk UniversitySelcuklu/Konya42130Turkey

Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational *** with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of *** this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is *** speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two *** hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution *** works according to the best and worst food source *** this way,it is thought that the leader-follower salps will find the best solution to reach the food *** hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark *** results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was *** addition,these results were statistically compared using wilcoxon rank sum test with other *** to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.

关键词： Metaheuristic optimization benchmark algorithm swarm hybrid

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An Implementation of the hybrid Genetic algorithm Based Routing for Manet

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2016年

作者： vijay trivedi ghumendra kumar dashriye

An Implementation of the hybrid Genetic algorithm Based Routing for Manet by Ghumendra Kumar Dashriye ,vijay Trivedi ,vineet Richhariya; published by

关键词： algorithm based genetic hybrid hybrid routing implementation manet network simulation performance improvement. routing

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hybrid Optimization algorithm of Particle Swarm Optimization and Cuckoo Search for Preventive Maintenance Period Optimization

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DISCRETE DYNAMICS IN NATURE AND SOCIETY 2016年第Pt.1期2016卷 1516271-1-1516271-12页

作者： Guo, Jianwen Sun, Zhenzhong Tang, Hong Jia, Xuejun Wang, Song Yan, Xiaohui Ye, Guoliang Wu, Guohong Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Dongguan Neutron Sci Ctr Dongguan 523890 Peoples R China S China Univ Technol Coll Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China Dongguan Hengli Mould Technol Dev Ltd Co Dongguan 523460 Peoples R China

All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.

关键词： hybrid Optimization algorithm Particle swarm optimization Optimization algorithms algorithms Searches Cuculidae

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