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Performance analysis of multiple-input multiple-output orthogonal frequency division multiplexing system using arithmetic optimization algorithm

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COMPUTER STANDARDS & INTERFACES 2025年 92卷

作者： Deepa, R. Karthick, R. Velusamy, Jayaraj Senthilkumar, R. Nehru Inst Engn & Technol Dept Elect & Commun Engn Coimbatore Tamilnadu India KLN Coll Engn Dept Comp Sci & Engn Pottapalayam 630612 India Hindusthan Inst Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

This research aims to optimize the interference mitigation and improve system performance metrics, such as bit error rates, inter-carrier interference (ICI), and inter-symbol interference (ISI), by integrating the Redundant Discrete Wavelet Transform (RDWT) with the arithmetic optimization algorithm (AOA). This will increase the spectral efficiency of MIMO-OFDM systems for ultra-high data rate (UHDR) transmission in 5 G networks. The most important contribution of this study is the innovative combination of RDWT and AOA, which effectively addresses the down sampling issues in DWT-OFDM systems and significantly improves both error rates and data rates in high-speed wireless communication. Fifth-generation wireless networks require transmission at ultrahigh data rates, which necessitates reducing ISI and ICI. Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is employed to achieve the UHDR. The bandwidth and orthogonality of DWT-OFDM (discrete wavelet transform-based OFDM) are increased;however system performance is degraded due to down sampling. The redundant discrete wavelet transform (RDWT) is proposed for eliminating down sampling complexities. Simulation results demonstrate that RDWT effectively lowers bit error rates, ICI, and ISI by increasing the carrier-to-interference power ratio (CIR). The arithmetic optimization algorithm is used to optimize ICI cancellation weights, further enhancing spectrum efficiency. The proposed method is executed in MATLAB and achieves notable performance gains: up to 82.95% lower error rates and 39.88 % higher data rates compared to the existing methods. Conclusion: The integration of RDWT with AOA represents a significant advancement in enhancing the spectral efficiency of MIMO-OFDM systems for UHDR transmission in 5 G networks. The proposed method not only enhances system performance but also lays a foundation for future developments in high-speed wireless communication by addressing down sampl

关键词： Redundant discrete wavelet transform arithmetic optimization algorithm Enhanced spectral efficiency Discrete wavelet transform

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Multi-strategy arithmetic optimization algorithm for global optimization and uncertain motion tracking

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第1期28卷 1-28页

作者： Gao, Zeng Zhuang, Yi Gu, Jingjing Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing 211106 Jiangsu Peoples R China

Previous trackers mostly assume that the target has a smooth motion and perform target matching within a local window. However, targets often exhibit uncertain movements in real-world scenarios. Once the tracked target undergoes abrupt motion and moves outside the predefined local window, these trackers often fail. To address this issue, this paper introduces a multi-strategy arithmetic optimization algorithm (MSAOA) for global optimization and uncertain motion tracking. MSAOA is a high-performance optimizer that effectively solves uncertain motion in visual tracking. For MSAOA, we first design a dynamic stratification strategy to divide the population into three subpopulations. Then the mathematical model of each subpopulation is modified to improve the exploration and exploitation performance. Finally, extensive experiments over 23 benchmark functions and CEC2020 benchmark problems show that MSAOA is better than other algorithms. For the MSAOA tracker (MSAOAT), we utilize the proposed MSAOA as a joint local sampling-global search to generate candidate targets and match the best targets by a fitness function. More importantly, we design a verifier to unite local sampling and global search to form a complete tracking framework, which can effectively address smooth and abrupt motion in visual tracking. The qualitative and quantitative analyses on the general motion group and the abrupt motion group demonstrate that the MSAOAT can outperform other trackers.

关键词： Uncertain motion tracking arithmetic optimization algorithm Dynamic stratification strategy Multi-strategy

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A novel hybrid algorithm based on arithmetic optimization algorithm and particle swarm optimization for global optimization problems

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JOURNAL OF SUPERCOMPUTING 2024年第7期80卷 8857-8897页

作者： Deng, Xuzhen He, Dengxu Qu, Liangdong Guangxi Minzu Univ Sch Math & Phys Nanning 530006 Guangxi Peoples R China Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Guangxi Peoples R China

arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy.

关键词： arithmetic optimization algorithm Compound opposition-based learning Particle swarm optimization Global optimization Meta-heuristic optimization algorithm

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A Multi-strategy Enhanced arithmetic optimization algorithm and Its Application in Path Planning of Mobile Robots

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NEURAL PROCESSING LETTERS 2024年第1期56卷 18-18页

A multi-strategy enhanced arithmetic optimization algorithm called MSEAOA is proposed to address the issues of low population diversity, imbalanced exploration and exploitation capabilities, and low accuracy of optimal solution in the arithmetic optimization algorithm. Firstly, using the good point set strategy for population initialization to improve population diversity and thus accelerate convergence speed. Secondly, we integrate the exploration and exploition capabilities of differential self-learning strategy, best example learning strategy, and second-order differential perturbation strategy balancing algorithm. Finally, the introduction of somersault foraging strategy improves the accuracy of the optimal solution. We select 14 classical benchmark test functions and the CEC2019 function test set to test the optimization ability of MSEAOA, and apply MSEAOA to the path planning problem of mobile robots. MSEAOA is compared with other meta-heuristic optimization algorithms, and the experimental results are statistically analyzed by the Wilcoxon rank-sum test. The simulation experimental results show that MSEAOA performs the best among 14 benchmark functions, but for 10 CEC2019 functions, MSEAOA has the best optimization performance among 5 of them (50%). In the path optimization problem of mobile robots, the path obtained by MSEAOA is also the best among all algorithms, its path shortening rate exceeds 8.8% in 83% of environments. The results indicate that MSEAOA is a reliable algorithm suitable for function optimization and practical optimization problems.

关键词： arithmetic optimization algorithm Multi-strategy enhancement Benchmark functions Path planning

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A guided epsilon-dominance arithmetic optimization algorithm for effective multi-objective optimization in engineering design problems

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 31673-31700页

作者： Zouache, Djaafar Abualigah, Laith Boumaza, Farid Univ Mohamed El Bachir El Ibrahimi Dept Comp Sci Bordj Bou Arreridj Algeria Univ Sci & Technol Houari Boumediene LRIA Lab Algiers Algeria Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 13505 Lebanon Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Univ Oran1 Dept Comp Sci Oran Algeria

The arithmetic optimization algorithm (AOA) was recently proposed as a solution for single-objective real continuous problems and has demonstrated superior performance in various contexts. This paper presents a multi-objective version of the algorithm to solve multi-objective problems. The MOAOA employs an archive repository to keep and retrieve the non-dominated solutions produced during optimization. The leaders are then selected from the population archive to lead the solutions of the main population toward the potential search locations. The epsilon-dominance and crowding distance strategies balance the convergence and diversity of the obtained Pareto set. To assess the effectiveness and efficiency of the proposed algorithm, it was tested on various dimensions of multi-objective benchmarks, among them: five unconstrained test functions taken from ZDT-series and four multi-objective constrained tests functions (BNH, SRN, TNK, OSY). Also, it is evaluated on four multi-objective structural design problems, such as welded beam design, speed-reduced design, disk brake design, and four-bar truss design. The algorithm is compared with three algorithms widely used in multi-objectives optimization, such as MSSA, MOEA-D, and MOGWO. The comparison results demonstrate that MOAOA outperforms all other algorithms in terms of both convergence and diversity of solutions, achieving a score of (100%,100%) in the ZDT tests, (75%,50%) in the constrained test functions, and (75%,75%) for the structural design problems.

关键词： Multi-objective optimization Crowding distance Epsilon-dominance arithmetic optimization algorithm Engineering design application

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arithmetic optimization algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第1期38卷

作者： Nesakumar, Darwin A. Devi, Rukmani S. Inbamalar, T. M. Pavithra, K. N. RMK Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India RMD Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India RMK Coll Engn & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India RMD Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

The spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection-based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED-NAQ-AOA-SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro-fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS). This paper presents a novel method using energy detection and nonparametric amplitude quantization, optimized with the arithmetic optimization algorithm (AOA), for spectrum sensing in cognitive radio networks (CRNs). The proposed ED-NAQ-AOA-SS method achieves superior detection probability, lower error rates, and reduced settling time compared to traditional techniques, enhancing the safety and performance of primary users in ***

关键词： arithmetic optimization algorithm cognitive radio network nonparametric amplitude quantization-based energy detection

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A new binary arithmetic optimization algorithm for uncapacitated facility location problem

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NEURAL COMPUTING & APPLICATIONS 2024年第8期36卷 4151-4177页

作者： Bas, Emine Yildizdan, Gulnur Konya Tech Univ Fac Engn & Nat Sci Dept Software Engn TR-42075 Konya Turkiye Selcuk Univ Kulu Vocat Sch Konya Turkiye

arithmetic optimization algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed algorithm (TSA), and Gray Wolf optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method.

关键词： Binary optimization arithmetic optimization algorithm Logic gate Uncapacitated facility location problem

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Accelerated fuzzy min-max neural network and arithmetic optimization algorithm for optimizing hyper-boxes and feature selection

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NEURAL COMPUTING & APPLICATIONS 2024年第4期36卷 1553-1568页

作者： Alzaqebah, Malek Ahmed, Eman A. E. Imam Abdulrahman Bin Faisal Univ Coll Sci Dept Phys POB 1982 Dammam 31441 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Basic & Appl Sci Res Ctr POB 1982 Dammam 31441 Saudi Arabia

The fuzzy min-max (FMM) neural network effectively solves classification problems. Despite its success, it has been observed recently that FMM has overlapping between hyper-boxes in some datasets which certainly the overall classification performance, as well as FMM has a high compactional complexity, especially when dealing with high-dimensional datasets. a hybrid model combining arithmetic optimization algorithm (AOA) and Accelerated fuzzy min-max (AFMM) neural network is proposed to produce an AFMM-AOA model, where AFMM is used to speed up the hyper-boxes contraction process and to reduce the number of hyper-boxes, then AOA is employed for selecting the optimal feature set in each hyper-box, which results in lowering the compactional complexity and overcoming the overlapping problem. Furthermore, the AOA algorithm has been modified (MAOA) to enhance the exploiting ability of the original AOA algorithm for handling the high dimensionality in hyper-box representation by introducing both random and neighbor search methods. The performance of the proposed methods is evaluated using twelve datasets, as a result, the neighbor search method shows better performance than the random search. In addition, both methods showed superior performance compared with the original AOA and some state-of-the-art algorithms.

关键词： Fuzzy minimum maximum arithmetic optimization algorithm Hyperbox optimization Feature selection

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An improved arithmetic optimization algorithm for task offloading in mobile edge computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1667-1682页

作者： Li, Hongjian Liu, Jiaxin Yang, Lankai Liu, Liangjie Sun, Hu Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China

The emergence of Mobile Edge Computing (MEC) not only provides low-latency computing services for the User Equipment (UE), but also extends the battery life of the UE. However, the computational resources of MEC servers are usually limited, and how to efficiently offload UE's task and allocate the resources of MEC servers has become a research hotspot in MEC. In this paper, we develop an improved arithmetic optimization algorithm (IAOA) to optimize the convergence speed and convergence accuracy of the arithmetic optimization algorithm. Then a task offloading algorithm based on IAOA is designed to reduce the cost of offloading tasks in the framework including a single MEC server and multi-UE. The proposed algorithm jointly optimizes the task offloading strategy of the UEs and the resource allocation of the MEC server, meanwhile, models the weighted sum of delay and energy consumption as the system cost, with the goal of minimizing the system cost while satisfying the delay and energy consumption constraints of the tasks. Simulation results show that the proposed algorithm can effectively reduce the system cost and achieve a performance improvement of up to 20% compared with the benchmark algorithm.

关键词： Mobile edge computing Task offloading Limited computational resources Energy arithmetic optimization algorithm

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Multi-objective arithmetic optimization algorithm with random searching strategies to solve combined economic emission dispatch problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 195卷

作者： Hao, Wen-Kuo Wang, Jie-Sheng Li, Xu-Dong Liu, Yu Zhu, Jun-Hua Zhang, Min Wang, Min Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

Given the escalating environmental problems, the significance of combined economic emission dispatch (CEED) in electricity generation is progressively growing. To solve the CEED problem more effectively and achieve reduced fuel costs and pollutant emissions, a multi-objective arithmetic optimization algorithm (AOA) with random searching strategies was proposed. The math optimizer probability (MOP) is a parameter that governs the individual position update of the AOA algorithm. The proposed algorithm uses four random search strategies, namely tangent flight, Levy flight, Brownian motion and lognormal flight, to replace the MOP parameter in AOA. This modification significantly enhances the algorithm's global search capability. The math optimizer accelerated (MOA) function is a crucial parameter for controlling the search mode selection in AOA. By incorporating hyperbolic tangent fluctuation into the variation trend of MOA, the exploration ability of the algorithm is enhanced. To achieve the Pareto optimal solution with extensive coverage, the non-dominated solutions are initially stored in a repository. Subsequently, the roulette mechanism is employed to select a leader from the repository for position update. Ultimately, this process yields a set of Pareto optimal solutions with relatively uniform distribution. The effectiveness of the proposed algorithm is verified through simulation experiments with 20 multi-objective benchmark functions, and the optimal improved strategy is selected among four improvement methods. It is also the best in comparison with other multi-objective algorithms. Subsequently, simulation experiments are conducted by using three CEED cases with varying sizes (6 units 1200 MW, 10 units 2000 MW and 40 units 10500 MW), wherein the objectives of fuel cost and pollutant emission are compared with methodologies employed in existing literature. The results show that the proposed algorithm is superior to other algorithms in solving CEED problems, and

关键词： Multi-objective optimization Combined economic emission dispatch Random search strategy arithmetic optimization algorithm

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