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An arithmetic optimization algorithm based on opposition jumping rate for time cost trade-off optimization problems

Asian Journal of Civil Engineering 2025年第2期26卷 867-886页

作者： Sulub, Abdikarim Said Eirgash, Mohammad Azim Toğan, Vedat Civil Engineering Department Karadeniz Technical University Trabzon 61080 Turkey

Trade-off problem requires a balance between the project objectives taken as time and cost, known as the NP-hard optimization problem. Due to this, any metaheuristic algorithm like the arithmetic optimization algorithm (AOA) gaining popularity for its simplicity and fast convergence might suffer from finding the optimal solution(s) when the construction project scale is increasing. To improve the overall optimization ability and overcome the drawbacks of the plain AOA in solving the time–cost trade-off optimization problems, in this study, the generation jumping phase of the opposition-based learning strategy is proposed and integrated with AOA. This enhancement realizes complementary advantages of the opposition jumping rate to avoid falling into the local optimum and premature convergence. Construction engineering projects involving 63, 81, and 146 activities are applied to verify the effectiveness and feasibility of the enhanced AOA. The experimental results reveal that the proposed model is more effective than the plain AOA and other emerging algorithms for simultaneously optimizing the trade-off problems in construction management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： arithmetic optimization algorithm Jumping rate Opposition based learning Time–cost trade-off problem

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AOAFS: A Malware Detection System Using an Improved arithmetic optimization algorithm

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TECHNOLOGIES 2025年第4期13卷 145-145页

作者： Alshorman, Rafat Abed-alguni, Bilal H. Alqudah, Yaqeen E. Yarmouk Univ Dept Comp Sci Irbid 21110 Jordan

Malware detection datasets often contain a huge number of features, many of which are irrelevant, noisy, and duplicated. This issue diminishes the efficacy of Machine Learning models used for malware detection. Feature Selection (FS) is an approach commonly used to reduce the number of features in a malware detection dataset to a smaller set of features to facilitate the ease of the Machine Learning process. The arithmetic optimization algorithm (AOA) is a relatively new efficient optimization algorithm that can be used for FS. This work introduces a new malware detection system called the improved AOA method for FS (AOAFS) that enhances the performance of Machine Learning techniques for malware detection. The AOAFS contains three key enhancements. First, the K-means clustering method is used to improve the initial population of the AOAFS. Second, sixteen Binary Transfer Functions are applied to model the binary solution space for FS in the AOAFS. Finally, Dynamic Opposition-based Learning is utilized to improve the mutation capability of the AOAFS. Several malware datasets were used to compare the AOAFS to four popular Machine Learning algorithms and eight famous wrapper-based optimization algorithms. Statistically, the AOAFS was evaluated using the Friedman Test for ranking the tested algorithms, while the Wilcoxon Signed-Rank Test was employed for pairwise comparisons. The results indicated that the AOAFS achieves the highest classification accuracy with the smallest feature set across all datasets.

关键词： malware detection arithmetic optimization algorithm feature selection k-means clustering dynamic opposition-based learning

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An autoencoder-based arithmetic optimization clustering algorithm to enhance principal component analysis to study the relations between industrial market stock indices in real estate

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Yang, Cheng-Hong Lee, Borcy Lee, Yi-In Chung, Yu-Fang Lin, Yu-Da Tainan Univ Technol Dept Informat Management Tainan Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Kaohsiung Med Univ Ph D Program Biomed Engn Kaohsiung Taiwan Kaohsiung Med Univ Sch Dent Kaohsiung Taiwan Kaohsiung Med Univ Drug Dev & Value Creat Res Ctr Kaohsiung Taiwan Tunghai Univ Dept Int Business Taichung Taiwan Tunghai Univ Dept Elect Engn Taichung Taiwan Natl Penghu Univ Sci & Technol Dept Comp Sci & Informat Engn Penghu Taiwan

Traditional methods of forecasting and analyzing property trends using statistical analysis and questionnaires are limited;in particular, they are too slow to provide insights based on complex, extensive, and multidimensional real estate data. To address these challenges, we developed a sophisticated analytical pipeline integrating an autoencoder with an arithmetic optimization clustering algorithm (AOCA) and principal component analysis (PCA). An autoencoder was used to reduce the dimensionality of data sourced from public government databases to accelerate clustering. A tailored AOCA was then applied, and these clusters were examined with PCA to uncover deep relationships between different industrial market stock indices and real estate prices in Taiwan. We identified the following five critical clusters for 98 industrial market stock indices related to real estate from the Taiwan Stock Exchange between 2009 and 2020: 1) industrial and biomedical activity, 2) computers and computer peripherals, 3) shipping and transportation, 4) construction salaries, and 5) construction and economic activity. We performed a temporal clustering of these factors for in-depth insights. This granular insight into the factors driving house prices provides actionable information for policymakers. Our precise and focused pipeline allows researchers and government authorities to concentrate on the pivotal aspects of property development and market regulation. This approach paves the way for more targeted and effective interventions in the real estate market.

关键词： Machine learning Real estate arithmetic optimization algorithm Economy

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arithmetic optimization algorithm based on elementary function disturbance for solving economic load dispatch problem in power system

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APPLIED INTELLIGENCE 2022年第10期52卷 11846-11872页

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

The objective of economic load dispatch (ELD) is to minimize the overall cost of generation under the premises of meeting the load demand and satisfying different operational constraints. A new improved arithmetic optimization algorithm (AOA) based on elementary function disturbance was proposed to solve ELD problems. Math optimizer accelerated (MOA) and math optimizer probability (MOP) are two important parameters in the original AOA. Ten six elementary functions were adopted, which are sine function, tangent function, hyperbolic secant function, power function, hyperbolic cosecant function and arc-cosine function. Adding the disturbance generated by six elementary functions respectively on the original MOA and MOP parameters can effectively balance the exploration and exploitation ability of AOA, enhance the global search ability, avoid falling into local optimization, and improve the convergence speed of AOA. Then the original AOA was compared with 6 variants by using 23 benchmark functions in CEC-2005, and selected the best improved method. Next, some intelligent optimization algorithms with better performance in optimizing CEC-2005 benchmark functions are adopted to carry out the simulation experiments with the improved AOA to verify its superiority. Finally, three ELD problem cases including valve point effect, prohibited operation area, transmission loss and other factors are selected with a total demand of 1263 MW, 2500 MW and 10,500 MW respectively. The AOA based on elementary function disturbance was used to solve these three ELD problems. Experimental results show that the proposed AOA has the best performance in solving ELD problems.

关键词： Economic load dispatch arithmetic optimization algorithm Elementary function Valve point effect

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arithmetic optimization algorithm based MPPT technique for centralized TEG systems under different temperature gradients

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ENERGY REPORTS 2022年 8卷 2424-2433页

作者： Zhang, Rui Yang, Bo Chen, Nuo Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Yunnan Peoples R China China Southern Power Grid EHV Transmiss Co Kunming 650217 Yunnan Peoples R China

Since centralized thermoelectric power generation (TEG) system presents multiple local maximum power points (LMPPs) at different temperature gradients (DTG), thus its optimal power harvesting is difficult to realize via conventional approaches. Therefore, an efficient arithmetic optimization algorithm (AOA) is applied to realize maximum power point tracking (MPPT) of centralized thermoelectric power generation system at different temperature gradients to improve the energy exploitation and utilization. AOA is utilized to efficiently and reliably identify unique global maximum power point (GMPP) in multiple LMPPs, which replicates the distribution mechanism of main arithmetic operators during mathematic calculation to find the best solution from a set of randomly generated candidate solutions. Compared with other well-known algorithms, AOA owns the distinctive superiorities of simple implementation structure, as well as few control parameters need to be tuning. Meanwhile, its own random and adaptive parameters selection principle greatly boost the AOA convergence performance. In addition, AOA shows strong ability to avoid falling into local optima thanks to its proper balancing between global exploration (diversification) and local exploitation (intensification). For further validation of the practicability of AOA based MPPT, two case studies are conducted, e.g., start-up test and step change of temperature. Experimental results show that AOA can achieve the optimal energy harvesting with minimal power fluctuations in comparison with the other three optimization algorithms, e.g., the energy produced by AOA is 34.44%, 6.03%, and 8.17% higher than that of perturb and observe (P&O), particle swarm optimization (PSO) and grey wolf optimization (GWO) respectively under the step change of temperature. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Centralized thermoelectric generation system Maximum power point tracking arithmetic optimization algorithm Different temperature gradients

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arithmetic optimization algorithm Based on Cauchy Mutation Trigonometric Function Search to Solve Combined Economic Emission Dispatch Problem

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IEEE ACCESS 2023年 11卷 136885-136909页

作者： Li, Yun-Peng Zhang, Lin-Xuan Feng, Zheng-Jie Xinjiang Univ Sch Elect Engn Urumqi 830049 Xinjiang Peoples R China Zhejiang Jinhua Lingsheng Electron Co Ltd Jinhua 311400 Zhejiang Peoples R China

Nowadays, problems such as greenhouse effect and air pollution become increasingly prominent. The power generation process should pay attention to the fuel cost and reduce the pollution to the environment. In this paper, an arithmetic optimization algorithm (AOA) based on Cauchy mutation trigonometric function search is proposed to solve the combined economic emission dispatch (CEED) problem. The price penalty factor is used to convert multiple objective functions of CEED problem into one objective. The math optimization probability (MOP) parameter is used to control the position update of the AOA. MOP is replaced by the oscillation coefficient and Cauchy mutation, which can not only guarantee the local search accuracy but also enhance the global search capability. 6 kinds of trigonometric function search operators, sine function, hyperbolic sine, inverse hyperbolic sine, hyperbolic tangent, inverse hyperbolic tangent, are used to replace the fixed value control parameters in AOA algorithm. The algorithm still has a certain ability to jump out of the local optimal in the late running. The 23 test functions are used to verify the effect of the improved AOA method. The AOA is compared with 6 kinds of improved AOA, and the improvement method with the best effect is selected, and then compared with other intelligent optimization algorithms to verify the effectiveness of the improved strategy. In addition to improving the algorithm, the constraint of CEED problem is also treated. Five kinds of random disturbance penalty functions are proposed, which are cosine function, hyperbolic sine function, tangent function, hyperbolic tangent function and V-type function. The CEED problem of 6 units was selected for simulation experiment under 4 different load demands. The experimental data show that the arithmetic optimization algorithm based on Cauchy mutation trigonometric function search has a good effect on solving CEED problems, and the random disturbance penalty strategy is

关键词： Combined economic emission dispatch Cauchy mutation random disturbance penalty function trigonometric function search arithmetic optimization algorithm

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arithmetic optimization algorithm for Optimal Installation of DSTATCOM into Distribution System based on Various Voltage Stability Indices 9

Arithmetic Optimization Algorithm for Optimal Installation o...

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9th International Conference on Modern Power Systems (MPS)

作者： Zellagui, Mohamed Lasmari, Adel Settoul, Samir El-Bayeh, Claude Ziad Chenni, Rachid Belbachir, Nasreddine Univ Batna 2 Dept Elect Engn Batna Algeria Mentouri Univ Constantine 1 Dept Electrotech Constantine Algeria Concordia Univ Canada Excellence Res Chairs Team Montreal PQ Canada Univ Mostaganem Dept Elect Engn Mostaganem Algeria

ISBN: (纸本)9781665433815

In this article is proposed a new approach based on an arithmetic optimization algorithm (AOA) that can notably envisage with diverse problems for optimal installation (locate and size) of Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). An approach applied for optimal installation of DSTATCOM units in EDS is used as the new multi-objective functions to minimize the Active Power Loss (APL) and various voltage stability indices. The voltage stability indices applied are the Voltage Stability Index (VSI), the Fast Voltage Stability Index (FVSI), the Line Stability Factor (LPQ), and the Power-Voltage Stability Index (PVSI). The methodology is tested on IEEE 33-, and 69-bus EDSs. The results of the simulation confirm that the optimal installation of DSTATCOM plays a significant role in EDS, where excellently reduced the power losses and enhanced the voltage profiles.

关键词： Electrical distribution system Distribution static compensator Optimal installation Voltage stability indices arithmetic optimization algorithm

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Hybrid arithmetic optimization algorithm for a new multi-warehouse joint replenishment and delivery problem under trade credit

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NEURAL COMPUTING & APPLICATIONS 2023年第10期35卷 7561-7580页

作者： Peng, Lu Wang, Lin Wang, Sirui Wuhan Univ Technol Sch Management Wuhan 430070 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China

Trade credit is a significant form of short-term financing in the real business situation. This study proposes a practical multi-warehouse joint replenishment and delivery (MJRD) problem under trade credit in accordance with the realistic situation. The goal of the MJRD is to find the reasonable basic replenishment cycle time, the joint replenishment frequency, the delivery frequency, and the assignment information of suppliers to minimize the total cost. Five intelligent algorithms, which include a differential evolution algorithm, genetic algorithm, adaptive hybrid differential evolution algorithm, arithmetic optimization algorithm (AOA), and hybrid arithmetic optimization algorithm (HAOA), are designed to find a solution to this MJRD problem under trade credit. The results of several experiments show that HAOA is effective in solving the proposed MJRD. Compared with AOA, the best improvement is 46.66%. HAOA is a satisfactory algorithm for the proposed MJRD under trade credit.

关键词： Joint replenishment and delivery problem Trade credit arithmetic optimization algorithm Differential evolution algorithm Genetic algorithm

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Advancements in arithmetic optimization algorithm: theoretical foundations, variants, and applications

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 37621-37664页

作者： Kumar, Vijay Dr B R Ambedkar Natl Inst Technol Dept Informat Technol Jalandhar India

arithmetic optimization algorithm (AOA) is a population-based metaheuristic algorithm that mimics the properties of primitive arithmetic operators. Researchers were attracted to AOA after its development in 2021. It has been extensively applied across several research domains. This study presents a thorough analysis of the AOA. The mathematical modelling of AOA and its inspiration are discussed. The variants of AOA namely improved, binary, chaotic, hybrid, and multi-objective are investigated in detail. The applications of AOA and its variants in numerous research domain are discussed. The possible research directions for AOA are examined. The young researchers will be assisted by this study in comprehending the fundamental ideas of AOA and in applying this knowledge to their own research issues.

关键词： arithmetic optimization algorithm Metaheuristic Population Exploration Exploitation

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Enhanced Flow Direction arithmetic optimization algorithm for mathematical optimization problems with applications of data clustering

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ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS 2022年第0期138卷 13-29页

作者： Abualigah, Laith Almotairi, Khaled H. Abd Elaziz, Mohamed Shehab, Mohammad Altalhi, Maryam Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Umm Al Qura Univ Comp Engn Dept Mecca Saudi Arabia Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Zagazig Univ Fac Sci Dept Math Zagazig Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates World Islamic Sci & Educ Univ Informat Technol Amman Jordan Taif Univ Coll Business Adm Dept Management Informat Syst POB 11099 Taif 21944 Saudi Arabia

Data clustering is one of the most common and challenging problems in the machine learning domain. It requires an efficient method to be addressed. This paper proposed a new version of the Flow Direction algorithm (FDA) to solve various optimization problems. The proposed method is called FDAOA, which enhanced the performance of the original Flow Direction algorithm by the arithmetic operators that have been used in the arithmetic optimization algorithm (AOA). The main aim of the proposed FDAOA is to avoid the recognized weaknesses in the original methods;stuck in the local area, premature convergence, and weak equilibrium between the exploration and exploitation search mechanisms. The proposed method is tested on two sets of various problems to validate its performance. In the first set, twenty-three benchmark functions are used, which belong to three categories;seven unimodal functions, six multimodal functions, and ten fixed dimension functions. In the second set, eight common data clustering problems are used to prove the ability of the proposed FDAOA to deal with real-world optimization problems. The results of the proposed method are compared with other well-established methods, and the proposed FDAOA achieved promising output compared to the other methods on various tested problems. The proposed method got the optimal clustering solutions almost in all the tested data clustering problems with clear significant improvements against the other comparative methods.

关键词： arithmetic optimization algorithm Flow Direction algorithm Meta-heuristics Mathematical benchmark problems Data clustering problems

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