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Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data

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JOURNAL OF SUPERCOMPUTING 2022年第13期78卷 15598-15637页

作者： Pashaei, Elham Pashaei, Elnaz Istanbul Gelisim Univ Dept Comp Engn Istanbul Turkey Istanbul Aydin Univ Dept Software Engn Istanbul Turkey

Gene expression data play a significant role in the development of effective cancer diagnosis and prognosis techniques. However, many redundant, noisy, and irrelevant genes (features) are present in the data, which negatively affect the predictive accuracy of diagnosis and increase the computational burden. To overcome these challenges, a new hybrid filter/wrapper gene selection method, called mRMR-BAOAC-SA, is put forward in this article. The suggested method uses Minimum Redundancy Maximum Relevance (mRMR) as a first-stage filter to pick top-ranked genes. Then, Simulated Annealing (SA) and a crossover operator are introduced into Binary arithmetic optimization algorithm (BAOA) to propose a novel hybrid wrapper feature selection method that aims to discover the smallest set of informative genes for classification purposes. BAOAC-SA is an enhanced version of the BAOA in which SA and crossover are used to help the algorithm in escaping local optima and enhancing its global search capabilities. The proposed method was evaluated on 10 well-known microarray datasets, and its results were compared to other current state-of-the-art gene selection methods. The experimental results show that the proposed approach has a better performance compared to the existing methods in terms of classification accuracy and the minimum number of selected genes.

关键词： Cancer classification Feature selection arithmetic optimization algorithm Gene selection optimization

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Chaotic quasi-oppositional arithmetic optimization algorithm for thermo-economic design of a shell and tube condenser running with different refrigerant mixture pairs

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NEURAL COMPUTING & APPLICATIONS 2022年第10期34卷 8103-8135页

作者： Turgut, Mert Sinan Turgut, Oguz Emrah Abualigah, Laith Ege Univ Fac Engn Dept Mech Engn Izmir Turkey Izmir Bakircay Univ Fac Engn & Architecture Dept Ind Engn Izmir Turkey Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan

This theoretical research study proposes a novel Chaotic Quasi-Oppositional arithmetic optimization algorithm (COAOA) for thermo-economic optimization of a shell and tube condenser working with refrigerant mixtures. arithmetic optimization algorithm (AOA) is a recently emerged metaheuristic algorithm considering different mathematical operators to optimize the candidate solutions over a wide range of search domains. The effectiveness the COAOA is assessed by applying it to a set of benchmark optimization problems and comparing the obtained solutions with that of the original AOA and its quasi-oppositional variant. The COAOA has been employed to acquire the minimum value of the total annual cost of the shell and tube condenser by iteratively varying nine decision variables of mass flow rate, shell diameter, the tube inside diameter, tube length, number of tube passes, tube layout, tube pitch ratio, the total number of baffles, and diameter ratio. Three different case studies are solved using different refrigerant pairs used for in-tube flow to show the proposed metaheuristic optimizer's efficiency and effectivity on real-world mixed-integer optimization problem. Optimal results retrieved for different mixture pairs with varying mass fractions are compared with each other, and parametric configuration yielding the minimum total cost is decided. Finally, a comprehensive sensitivity analysis is performed to investigate the influences of the design variables over the considered problem objective. Overall analysis results indicate that COAOA can be an excellent optimizer to obtain a shell and tube condenser's optimal configuration within a reasonable computation time.

关键词： Shell and Tube Heat Exchangers arithmetic optimization algorithm Condensers Metaheuristics

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Intelligent Intrusion Detection System using Enhanced arithmetic optimization algorithm with Deep Learning Model

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2023年第4期30卷 1217-1224页

作者： Kavitha, S. Maheswari, N. Uma Venkatesh, R. Velammal Coll Engn & Technol Dept Comp Sci & Engn Madurai India PSNA Coll Engn & Technol Dept Comp Sci & Engn Dindigul India PSNA Coll Engn & Technol Dept Informat Technol Dindigul India

The widespread use of interoperability and interconnectivity of computing systems is becoming indispensable for enhancing our day-to-day actions. The susceptibilities deem cyber-security systems necessary for assuming communication interchanges. Secure transmission needs security measures for combating the threats and required developments to security measures that counter evolving security risks. Though firewalls were devised to secure networks, in real-time they cannot detect intrusions. Hence, destructive cyber-attacks put forward severe security complexities, requiring reliable and adaptable intrusion detection systems (IDS) that could monitor unauthorized access, policy violations, and malicious activity practically. Conventional machine learning (ML) techniques were revealed for identifying data patterns and detecting cyber-attacks IDSs successfully. Currently, deep learning (DL) methods are useful for designing accurate and effective IDS methods. In this aspect, this study develops an intelligent IDS using enhanced arithmetic optimization algorithm with deep learning (IIDS-EAOADL) method. The presented IIDS-EAOADL model performs data standardization process to normalize the input data. Besides, equilibrium optimizer based feature selection (EOFS) approach is developed to elect an optimal subset of features. For intrusion detection, deep wavelet autoencoder (DWAE) classifier is applied. Since the proper tuning of parameters of the DWNN is highly important, EAOA algorithm is used to tune them. For assuring the simulation results of the IIDS-EAOADL technique, a widespread simulation analysis takes place using a benchmark dataset. The experimentation outcomes demonstrate the improvements of the IIDS-EAOADL model over other existing techniques

关键词： arithmetic optimization algorithm deep learning feature selection intrusion detection security

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Fusion of modern meta-heuristic optimization methods using arithmetic optimization algorithm for global optimization tasks

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SOFT COMPUTING 2022年第14期26卷 6749-6763页

作者： Mahajan, Shubham Abualigah, Laith Pandit, Amit Kant Al Nasar, Mohammad Rustom Alkhazaleh, Hamzah Ali Altalhi, Maryam Shri Mata Vaishno Devi Univ Sch Elect & Commun Katra 182320 Jammu & Kashmir India Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia ALDAR Univ Coll Sch Engn & Technol Dept Informat Technol Garhoud Dubai U Arab Emirates Univ Dubai IT Dept Coll Engn & IT Academic City U Arab Emirates Taif Univ Coll Business Adm Dept Management Informat Syst POB 11099 At Taif 21944 Saudi Arabia

Several population-based techniques have subsequently been proposed. Despite their broad use in a variety of applications, we are still investigating the use of proposed methods to tackle real-world challenges. As a result, researchers must considerably modify and enhance their approaches based on the primary evolutionary processes in order to achieve rapid convergence, consistent equilibrium with high-quality data, and optimization. The paper proposes a fusion method (AOA-GOA) meta-heuristic optimization methods for global optimization tasks. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design, etc. The method fusion proposed is analyzed in context with GOA and AOA. To evaluate the performance, each method is tested on the same parameters like population size and number of iteration. The proposed IAOA is evaluated by varying the dimensions. The impact of varying dimensions is a standard test used in previous studies for optimizing test functions that show the effect of varying dimensions on efficiency of IAOA. From this, it is noted that it works efficiently for both high and low dimensional problems. In high dimensional problem, the proposed method gives efficient search results.

关键词： arithmetic optimization algorithm Grasshopper optimization algorithm Metaheuristic optimization methods

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LMRAOA: An improved arithmetic optimization algorithm with multi-leader and high-speed jumping based on opposition-based learning solving engineering and numerical problems

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ALEXANDRIA ENGINEERING JOURNAL 2022年第12期61卷 12367-12403页

作者： Zhang, Yu-Jun Wang, Yu-Fei Yan, Yu-Xin Zhao, Juan Gao, Zheng-Ming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Acad Arts Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

This paper proposes an improved variant of the arithmetic optimization algorithm (AOA), called LMRAOA, which is used to solve numerical and engineering problems. Various strategies are proposed to improve AOA. First, Multi-Leader Wandering Around Search Strategy (MLWAS) is proposed to improve the exploration ability of the algorithm on global scale. Then, Random High-Speed Jumping Strategy (RHSJ) is proposed, and the search agent performs high-speed search in the current neighborhood to improve the exploitation ability. Finally, in order to avoid local optima, adaptive lens opposition-based learning strategy is proposed, and linear changes are proposed in its parameters to further satisfy the dynamic changes. 27 classic benchmark functions, 6 engineering optimization problems, and CEC2014, CEC2019 and CEC2020 competition functions are tested by LMRAOA algorithm and comparison algorithm. The experimental results show that, in most cases, the LMRAOA outperforms other algorithms in solving engineering and numerical problems, and can provide effective solutions. (C) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University

关键词： arithmetic optimization algorithm LMRAOA Classic benchmark functions Engineering optimization problems CEC test function

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A localization and deployment model for wireless sensor networks using arithmetic optimization algorithm

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第3期15卷 1473-1485页

作者： Bhat, Soumya J. Santhosh, K., V Manipal Acad Higher Educ Ctr Cyber Phys Syst Manipal Inst Technol Dept Instrumentat & Control Engn Manipal 576104 India

With the advancement of sensor technologies, Wireless Sensor Networks (WSN) are envisioning a rich variety of promising services in many fields. WSN is formed by the deployment of sensor nodes in the regions of interest using a deterministic or random deployment strategy. The random deployment strategy is more suitable in large monitoring areas and harsh environments. But, in this type of deployment, coverage holes and disconnected networks can exist. Important events may get unnoticed reducing the reliability of the networks. Since the locations of nodes are unknown in a randomly deployed WSN, it is difficult to locate the holes. For this, we are proposing a localization and deployment model. The localization algorithm uses arithmetic optimization algorithm (AOA) and the results of this algorithm are further used to develop a deployment model to achieve a completely connected network. This algorithm is tested in various fields. The algorithm is able to localize nodes accurately and identify the coverage holes with an error rate of less than 0.27% when the Average Localization Error (ALE) is within 5m.

关键词： arithmetic optimization algorithm Deployment Heterogeneous Range free localization Wireless sensor networks

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A Novel Enhanced arithmetic optimization algorithm for Global optimization

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IEEE ACCESS 2022年 10卷 75040-75062页

作者： Zhang, Jinzhong Zhang, Gang Huang, Yourui Kong, Min West Anhui Univ Sch Elect & Optoelect Engn Luan 237012 Peoples R China

The arithmetic optimization algorithm (AOA) is based on the distribution character of the dominant arithmetic operators and imitates addition (A), subtraction (S), multiplication (M) and division (D) to find the global optimal solution in the entire search space. However, the basic AOA has some drawbacks of premature convergence, easily falls into a local optimal value, slow convergence rate, and low calculation precision. To improve the overall optimization ability and overcome the drawbacks of the basic AOA, an enhanced AOA (EAOA) based on the Levy variation and the differential sorting variation is proposed to solve the function optimization and the project optimization. The Levy variation increases population diversity, broadens the optimization space, enhances the global search ability and improves the calculation precision. The differential sorting variation filters out the optimal search agent, avoids search stagnation, enhances the local search ability and accelerates the convergence rate. The EAOA realizes complementary advantages of the Levy variation and the differential sorting variation to avoid falling into the local optimum and the premature convergence. The sixteen benchmark functions and five engineering design projects are applied to verify the effectiveness and feasibility of the EAOA. The EAOA is compared with other algorithms by minimizing the fitness value, such as artificial bee colony, ant line optimizer, cuckoo search, dragongfly algorithm, moth-flame optimization, sine cosine algorithm, water wave optimization and arithmetic optimization algorithm. The experimental results show that the overall optimization ability of the EAOA is superior to that of other algorithms, the EAOA can effectively balance the exploration and the exploitation to obtain the best solution. In addition, the EAOA has a faster convergence rate, higher calculation precision and stronger stability.

关键词： arithmetic optimization algorithm Levy variation differential sorting variation benchmark function engineering design

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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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Sand cat arithmetic optimization algorithm for global optimization engineering design problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第6期10卷 2122-2146页

作者： Chen, Shuilin Zheng, Jianguo Donghua Univ Glorious Sun Sch Business & Management Shanghai 200051 Peoples R China

Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations - low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance. Graphical Abstract

关键词： sand cat swarm optimization arithmetic optimization algorithm exploration and exploitation hybrid algorithms

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The Hybrid Harris Hawks Optimizer-arithmetic optimization algorithm: A New Hybrid algorithm for Sizing optimization and Design of Microgrids

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IEEE ACCESS 2022年 10卷 19254-19283页

作者： Cetinbas, Ipek Tamyurek, Bunyamin Demirtas, Mehmet Eskisehir Osmangazi Univ Dept Elect & Elect Engn TR-26480 Eskisehir Turkey Gazi Univ Dept Elect & Elect Engn TR-06570 Ankara Turkey

This paper presents a new hybrid metaheuristic algorithm, the hybrid Harris Hawks Optimizer-arithmetic optimization algorithm (hHHO-AOA), as we have named it. It is proposed for sizing optimization and design of autonomous microgrids. The proposed hybrid algorithm has been developed based on operating the Harris Hawks Optimizer (HHO) and the arithmetic optimization algorithm (AOA) in a uniquely cooperative manner. The developed algorithm is expected to increase the solution accuracy by increasing the solution diversity during an optimization process. The performance is verified with the evaluation metrics and the well-known statistical tests. According to the Friedman ranking test, the new algorithm performs 77.9% better than HHO and 78.6% better than AOA. Similarly, the performance checked with the Wilcoxon signed-rank test has revealed a significant superiority in solution accuracy compared to HHO and AOA alone. Later, the hybrid algorithm is tested on a microgrid that consists of a photovoltaic (PV) system, a wind turbine (WT) system, a battery energy storage system (BESS), diesel generators (DGs), and a commercial type load. For the optimal capacity planning of these components, a problem in which the loss of power supply probability (LPSP) and the cost of energy (COE) are defined as the objective function is formulated. The optimization done by the proposed algorithm has produced the lowest LPSP and the COE along with the highest rate of renewable fraction (RF). In conclusion, it is demonstrated that the new hHHO-AOA has proved itself in designing reliable, economical, and eco-friendly autonomous microgrids in the best optimal way.

关键词： arithmetic optimization algorithm Harris hawks optimizer hybrid algorithm Friedman ranking test microgrid off-grid optimal capacity planning sizing optimization Wilcoxon signed rank test

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