The Arithmetic Optimization Algorithm

Abualigah, Laith; Diabat, Ali; Mirjalili, Seyedali; Elaziz, Mohamed Abd; Gandomi, Amir H.

doi:10.1016/j.cma.2020.113609

Cited by 1,630 publications

(552 citation statements)

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“…The principle of the basic model of the AOA is briefly discussed in this section. This algorithm was proposed in [24], which is motivated by the use of arithmetic operators to solve mathematical problems. The arithmetic operators, such as multiplication, division, subtraction, and addition, are utilized in scientific optimization to find the best solution subjected to specific criteria from some set of candidate solutions.…”

Section: A Basic Version Of Arithmetic Optimization Algorithm (Aoa)mentioning

confidence: 99%

“…The best solutions replace the worst solution in the population, and the duplicate solution is replaced by randomly generated solutions following a greedy selection process. For further information on four-phase arithmetic operators, please refer to [24]. The pseudocode of the AOA can be detailed in Fig.…”

Section: A Basic Version Of Arithmetic Optimization Algorithm (Aoa)mentioning

confidence: 99%

“…This theory provides guidelines for researchers to implement new algorithms or improve existing algorithms to achieve enhanced efficiency. These are indeed the reasons behind the new research described in this paper, in which a Multiobjective Arithmetic Optimization (MOAOA) focused on the newly published Arithmetic Optimization Algorithm (AOA) proposed by Abualigah et al [24] in 2021 that employs the distribution of leading arithmetic operator's behavior in mathematics. When working with MOAOA, one of the most important questions is why this algorithm needs to be applied for a constraint optimization problem.…”

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A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

Jangir²,

et al. 2021

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In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different areas, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist nondominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SP), Inverse Generalized Distance (IGD), Runtime (RT), and Generalized Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems. INDEX TERMS Arithmetic Optimization Algorithm (AOA); CEC-2021 real-world problems; Constrained optimization; Multi-Objective Arithmetic Optimization Algorithm (MOAOA). I. INTRODUCTIONRecently, computer technology advancements have increased the quality of addressing complex problems and decreased the time and cost of producing the optimal solution. However, human input is yet needed to determine the best of different solutions. Significant efforts can be seen in the literature to produce a system that optimally solves the given problem without any human effort [1]. One of the most reliable methods to accomplish this depends on optimization techniques. In many instances, most engineering problems, such as city programming, program management, investment decision, control system design, engineering

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Section: A Basic Version Of Arithmetic Optimization Algorithm (Aoa)mentioning

confidence: 99%

Section: A Basic Version Of Arithmetic Optimization Algorithm (Aoa)mentioning

confidence: 99%

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confidence: 99%

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A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

Jangir²,

et al. 2021

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“…The optimization techniques have been gaining a greater demand in recent decades due to the need for reliable and effective methods to deal with real-life problems that present increased complexity [1]. In addition to this fact, the increasing number of optimization problems of different fields has led the optimization techniques to be one of the major research areas [2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization

Dundar¹,

İzci²,

Ekinci³

et al. 2021

DÜMF Mühendislik Dergisi

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This paper aims to improve one of the recently proposed metaheuristic approaches known as Lévy flight distribution (LFD) algorithm by adopting a well-known simplex search algorithm named Nelder-Mead (NM) method. Three new strategies were utilized to demonstrate the improved capability of the original LFD algorithm. In the first strategy, NM was run twice as much the number of iterations of LFD after the latter completes its task. In the second strategy, NM was applied after each iterations of LFD instead of waiting for the completion of the latter. Lastly, in the third strategy, NM was applied after each iterations of LFD and run for the total number of current iterations of the latter algorithm. Well-known unimodal and multimodal benchmark functions were adopted, and statistical analysis was performed for performance evaluation. Further assessment was carried out through a nonparametric statistical test. The obtained results have shown the proposed versions of LFD algorithm provide significant performance improvement in general. In addition, the efficiency of the third strategy was found to be better for NM modified LFD algorithm which has greater balance between global and local search stages and can be used as an effective tool for function optimization.

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“…This motivates us to use metaheuristic algorithm for feature selection. Here, we propose to use Arithmetic Optimization Algorithm (AOA) (Abualigah et al 2021), a recently proposed metaheuristic algorithm, for the task of feature selection. AOA has been proved to be superior as compared to other metaheuristic algorithms in benchmark tests but it is aimed for continuous-valued problems.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Osteosarcoma Based on Integrated Features and Feature Selection Using Binary Arithmetic Optimization Algorithm

Bansal

Gehlot

Singhal

2021

Preprint

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Osteosarcoma is one of the most common malignant bone tumor mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify images obtained from different sources. Here, we propose an automatic detection system Integrated Features-Feature Selection Model for Classification (IF-FSM-C) that detect osteosarcoma from the high-resolution whole slide images (WSIs). The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted feature extraction techniques and deep learning models. It is quite possible that the integrated features may contain some redundant and irrelevant features which may unnecessarily increases the computation time and leads to wastage of resources. To avoid this, we perform feature selection (FS) before giving the integrated features to the classifier. To perform feature selection, we propose two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed and the results prove the superiority of the proposed IF-FSM-C that uses integrated features and feature selection in classifying WSIs as compared to the classifiers which use handcrafted or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection.

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The Arithmetic Optimization Algorithm

Cited by 1,630 publications

References 58 publications

A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization

Automatic Detection of Osteosarcoma Based on Integrated Features and Feature Selection Using Binary Arithmetic Optimization Algorithm

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