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Enhancing forest optimization algorithm with gravitational search for nonlinear continuous optimization

INTERNATIONAL JOURNAL OF GENERAL SYSTEMS 2024年第7-8期53卷 971-1013页

作者： Farzi-Veijouyeh, Najibeh Matin, Neda Sahargahi, Vahideh Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran Islamic Azad Univ Dept Comp Engn Shabestar Branch Shabestar Iran

Meta-heuristic algorithms have great role in solving problems related to optimization. Meta-heuristic method cannot solve problems related to optimization due to No Free Lunch theory. Hence different optimization methods are proposed by various researchers each year in order to solve optimization problems. forest optimization algorithm (FOA) is an evolutionary optimization algorithm that is appropriate for continuous nonlinear optimization problems. The algorithm drawbacks include entrapment in local optimum and failure in achieving global optimum. The paper proposes hybrid algorithm called FOAGSA, in which the Gravitational Search algorithm (GSA) is employed to improve the FOA performance in order to solve nonlinear continuous problems. The FOAGSA was evaluated through 39 benchmark optimization functions and two engineering problems. The experimental results proved that the FOAGSA exhibited acceptable results compared to state-of-art and well-known Meta-heuristic algorithms. Friedman ranking algorithm was utilized to compare FOAGSA with existing methods. The FOAGSA was ranked first on that basis.

关键词： optimization metaheuristic algorithms forest optimization algorithm Gravitational Search algorithm hybrid algorithm

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Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm

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Journal of Control and Decision 2024年第1期11卷 36-49页

作者： Tahere Motedayen Mahdi Yaghoobi Maryam Kheirabadi Department of Computer Engineering Neyshabur BranchIslamic Azad UniversityNeyshaburIran

In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart *** monitor their data in the form of fog computing(mobile mass monitoring).Service providers are required to pay user rewards without increasing platform *** of the NP-Hard methods to maximise the coverage rate and reduce the platform costs(reward)is the Cooperative Based Method for Smart Sensing Tasks(CMST).This article uses chaos theory and fuzzy parameter setting in the forest optimisation *** proposed method is implemented with *** average findings show that the network coverage rate is 31%and the monitoring cost is 11%optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic *** using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.

关键词： Internet of Things mobile mass monitoring forest optimization algorithm chaos theory fuzzy system

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An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

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CIRCUIT WORLD 2023年第3期49卷 269-280页

作者： Wu, Minning Zhang, Feng Rui, X. Yulin Univ Sch Informat Engn Yulin Shaanxi Peoples R China State Grid Hebei Informat & Telecommun Branch Shijiazhuang Hebei Peoples R China

Purpose Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm's success in complex problems, the authors used this algorithm to allocate resources in IoT. Design/methodology/approach For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm. Findings Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of "energy" and "delay," is better than other ones (GA, PSO and distance-based algorithm). Practical implications The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practica

关键词： Energy consumption Delay IoT forest optimization algorithm

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Advanced SLM scheme based on discrete forest optimization algorithm for PAPR minimization in UFMC waveform

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WIRELESS NETWORKS 2021年第2期27卷 1353-1368页

作者： Taspinar, Necmi Simsir, Sakir Erciyes Univ Dept Elect & Elect Engn Kayseri Turkey Nevsehir Haci Bektas Veli Univ Dept Elect & Elect Engn Nevsehir Turkey

Inherent multicarrier transmission mechanism of the universal filtered multicarrier (UFMC) waveform engenders the problem of high peak-to-average power ratio (PAPR). Since it is impossible for a nonlinear high power amplifier (HPA) to execute a distortionless amplification unless the PAPR of transmission signal is below an acceptable level, eliminating the aforementioned PAPR drawback in UFMC waveform is so critical for smooth communication. With this in mind, we developed a new selective mapping (SLM) scheme based on discrete forest optimization algorithm (DFOA) for the UFMC waveform. The related scheme was created by embedding the DFOA into the conventional SLM with the intention of optimizing the values of phase factors by which the phase rotation process is carried out in frequency domain to reduce the PAPR of eventual time domain signal attained from the SLM output. It is confirmed via the simulations that, remarkable PAPR improvements are achieved through the DFOA-SLM scheme in the UFMC signal thanks to the DFOA-supported search for the optimal sequence of phase factors instead of classical random search strategy inherent in the conventional SLM method.

关键词： forest optimization algorithm Discrete forest optimization algorithm PAPR UFMC SLM 5G

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A novel multi-objective forest optimization algorithm for wrapper feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 175卷 114737-114737页

作者： Nouri-Moghaddam, Babak Ghazanfari, Mehdi Fathian, Mohammad Iran Univ Sci & Technol Dept Ind Engn Tehran *** Iran

Feature selection is one of the important techniques of dimensionality reduction in data preprocessing because datasets generally have redundant and irrelevant features that adversely affect the performance and complexity of classification models. Feature selection has two main objectives, i.e., reducing the number of features and increasing classification performance due to its inherent nature. In this paper, we propose a multi-objective feature selection algorithm based on forest optimization algorithm (FOA) using the archive, grid, and regionbased selection concepts. For this purpose, two versions of the proposed algorithm are developed using continuous and binary representations. The performance of the proposed algorithms is investigated on nine UCI datasets and two microarray datasets. Next, the obtained results are compared with seven traditional singleobjective and five multi-objective methods. Based on the results, both proposed algorithms have reached the same performance or even outperformed the single-objective methods. Compared with other multi-objective algorithms, MOFOA with continuous representation has managed to reduce the classification error in most cases by selecting less number of features than other methods.

关键词： Feature selection Multi-objective optimization forest optimization algorithm Wrapper method Dimension reduction

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Feature selection using forest optimization algorithm based on contribution degree

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INTELLIGENT DATA ANALYSIS 2018年第6期22卷 1189-1207页

作者： Ma, Tinghuai Jia, Dongdong Zhou, Honghao Xue, Yu Cao, Jie Nanjing Univ Informat Sci & Technol CICAEET Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Econ & Management Nanjing 210044 Jiangsu Peoples R China

As a combinatorial optimization problem, feature selection has been widely used in machine learning and data mining In this paper, a feature selection method using forest optimization algorithm based on contribution degree is proposed. The proposed method uses a contribution degree strategy which is embedded in forest optimization algorithm. The goal of the contribution degree is to guide the search process of the forest optimization algorithm to select features according to high class correlation and low redundancy between features. The proposed algorithm is verified on some data sets from the UCI repository and the experiments show that the proposed method improves the classification accuracy compared with some other methods.

关键词： Feature selection forest optimization algorithm contribution degree

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A MODERN APPROACH FOR LOAD BALANCING USING forest optimization algorithm 2

A MODERN APPROACH FOR LOAD BALANCING USING FOREST OPTIMIZATI...

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2nd International Conference on Computing Methodologies and Communication (ICCMC)

作者： Mohapatral, Subasish Aryendu, Ishan Panda, Anshuman Padhi, Aswini Kumar Coll Engn & Technol Dept Comp Sci & Engn Bhubaneswar Odisha India

ISBN: (纸本)9781538634523

Load Balancing plays a dynamic role in keeping the tempo of the Cloud Computing framework. This research paper proposes a forest optimization algorithm for load balancing in distributed computing structure. This depends on the conduct of the trees in the forest and uses the seed dispersal strategies for streamlining the makespan, which results in improved average response time and the total execution time. The simulation results prove that the proposed algorithm gives better outcomes than its counterpart load balancing algorithms.

关键词： Load balancing Cloud Computing forest optimization algorithm makespan average response time total execution time

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A Novel Metaheuristic Based Method for Software Mutation Test Using the Discretized and Modified Forrest optimization algorithm

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JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 2023年第3期39卷 347-370页

作者： Arasteh, Bahman Gharehchopogh, Farhad Soleimanian Gunes, Peri Kiani, Farzad Torkamanian-Afshar, Mahsa Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Islamic Azad Univ Dept Comp Urmia Branch Orumiyeh Iran Yildiz Tech Univ Comp Educ & Instructional Technol Istanbul Turkiye Fatih Sultan Mehmet Vakif Univ Fac Engn Comp Engn Dept TR-34445 Istanbul Turkiye Nisantasi Univ Fac Engn & Architecture Comp Engn Dept TR-34398 Istanbul Turkiye

The number of detected bugs by software test data determines the efficacy of the test data. One of the most important topics in software engineering is software mutation testing, which is used to evaluate the efficiency of software test methods. The syntactical modifications are made to the program source code to make buggy (mutated) programs, and then the resulting mutants (buggy programs) along with the original programs are executed with the test data. Mutation testing has several drawbacks, one of which is its high computational cost. Higher execution time of mutation tests is a challenging problem in the software engineering field. The major goal of this work is to reduce the time and cost of mutation testing. Mutants are inserted in each instruction of a program using typical mutation procedures and tools. Meanwhile, in a real-world program, the likelihood of a bug occurrence in the simple and non-bug-prone sections of a program is quite low. According to the 80-20 rule, 80 percent of a program's bugs are discovered in 20% of its fault-prone code. The first stage of the suggested solution uses a discretized and modified version of the Forrest optimization algorithm to identify the program's most bug-prone paths;the second stage injects mutants just in the identified bug-prone instructions and data. In the second step, the mutation operators are only injected into the identified instructions and data that are bug-prone. Studies on standard benchmark programs have shown that the proposed method reduces about 27.63% of the created mutants when compared to existing techniques. If the number of produced mutants is decreased, the cost of mutation testing will also decrease. The proposed method is independent of the platform and testing tool. The results of the experiments confirm that the use of the proposed method in each testing tool such as Mujava, Muclipse, Jester, and Jumble makes a considerable mutant reduction.

关键词： Software mutation test Bug-prone codes forest optimization algorithm Mutation score

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A novel bio-inspired hybrid multi-filter wrapper gene selection method with ensemble classifier for microarray data

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NEURAL COMPUTING & APPLICATIONS 2023年第16期35卷 11531-11561页

作者： Nouri-Moghaddam, Babak Ghazanfari, Mehdi Fathian, Mohammad Iran Univ Sci & Technol Dept Ind Engn Tehran *** Iran

Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior per

关键词： Gene selection DNA microarray data Hybrid method Multi-filter Multi-objective wrapper forest optimization algorithm Ensemble classification

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Generating the structural graph-based model from a program source-code using chaotic forrest optimization algorithm

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EXPERT SYSTEMS 2023年第6期40卷 e13228-e13228页

作者： Arasteh, Bahman Ghanbarzadeh, Reza Gharehchopogh, Farhad Soleimanian Hosseinalipour, Ali Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Southern Cross Univ Fac Sci & Engn Gold Coast Australia Islamic Azad Univ Dept Comp Engn Urmia Branach Orumiyeh Iran Islamic Azad Univ Dept Comp Engn Heris Branch Heris Iran

One of the most important and costly stages in software development is maintenance. Understanding the structure of software will make it easier to maintain it more efficiently. Clustering software modules is thought to be an effective reverse engineering technique for deriving structural models of software from source code. In software module clustering, the most essential objectives are to minimize connections between produced clusters, maximize internal connections within created clusters, and maximize clustering quality. Finding the appropriate software system clustering model is considered an NP-complete task. The previously proposed approaches' key limitations are their low success rate, low stability, and poor modularization quality. In this paper, for optimal clustering of software modules, Chaotic based heuristic method using a forest optimization algorithm is proposed. The impact of chaos theory on the performance of the other SFLA-GA and PSO-GA has also been investigated. The results show that using the logistic chaos approach improves the performance of these methods in the software-module clustering problem. The performance of chaotic based FOA, SFLA-GA and PSO-GA is superior to the other heuristic methods in terms of modularization quality and stability of the results.

关键词： forest optimization algorithm modularization quality module dependency graph software modules clustering

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