GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Dehghani, Mohammad; Montazeri, Zeinab; Hubálovský, Štěpán

doi:10.3390/math9111190

Cited by 17 publications

(7 citation statements)

References 25 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 2014 ) 248 Group Counseling Optimization (GCO) Eita and Fahmy ( 2014 ) 249 Group Escape Behavior (GEB) Min and Wang ( 2011 ) 250 Group Leaders Optimization Algorithm (GIOA) Daskin and Kais ( 2011 ) 251 Group Mean-Based Optimizer (GMBO) Dehghani et al. ( 2021 ) 252 Group Search Optimizer (GSO) He et al. ( 2009 ) 253 Group Teaching Optimization Algorithm (GTOA) Zhang and Jin ( 2020 ) 254 Harmony Element Algorithm (HEA) Cui et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

View full text Add to dashboard Cite

As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

show abstract

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

View full text Add to dashboard Cite

show abstract

“…Grey Wolf Optimization (GWO) is a swarm-based method based on simulating gray wolves' hierarchical strategy during hunting [30]. Some other swarm-based algorithms are Penicillium Reproduction Algorithm (PRA) [31], Dandelion Algorithm (DA) [32], Pelican Optimization Algorithm (POA) [33], Emperor Penguin Optimizer (EPO) [34], Marine Predators Algorithm (MPA) [35], Rat Swarm Optimization (RSO) [36], Mutated Leader Algorithm (MLA) [37], Reptile Search Algorithm (RSA) [38], Cat and Mouse Based Optimizer (CMBO) [39], Donkey Theorem Optimization (DTO) [40], All Member Based Optimizer (AMBO) [41], Group Mean-Based Optimizer (GMBO) [42], Tunicate Swarm Algorithm (TSA) [43], Two Stage Optimization (TSO) [44], White Shark Optimizer (WSO) [45], and African Vultures Optimization Algorithm (AVOA) [46]. Evolutionary-based metaheuristic algorithms are developed inspired by the concepts of biological, genetics sciences, and natural selection.…”

Section: Literature Reviewmentioning

confidence: 99%

Migration Algorithm: A New Human-Based Metaheuristic Approach for Solving Optimization Problems

Dehghani¹

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

This paper introduces a new metaheuristic algorithm called Migration Algorithm (MA), which is helpful in solving optimization problems. The fundamental inspiration of MA is the process of human migration, which aims to improve job, educational, economic, and living conditions, and so on. The mathematical modeling of the proposed MA is presented in two phases to empower the proposed approach in exploration and exploitation during the search process. In the exploration phase, the algorithm population is updated based on the simulation of choosing the migration destination among the available options. In the exploitation phase, the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions. MA's performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite. In addition, MA's results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach's high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems. The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions. Also, the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications.

show abstract

“…In order to enhance the overall efficiency of the GCNN model, the GMBO algorithm is utilized to adjust the GCNN parameters appropriately. GMBO is a population-based optimization method proposed earlier based on efficiently using population member data when upgrading a model [18]. In all the iterations, two groups of members are chosen carefully, such as the bad group members and the good group members, with a specific number of members in each group.…”

Section: Hyperparameter Optimization Using Gmbo Algorithmmentioning

confidence: 99%

Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

Alsubaei¹,

Alshahrani²,

Tarmissi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Cybersecurity has become the most significant research area in the domain of the Internet of Things (IoT) owing to the ever-increasing number of cyberattacks. The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process. Furthermore, Android malware is increasing on a daily basis. So, precise malware detection analytical techniques need a large number of hardware resources that are significantly resource-limited for mobile devices. In this research article, an optimal Graph Convolutional Neural Network-based Malware Detection and classification (OGCNN-MDC) model is introduced for an IoT-cloud environment. The proposed OGCNN-MDC model aims to recognize and categorize malware occurrences in IoT-enabled cloud platforms. The presented OGCNN-MDC model has three stages in total, such as data pre-processing, malware detection and parameter tuning. To detect and classify the malware, the GCNN model is exploited in this work. In order to enhance the overall efficiency of the GCNN model, the Group Mean-based Optimizer (GMBO) algorithm is utilized to appropriately adjust the GCNN parameters, and this phenomenon shows the novelty of the current study. A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model. A comprehensive comparison study was conducted, and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

show abstract

GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Cited by 17 publications

References 25 publications

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Migration Algorithm: A New Human-Based Metaheuristic Approach for Solving Optimization Problems

Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

Contact Info

Product

Resources

About