Self-organizing migrating algorithm with clustering-aided migration and adaptive perturbation vector control

Kadavý, Tomáš; Pluháček, Michal; Viktorin, Adam; Šenkeřík, Roman

doi:10.1145/3449726.3463212

Cited by 5 publications

(3 citation statements)

References 11 publications

(12 reference statements)

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“…In 2021, the SOMA-CLP (Kadavy et al 2021a) was introduced. The main difference between this version and the SOMA-CL is the adaptation of the perturbation parameter based on the following equation:…”

Section: Control Parameters Pools and Adaptationmentioning

confidence: 99%

Self-organizing migrating algorithm: review, improvements and comparison

Skanderová

2022

Artif Intell Rev

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The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the publications. First, novel approaches contributing to the improvement of its performance. Second, solving the various optimization problems. Despite the different approaches and applications, there exists no work summarizing them. Therefore, this work reviews the research papers dealing with the principles and application of the SOMA. The second goal of this work is to provide additional information about the performance of the SOMA. This work presents the comparison of the selected algorithms. The experimental results indicate that the best-performing SOMAs provide competitive results comparing the recently published algorithms.

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Section: Control Parameters Pools and Adaptationmentioning

confidence: 99%

Self-organizing migrating algorithm: review, improvements and comparison

Skanderová

2022

Artif Intell Rev

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“…The following subsections cover the proposed modifications to the original SOMA-CLP. To improve the average algorithm performance on the CEC benchmark, all three modifications were suggested and discussed at the latest Genetic and Evolutionary Computation Conference, GECCO 2021 [5], where SOMA-CLP was firstly introduced as a CEC 2021 benchmark competition entry. The proposed modifications deal with the mechanisms of the cluster leader selection process for the third phase of the SOMA-CLP.…”

Section: Proposed Modificationsmentioning

confidence: 99%

“…The SOMA-CLP algorithm is a direct descendant of SOMA-CL [4]. SOMA-CLP [5] uses a linear adaptation of the prt control parameter, promoting the global transition from the tendency of exploration to exploitation. The workflow of the SOMA-CLP can be divided into three phases: search space mapping, clustering of the mapped space, and the exploitation by performing a more detailed screening of areas of interest discovered during the first phase.…”

Section: Introductionmentioning

confidence: 99%

On Modifications Towards Improvement of the Exploitation Phase for SOMA Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector Control

Kadavý

Viktorin

Pluháček

et al. 2021

2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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This paper represents the next step in the development of the recently proposed single objective metaheuristic algorithm -Self-Organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The CEC 2021 single objective boundconstrained optimization benchmark testbed was used for the performance evaluation of the modifications of the algorithm. The presented modifications were invoked by the results of CEC 2021 competition, where the SOMA-CLP ranked 7th out of 9 competing algorithms. This paper introduces three modifications of population organization process focusing on one particular phase of the SOMA-CLP algorithm aimed at exploitation. All results were compared and tested for statistical significance against the original variant using the Friedman rank test. The algorithm modification and analysis of the results presented here can be inspiring for other researchers working on the development and modifications of evolutionary computing techniques.

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Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and Challenges

Houssein,

Saeed,

et al. 2024

Arch Computat Methods Eng

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The greatest and fastest advances in the computing world today require researchers to develop new problem-solving techniques capable of providing an optimal global solution considering a set of aspects and restrictions. Due to the superiority of the metaheuristic Algorithms (MAs) in solving different classes of problems and providing promising results, MAs need to be studied. Numerous studies of MAs algorithms in different fields exist, but in this study, a comprehensive review of MAs, its nature, types, applications, and open issues are introduced in detail. Specifically, we introduce the metaheuristics' advantages over other techniques. To obtain an entire view about MAs, different classifications based on different aspects (i.e., inspiration source, number of search agents, the updating mechanisms followed by search agents in updating their positions, and the number of primary parameters of the algorithms) are presented in detail, along with the optimization problems including both structure and different types. The application area occupies a lot of research, so in this study, the most widely used applications of MAs are presented. Finally, a great effort of this research is directed to discuss the different open issues and challenges of MAs, which help upcoming researchers to know the future directions of this active field. Overall, this study helps existing researchers understand the basic information of the metaheuristic field in addition to directing newcomers to the active areas and problems that need to be addressed in the future.

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Self-organizing migrating algorithm with clustering-aided migration and adaptive perturbation vector control

Cited by 5 publications

References 11 publications

Self-organizing migrating algorithm: review, improvements and comparison

Self-organizing migrating algorithm: review, improvements and comparison

On Modifications Towards Improvement of the Exploitation Phase for SOMA Algorithm with Clustering-aided Migration and Adaptive Perturbation Vector Control

Metaheuristics for Solving Global and Engineering Optimization Problems: Review, Applications, Open Issues and Challenges

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