Self-organizing migrating algorithm with clustering-aided migration

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

doi:10.1145/3377929.3398129

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…In Kadavy et al (2020), a population is divided into two types of individuals, best individuals of clusters (sub-populations) and the others. For the first group PathLength ¼ 2, for the second one, PathLength ¼ 3.…”

Section: Control Parametersmentioning

confidence: 99%

“…As mentioned, in Kadavy et al (2020), there are two types of individuals. The best individuals from the clusters (sub-populations) and the others.…”

Section: Control Parametersmentioning

confidence: 99%

“…In Kadavy et al (2019aKadavy et al ( , 2019b, the value of prt i for each individual x ~i is selected from a predefined pool based on roulette selection. In Kadavy et al (2020), for the best individuals from the clusters, prt ¼ 0:3. For the others, prt ¼ 0:5.…”

Section: Control Parametersmentioning

confidence: 99%

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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 Parametersmentioning

confidence: 99%

“…As mentioned, in Kadavy et al (2020), there are two types of individuals. The best individuals from the clusters (sub-populations) and the others.…”

Section: Control Parametersmentioning

confidence: 99%

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Self-organizing migrating algorithm: review, improvements and comparison

Skanderová

2022

Artif Intell Rev

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“…The Self-organizing Migrating Algorithm with Clustering-aided Migration (SOMA-CL) is one of the recent variants of SOMA [6] proposed by the authors of this paper. The SOMA-CL utilizes the natural abilities of exploration and exploitation phases of its predecessor.…”

Section: Soma-clmentioning

confidence: 99%

“…The paper presents the new variant of SOMA, Self-organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The SOMA-CLP algorithm is a direct descendant of SOMA-CL [6] and both algorithms can be classified as modern variants of SOMA. SOMA-CLP uses a linear adaptation of the prt control parameter, promoting the global transition from the tendency of exploration to exploitation.…”

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confidence: 99%

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

Kadavý

Pluháček

Viktorin

et al. 2021

Proceedings of the Genetic and Evolutionary Computation Conference Companion

Self Cite

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The paper proposes the Self-organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The SOMA-CLP is the next iteration of the SOMA-CL algorithm, further enhanced by the linear adaptation of the prt control parameter used to generate a perturbation vector. The latest CEC 2021 benchmark set on a single objective bound-constrained optimization was used for the performance measurement of the improved variant. The proposed algorithm SOMA-CLP results were compared and tested for statistical significance against four other SOMA variants. CCS CONCEPTS• Mathematics of computing → Evolutionary algorithms.

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Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

Mohamed

Sallam

Agrawal

et al. 2022

Neural Comput & Applic

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To develop new meta-heuristic algorithms and evaluate on the benchmark functions is the most challenging task. In this paper, performance of the various developed meta-heuristic algorithms are evaluated on the recently developed CEC 2021 benchmark functions. The objective functions are parametrized by inclusion of the operators, such as bias, shift and rotation. The different combinations of the binary operators are applied to the objective functions which leads to the CEC2021 benchmark functions. Therefore, different meta-heuristic algorithms are considered which solve the benchmark functions with different dimensions. The performance of some basic, advanced meta-heuristics algorithms and the algorithms that participated in the CEC2021 competition have been experimentally investigated and many observations, recommendations, conclusions have been reached. The experimental results show the performance of meta-heuristic algorithms on the different combinations of binary parameterized operators.

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Self-organizing migrating algorithm with clustering-aided migration

Cited by 12 publications

References 8 publications

Self-organizing migrating algorithm: review, improvements and comparison

Self-organizing migrating algorithm: review, improvements and comparison

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

Evaluating the performance of meta-heuristic algorithms on CEC 2021 benchmark problems

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