Introducing Self-Adaptive Parameters to Self-organizing Migrating Algorithm

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

doi:10.1109/cec.2019.8790283

Cited by 1 publication

(6 citation statements)

References 19 publications

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“…In Kadavy et al (2019b), a pool of the step sizes is created. At the beginning of the algorithm, for each individual, the step size is selected randomly.…”

Section: Control Parametersmentioning

confidence: 99%

“…The next frequented value is 1.0, see Deep et al (2007Deep et al ( , 2008, Singh and Agrawal (2015b) and Duary et al (2020). 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.…”

Section: Control Parametersmentioning

confidence: 99%

“…In Kadavy et al (2019b), the strategies AllToOne and AllToAll are compared based on benchmark CEC 2017 special session and competition on single objective real-parameter numerical optimization (Awad et al 2016) for different settings of control parameters Step and prt.…”

Section: Comparisonmentioning

confidence: 99%

“…In Skanderova et al (2019) and Kadavy et al (2019b), the list of migration strategies taken into account is not complete. In Skanderova et al (2019), just one control parameters setting is taken into account to demonstrate the efficiency of the migration strategy regardless of the control parameters setting since the control parameters are self-adapted.…”

Section: Comparisonmentioning

confidence: 99%

“…In Skanderova et al (2019), just one control parameters setting is taken into account to demonstrate the efficiency of the migration strategy regardless of the control parameters setting since the control parameters are self-adapted. In Kadavy et al (2019b), the influence of the PathLength is omitted, and only two migration strategies are compared. To the best of my knowledge, there is no publication dealing with the comparison of the migration strategies of SOMA (including the newest one) using different parameters settings to show the performance of these strategies and the efficiency of the various control parameters settings.…”

Section: Comparisonmentioning

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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“…In Kadavy et al (2019b), a pool of the step sizes is created. At the beginning of the algorithm, for each individual, the step size is selected randomly.…”

Section: Control Parametersmentioning

confidence: 99%