Self-adapting self-organizing migrating algorithm

Skanderová, Lenka; Fabián, Tomáš; Zelinka, Ivan

doi:10.1016/j.swevo.2019.100593

Cited by 10 publications

(9 citation statements)

References 31 publications

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“…For the first group PathLength ¼ 2, for the second one, PathLength ¼ 3. In Skanderova et al (2019), the value of PathLength is adapting and its value oscillates between 2 and 3. Occasionally, we can see PathLength ¼ 1:8 in the literature, for example, dos Santos Coelho (2009aCoelho ( , 2009b and Mariani et al (2009).…”

Section: Control Parametersmentioning

confidence: 99%

“…If an individual is not improved for a certain number of migration loops, a new value of Step will be selected based on the roulette selection. Adaptation of the Step is also used in Skanderova et al (2019).…”

Section: Control Parametersmentioning

confidence: 99%

“…For the others, prt ¼ 0:5. In Skanderova et al (2019), the perturbation vectors are generated based on the groups of interacting variables, or the parameter prt is self-adapted. And in Diep (2019), it is self-adapted based on the actual and maximal number of the objective function evaluations.…”

Section: Control Parametersmentioning

confidence: 99%

“…In Skanderova et al (2019), two migration strategies are used, AllToOne and AllToRandom. For each individual, a new leader is generated.…”

Section: Migration Strategiesmentioning

confidence: 99%

“…In Skanderova et al (2019), the migration strategies AllToOne, AllToRandom, AllToAll, and AllToAll Adaptive are compared based on the selected test functions of CEC 2015 competition on learning-based real-parameter single objective optimization (Liang et al 2014). The analysis indicates that the best results were provided by the AllToRandom strategy, while the worst by AllToAll and AllToAll Adaptive, where the population often stagnated.…”

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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