Hybrid self-organizing migrating algorithm based on estimation of distribution

Lin, Zhiyi; Wang, Li juan

doi:10.2991/meic-14.2014.56

Cited by 5 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also introduced adaptive control parameters for each migration loop, the performance has been demonstrated through the CEC'13 and CEC'17 benchmark test suites. In addition, other variants of SOMA have also confirmed its superior performance compared to the original version such as SOMGA [10], C-SOMGA [9], CSOMA [28], SOMAQI [30], M-SOMAQI [29], mNM-SOMA [1], and HSOMA [24].…”

Section: Introductionmentioning

confidence: 81%

Self-Organizing Migrating Algorithm Pareto

Diep

Zelinka

Das

2019

mendel

View full text Add to dashboard Cite

In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm (SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The important key in the Organization process is the application of the Pareto Principle to select the Migrant and the Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces. Based on the testing results on the well-known benchmark suites such as CEC'13, CEC'15, and CEC'17, the superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective, promising algorithm.

show abstract

Section: Introductionmentioning

confidence: 81%

Self-Organizing Migrating Algorithm Pareto

Diep

Zelinka

Das

2019

mendel

View full text Add to dashboard Cite

show abstract

“…In Nolle et al (2005), PathLength ¼ 2. In Lin and Juan Wang (2014), PathLength ¼ 2:1. The setting PathLength ¼ 2:5 is mentioned in Bao and Zelinka (2019).…”

Section: Control Parametersmentioning

confidence: 99%

“…In most publications, PathLength ¼ 3. However, we will find ones where it is close to 2, see Lin and Juan Wang (2014), dos Santos Coelho (2009a, 2009b, and Mariani et al (2009). Therefore, in this experiment, values 2 and 3 were taken.…”

Section: Comparisonmentioning

confidence: 99%

Self-organizing migrating algorithm: review, improvements and comparison

Skanderová

2022

Artif Intell Rev

View full text Add to dashboard Cite

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.

show abstract