Behavior of Bioinspired Algorithms in Parallel Island Models

Silveira, Lucas Ângelo; Soncco-Álvarez, José Luis; Lima, Thaynara Arielly de; Ayala-Rincón, Maurício

doi:10.1109/cec48606.2020.9185732

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Different topologies and strategies to implement them are available (e.g. [9], [10], [11]). Homogeneous PIMs execute the same BA in all islands, whereas heterogeneous models admit different BAs running in their islands.…”

Section: Background a Parallel Island Model (Pim)mentioning

confidence: 99%

“…We select a static and a dynamic topology that successfully addressed URD in [10] for homogeneous PIMs and in [1] for non reconfigurable heterogeneous PIMs. We choose asynchronous models since the dynamic asynchronous HePIM was the one that provided the best results.…”

Section: Communication Topologiesmentioning

confidence: 99%

“…The basis to compare the performance of PIMs, are sequential versions of GA, GAD, DE and PSO with populations of size 24n log n and breeding cycles fixed as n. Also, we select eight 12-island asynchronous HoPIMs, designed in [10], running exclusively one of the BAs: GA, GAD, DE or PSO. Furthermore, we select two asynchronous HePIMs designed in [1].…”

Section: Experiments and Analysis Of Accuracymentioning

confidence: 99%

“…Such PIMs outperformed the accuracy obtained by the GA after careful calibration of the migration and breeding cycle parameters and exploration of a variety of topologies [29], [30]. Further, Silveira et al [10] analyzed synchronous HoPIMs for GA, PSO, and the Social Spider Algorithm (SSA). HePIMs also have been conceived to solve multiobjective optimization problems (MOPs).…”

Section: Related Workmentioning

confidence: 99%

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Reconfigurable Heterogeneous Parallel Island Models

Silveira¹,

Lima²,

Ayala-Rincón³

2022

Preprint

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Heterogeneous Parallel Island Models (HePIMs) run different bio-inspired algorithms (BAs) in their islands. From a variety of communication topologies and migration policies finetuned for homogeneous PIMs (HoPIMs), which run the same BA in all their islands, previous work introduced HePIMs that provided competitive quality solutions regarding the best-adapted BA in HoPIMs. This work goes a step forward, maintaining the population diversity provided by HePIMs, and increasing their flexibility, allowing BA reconfiguration on islands during execution: according to their performance, islands may substitute their BAs dynamically during the evolutionary process. Experiments with the introduced architectures (RecHePIMs) were applied to the NP-hard problem of sorting permutations by reversals, using four different BAs, namely, simple Genetic Algorithm (GA), Double-point crossover Genetic Algorithm (GAD), Differential Evolution (DE), and self-adjusting Particle Swarm Optimization (PSO). The results showed that the new reconfigurable heterogeneous models compute better quality solutions than the HePIMs closing the gap with the HoPIM running the best-adapted BA.

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Section: Background a Parallel Island Model (Pim)mentioning

confidence: 99%

Section: Communication Topologiesmentioning

confidence: 99%