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arXiv · 2205.02916

Reconfigurable Heterogeneous Parallel Island Models

Lucas Ângelo da Silveira·Thaynara Arielly de Lima·Mauricio Ayala-Rincón
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Abstract

Heterogeneous Parallel Island Models (HePIMs) run different bio-inspired algorithms (BAs) in their islands. From a variety of communication topologies and migration policies fine-tuned 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, Double-point crossover Genetic Algorithm, Differential Evolution, and self-adjusting Particle Swarm Optimization. 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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Lucas Ângelo da Silveira, Thaynara Arielly de Lima, Mauricio Ayala-Rincón. 2022-05-05. Reconfigurable Heterogeneous Parallel Island Models. https://arxiv.org/abs/2205.02916

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