[HTML][HTML] Trial-based dominance for comparing both the speed and accuracy of stochastic optimizers with standard non-parametric tests
KV Price, A Kumar, PN Suganthan - Swarm and Evolutionary Computation, 2023 - Elsevier
Non-parametric tests can determine the better of two stochastic optimization algorithms
when benchmarking results are ordinal—like the final fitness values of multiple trials—but for …
when benchmarking results are ordinal—like the final fitness values of multiple trials—but for …
[HTML][HTML] Optimizing parameters in swarm intelligence using reinforcement learning: An application of Proximal Policy Optimization to the iSOMA algorithm
L Klein, I Zelinka, D Seidl - Swarm and Evolutionary Computation, 2024 - Elsevier
This paper presents a new algorithm for optimizing parameters in swarm algorithm using
reinforcement learning. The algorithm, called iSOMA-RL, is based on the iSOMA algorithm …
reinforcement learning. The algorithm, called iSOMA-RL, is based on the iSOMA algorithm …
Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization
In this paper, we propose a novel methodology that combines the opposition Nelder–Mead
algorithm and the selection phase of the genetic algorithm. This integration aims to enhance …
algorithm and the selection phase of the genetic algorithm. This integration aims to enhance …
Revisiting CEC 2022 ranking: A new ranking method and influence of parameter tuning
R Biedrzycki - Swarm and Evolutionary Computation, 2024 - Elsevier
Optimization competitions give an impulse to develop optimization algorithms. However,
there is no common agreement on how to rank the contestants. This paper proposes a …
there is no common agreement on how to rank the contestants. This paper proposes a …
Trial-based dominance enables non-parametric tests to compare both the speed and accuracy of stochastic optimizers
KV Price, A Kumar, PN Suganthan - arXiv preprint arXiv:2212.09423, 2022 - arxiv.org
Non-parametric tests can determine the better of two stochastic optimization algorithms
when benchmarking results are ordinal, like the final fitness values of multiple trials. For …
when benchmarking results are ordinal, like the final fitness values of multiple trials. For …
Exploring Adaptive Components of SOMA
M Matusikova, M Pluhacek, T Kadavy… - Proceedings of the …, 2023 - dl.acm.org
This research paper aims to explore the possibilities of parameterization of state-of-the-art
adaptive mechanisms incorporated in the self-organizing migrating algorithm (SOMA). This …
adaptive mechanisms incorporated in the self-organizing migrating algorithm (SOMA). This …