Ions motion algorithm for solving optimization problems

Optimizing the echo state network with a binary particle swarm optimization algorithm

“…Because The reservoir connectivity and weight structure of ESN are created randomly beforehand, So the random connectivity and weight structure of the reservoir is unlikely to be optimal. In order to find an optimal reservoir given a task and to improve the performance of ESN, we will apply several recently proposed algorithms such as, Ions Motion Algorithm [73], Grey Wolf Optimizer [74], Ant Lion Optimizer [75] and Multi Verse Optimizer [76] to ESN in the future. …”

Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm

“…Magnetic Charged System Search (MCSS) [21] utilizes the governing laws for magnetic forces in addition to electrical forces for optimization. Ions Motion Optimization (IMO) [22] is proposed based on the attraction and repulsion of anions and cations to perform optimization.…”

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

“…Recently, many bioinspired heuristic algorithms have been designed to solve optimization problems and successfully applied in data classification problems [6][7][8]. Among them, ant colony optimization (ACO) algorithms have shown promising performance in mining classification rules in the form of "IF ⟨ 1 ⟩ AND ⟨ 2 ⟩ AND ⋅ ⋅ ⋅ ⟨ ⟩ THEN ⟨ ⟩."…”

“…ℎ AntMiner order is equipped with an orderly roulette selection strategy and a new pheromone update strategy to enhance the capability of handling largescale problems and the robustness. Particularly, in the orderly ← 0; (6) while t < maximum iterations and no stagnation do (7) for ← 1 to ants_size do (8) ← Create Rule(examples); (9) Prune( ); (10) Evaluate ; (11) -← ; (12) end for (13) Update Pheromones( roulette selection, the data attributes are sorted such that the algorithm can distinguish the pros and cons of each attribute and construct good classification model more efficiently. The new pheromone update strategy is designed to guide the ants to find better global optimal solutions, which strengthens the degree of pheromone update to avoid falling into local optimum.…”