A cutting-edge metaheuristic that tackles complex optimization by systematically exploring diverse neighborhoods. It breaks free from local minima, scales for combinatorial problems and enhances performance with Variable Neighborhood Descent (VND)
In Bioinformatics: Gene clustering, protein alignment, and data integration
An iterative global optimization approach blending greedy construction and localized search.
It balances exploration and exploitation and adapts to diverse challenges
In Bioinformatics: Genome assembly, pathway enrichment, and biological networks
A metaheuristic leveraging diverse, high-quality solutions to solve non-linear problems.
It ensures diversity and elite solutions and excels in data-intensive applications
In Bioinformatics: Multi-omics pipelines, protein-ligand docking, large-scale simulations
A memory-based metaheuristic driving exploration beyond local optima.
It prevents cycling and stagnation and encourages broader exploration.
In Bioinformatics: Protein structure prediction, dynamic modeling, evolutionary analysis
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