Metaheuristic Optimization Algorithms Research

Large-scale analysis of optimisation methods for parameter estimation problems in the life sciences

The paper benchmarks optimization methods for parameter estimation in life sciences, identifying performance tiers and proposing a hybrid strategy that improves solution quality and robustness.

Toroidal Search Algorithm: A Topology-Inspired Metaheuristic with Applications to ODE Parameterization in Mathematical Oncology

This paper introduces the Toroidal Search Algorithm (TSA), a metaheuristic optimization method that outperforms existing techniques in high-dimensional and bounded search spaces, with applications in mathematical oncology parameter estimation.

An adaptive multi-strategy metaheuristic for robust model calibration in large-scale systems biology

This paper introduces eLSHADE+, a metaheuristic for robust model calibration in systems biology, showing improved convergence and accuracy over existing methods.

Evaluating Evolutionary and Gradient-Based Algorithms for Optimal Pathfinding

This study compares Genetic Algorithm, Particle Swarm Optimization, and Sequential Quadratic Programming for pathfinding in nonlinear terrains, finding SQP to be more efficient and reliable.

Hyperparameter optimisation in differential evolution using Summed Local Difference Strings, a rugged but easily calculated landscape for combinatorial search problems

The paper evaluates hyperparameter optimization in differential evolution using a novel objective function and compares machine learning approaches with grid search and other optimizers.

Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework

The paper evaluates and compares neuronal parameter optimization methods using the Neuroptimus framework, identifying effective algorithms and creating an online database for benchmarking.

ISRES+: An improved evolutionary strategy for function minimization to estimate the free parameters of Systems Biology models

ISRES+ improves parameter estimation in systems biology models by enhancing an evolutionary strategy to better minimize error functions.

Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models

This paper introduces DOPS, a hybrid meta-heuristic for parameter estimation in biochemical models, which outperforms existing methods on test functions and real-world models.