Metaheuristic Optimization Techniques in Geotechnical Design and Applications

Metaheuristic optimization has emerged as a powerful paradigm for addressing the complex, nonlinear and multi-objective problems that typify modern geotechnical design. By drawing on strategies inspired by natural and social phenomena—such as evolutionary selection, swarm intelligence and ecological interactions—these methods explore vast solution spaces to identify parameter combinations that satisfy safety, performance and cost criteria. In geotechnical contexts they have been deployed to calibrate constitutive models, optimise slope stability, determine optimal pile dimensions, design retaining structures and refine soil–structure interaction parameters. Their flexibility allows simultaneous handling of discrete and continuous variables, incorporation of probabilistic soil behaviour and accommodation of multiple conflicting objectives such as minimising construction cost while maximising safety factors. Advances in hybridisation—combining two or more metaheuristic strategies or integrating machine-learning surrogates—have significantly enhanced convergence speed and solution robustness. The global significance of these developments is underscored by applications ranging from seismic resilience of earth dams to sustainable design of deep foundations in urban environments. Overall, metaheuristic approaches now form an integral component of the geotechnical engineer’s toolkit, bridging the gap between high-fidelity numerical simulation and practical engineering design.

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Technical terms

Metaheuristic optimization: A class of high-level procedures that guide heuristic searches to explore large and complex solution spaces, often inspired by natural or social phenomena.

Genetic algorithm (GA): An evolutionary metaheuristic that mimics natural selection by generating populations of solutions, applying crossover and mutation operators and selecting the fittest individuals.

Particle swarm optimisation (PSO): A swarm-intelligence method inspired by flocking behaviour, in which candidate solutions (‘particles’) adjust their positions based on personal and collective experience.

Symbiotic organism search (SOS): A bio-inspired algorithm that models mutualism, commensalism and parasitism phases among organisms to evolve candidate solutions.

Least squares support vector machine (LS-SVM): A supervised learning technique that constructs a regression or classification model by solving a set of linear equations derived from a least-squares cost function.

Differential evolution (DE): A population-based optimisation algorithm that uses vector differences for perturbation and combines target and mutant vectors via crossover to explore the search space.

References

1. Optimizing the Prediction Accuracy of Friction Capacity of Driven Piles in Cohesive Soil Using a Novel Self‐Tuning Least Squares Support Vector Machine. Advances in Civil Engineering (2018).