Artificial Immune Systems in Optimization and Robotics

Since their inception in the 1990s, artificial immune systems (AIS) have drawn inspiration from the human immune response to create flexible, distributed problem-solving frameworks. In optimisation, AIS mimic lymphocyte selection, hypermutation and network interactions to explore high-dimensional search spaces without falling into premature local optima. Adaptive mechanisms, such as negative selection, facilitate anomaly detection in complex datasets, while clonal selection drives iterative refinement of candidate solutions. In robotics, AIS principles underpin adaptive control and coordination: memory-based learning guides autonomous vehicles through dynamic environments, whereas immune-network models support collaborative behaviours in multi-robot swarms. By integrating diversity preservation and concentration adjustment, AIS frameworks have delivered robust performance across scheduling, material design and autonomous navigation tasks, with tangible applications in composite-structure optimisation and spacecraft formation control.

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Studies have demonstrated that AIS can be paired with particle swarm optimisation to design composite materials with enhanced mechanical properties. By modelling fibre orientation as antigens and employing affinity-based sampling, the hybrid approach achieves improved Young’s modulus distributions while maintaining computational efficiency.

A memory-based control method inspired by immunological mechanisms has been applied to spacecraft formation flying. The approach utilises past manoeuvre histories as immune memory, enabling multiple satellites to maintain precise geometric configurations under uncertain gravitational perturbations with minimal system dynamic information.

A hybrid clonal selection algorithm incorporating combinatorial recombination and modified hypermutation operators has been introduced for global optimisation. By fusing parent solutions and adaptively adjusting mutation rates, the method enhances exploration and exploitation balance, yielding superior convergence on multimodal benchmark problems compared with classical immune-inspired techniques.

Artificial Immune Systems in Optimization and Robotics publication trend

The graph below shows the total number of articles in artificial immune systems in optimization and robotics across all publications each year (not limited to Nature Index journals).

Technical terms

Artificial immune system (AIS): A computational paradigm that emulates the human immune response, using distributed agents to solve optimisation and pattern-recognition problems through selection and adaptation.

Clonal selection: A process by which high-affinity candidate solutions are replicated and subjected to controlled variation, mirroring antibody proliferation in biological immune systems.

Immune network: A conceptual model in which interactions between solution elements regulate diversity and information flow, akin to cytokine signalling networks.

Hypermutation: A mechanism of introducing strategic variations in replicated candidates, analogous to rapid mutation of B-cell receptors to improve antigen affinity.

References

1. A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization. Computational Intelligence and Neuroscience (2016).
2. A Memory/Immunology‐Based Control Approach with Applications to Multiple Spacecraft Formation Flying. Mathematical Problems in Engineering (2013).
3. Optimization of Composite Structures with Thin Rigid Fibers Using Bioinspired Algorithms. Applied Sciences (2024).