Computational intelligence · United Kingdom

Collective intelligence.
Engineered for complex decisions.

Research and applied innovation in swarm intelligence, machine learning, swarm robotics and bio-inspired decision systems.

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9 Peer-reviewed publications
since 2020

7 Novel algorithm
families

IEEE & Q1 Work presented at international IEEE conferences
and published in Q1 journals

What we investigate

Intelligence emerges
from interaction.

SwarmLab studies how simple agents, local information and heterogeneous behaviours can produce resilient global problem-solving. We translate principles observed in nature into algorithms designed for demanding optimisation and decision environments.

Swarm & evolutionary algorithms

We design optimisation methods that use heterogeneous behaviour, dynamic neighbourhoods and multiple interacting populations to navigate difficult search landscapes. The programme spans standalone, hybrid and ensemble methods.

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Machine learning

We investigate swarm and evolutionary approaches to model selection, hyperparameter optimisation, clustering and prediction—particularly where conventional gradient-based methods are constrained.

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Swarm robotics

Our robotics direction explores how distributed agents can coordinate without a single point of control, adapting collectively to uncertainty, resource constraints and changing objectives.

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Bio-inspired decision making

Biological behaviours including altruism, interspecific eavesdropping, quorum decisions and fission–fusion dynamics become computational mechanisms for information exchange and resource allocation.

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Algorithm research programme

Seven families.
One research trajectory.

Explore all algorithms ↗

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HIDMS-PSO

Heterogeneous dynamic multi-swarm

A heterogeneous particle-swarm architecture that combines complementary search behaviours with adaptive neighbourhood structure.

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GA-HIDMS-PSO

Evolution-assisted swarm search

A hybrid optimiser using genetic operators to reinforce exploration and preserve useful diversity during global search.

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AHPSO

Altruistic particle coordination

Conditional energy lending and borrowing between particles, inspired by altruistic behaviour in biological populations.

Featured publication · 2026

E-AHPSO

Ensemble altruistic heterogeneous particle swarm optimisation

Published by Applied Soft Computing, this work develops the altruistic heterogeneous PSO research line into a robust ensemble architecture and provides expanded analysis of velocity handover, diversity revival and success rate.

Q1 journalEnsemble optimisationComputational intelligence

AuthorsDr. Fevzi Tugrul VarnaProf. Phil HusbandsJournalApplied Soft ComputingDOI10.1016/j.asoc.2026.116151Read publication ↗

Research record