Bio-Inspired Optimization Algorithms for Complex Systems

Bio-inspired optimisation algorithms draw inspiration from natural processes and the adaptive behaviours of living organisms to address high-dimensional, nonlinear and multi-modal problems. By emulating mechanisms such as swarm intelligence, evolutionary selection and collective learning, these algorithms strike a balance between exploration of the global search space and exploitation of promising regions. Their flexibility and robustness have proved invaluable in domains ranging from communications network design and energy management to logistics, materials discovery and bioinformatics. Key paradigms include particle swarm optimisation, ant colony systems, genetic algorithms, krill herd and elephant herding optimisation, all of which leverage decentralised decision-making, stochastic perturbations and feedback loops to navigate complex solution landscapes. Recent advances have focused on enhancing convergence speed through adaptive parameter tuning, hybridising multiple metaheuristics and incorporating mathematical operators—such as Lévy flights or elite selection—to avoid premature stagnation. This suite of methods continues to find broad application in engineering design, machine learning hyperparameter tuning and the real-time control of dynamic systems.

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Research from all publishers

Elitist Barnacles Mating Optimiser (eBMO) has been introduced to overcome slow convergence in the original barnacles mating algorithm by integrating an elite exponential probability criterion for dynamically shifting between intensification and diversification phases. A Chebyshev map replaces uniform random sampling to enhance exploration, yielding competitive performance on cryptographic S-box generation benchmarks and demonstrating broad potential for combinatorial tasks.

A comprehensive survey of Lévy-flight based metaheuristics emphasises the pivotal role of heavy-tailed random walks in escaping local optima. By classifying and analysing the statistical properties of Lévy flights within diverse algorithms—such as cuckoo search, monarch butterfly optimisation and moth search—this work highlights improvements in global convergence rates and provides guidelines for parameter selection and future algorithmic integration.

Elephant Herding Optimisation variants and hybrids have been systematically reviewed, revealing novel individual-updating schemes that exploit historical information and clan structures to enhance large-scale and multi-objective problem solving. Applications spanning continuous engineering benchmarks, scheduling and resource allocation confirm the adaptability of these methods, while proposed research directions include adaptive clan formation and real-world deployment in cyber-physical systems.

Bio-Inspired Optimization Algorithms for Complex Systems publication trend

The graph below shows the total number of articles in bio-inspired optimization algorithms for complex systems across all publications each year (not limited to Nature Index journals).

Technical terms

Metaheuristic algorithm: A high-level, problem-independent strategy that guides subordinate heuristics to efficiently explore and exploit complex solution spaces.

Exploration: The process by which an algorithm surveys the global search space to discover diverse candidate solutions.

Exploitation: The focused refinement of existing solutions to improve quality and accelerate convergence towards an optimum.

Elitism: A selection mechanism that preserves a subset of the best solutions across iterations to maintain solution quality and guide search.

Lévy flight: A random walk characterised by many small steps interspersed with occasional large jumps, used to enhance global search capabilities.

References

1. Exploiting an Elitist Barnacles Mating Optimizer implementation for substitution box optimization. ICT Express (2023).
2. Survey of Lévy Flight-Based Metaheuristics for Optimization. Mathematics (2022).
3. Elephant Herding Optimization: Variants, Hybrids, and Applications. Mathematics (2020).