Swarm Intelligence and Evolutionary Algorithms for Real World Applications

This reprint showcases recent advances in swarm intelligence (SI) and evolutionary computation (EC), emphasising their capacity to address complex, data-intensive, and noise-prone real-world problems where conventional methods often fall short. The collected works highlight how the self-organising and adaptive nature of SI and EC enables robust search, optimisation, and modeling across diverse domains.

The contributions span finance, healthcare, hardware design, energy systems, molecular biology, and intelligent environments. They include hybrid neural-evolutionary strategies for portfolio optimisation, evolutionary approaches for identifying circadian-modulating molecules, and grammatical evolution for automatic generation of synthesizable hardware code. Bio-inspired multi-objective optimisation is applied to early voice-disorder detection, while particle swarm optimisation supports optimal placement of electric-vehicle parking infrastructure. New algorithmic developments-such as a swarm optimiser and symbiotic organism search-based unsupervised feature selection-advance global optimisation and data analytics. Finally, a QPSO-based hybrid technique enhances indoor positioning by fusing WLAN and WSN data.

Together, these papers demonstrate the versatility and growing impact of SI and EC techniques, fostering dialogue among emerging and established researchers and advancing their application to pressing real-world challenges.