Experimental Design of Bio-Inspired Algorithms for Optimization Problems in Industry 5.0

Applied Machine Learning for IoT and Data Analytics (Volume 1) is an integrated exploration of nature-inspired optimisation techniques within the emerging Industry 5.0 paradigm- Positioned at the intersection of artificial intelligence, computational intelligence, industrial engineering, and cyber-physical systems, this volume centres on human-centricity, sustainability, resilience, and intelligent automation.The book comprehensively reviews evolutionary computation, swarm intelligence, neural computation, and hybrid metaheuristics, explaining how these methods can be systematically designed, statistically validated, and benchmarked for real-world deployment. Foundational chapters address Explainable AI (XAI), statistical experimental design, ANOVA-based modelling, parameter tuning strategies, and performance evaluation frameworks.Through fifteen carefully curated chapters, the book presents practical case studies in wireless sensor networks, smart manufacturing, micro-machining, welding optimisation, renewable energy systems, motor control, wireless communications, banking automation, and advanced antenna design. Emphasis is placed on experimental rigour, benchmarking, and reproducibility-bridging the gap between theoretical advancements and industrial implementation.Key Features:-Comprehensive review of classical and hybrid bio-inspired algorithms.-Integration of optimisation techniques within the Industry 5.0 framework-Covers Explainable AI for transparent optimisation systems with a strong focus on experimental design, ANOVA modelling, and statistical validation.-Practical case studies across manufacturing, energy, communications, and automation-Emphasis on reproducibility and methodological rigour with forward-looking insights into AI-enhanced and explainable optimisation trends.