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

By: Pijush Dutta

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.

• Format: Paperback
• Language: English
• ISBN: B0GZLFVHBW
• ISBN13: 9798898814106
• Release Date: April 2026
• Publisher: Bentham Science Publishers
• Length: 228 Pages
• Weight: 1.22 lbs.
• Dimensions: 0.6" x 7.0" x 10.0"