Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Experimental Design of Bio-Inspired Algorithms for Optimization Problems in Industry 5.0

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

Mandal, Sudip
Cengiz, Korhan
Balamurugan, S.

Paperback

Series: Applied Machine Learning for Iot and Data Analytics, Book 1

General Computers

ISBN13: 9798898814106
Publisher: Bentham Science Publishers
Published: Apr 22 2026
Pages: 228
Weight: 1.22
Height: 0.59 Width: 7.00 Depth: 10.00
Language: English
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.

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