ItemArticle

Adaptive Hybrid Multi-Objective Evolutionary Algorithm for Wireless Sensor Network Optimization: A Comprehensive Framework Integrating Opposition-Based Learning and Levy Flight Strategies

Title
Adaptive Hybrid Multi-Objective Evolutionary Algorithm for Wireless Sensor Network Optimization: A Comprehensive Framework Integrating Opposition-Based Learning and Levy Flight Strategies
Creator
Deshmukh, Sandeep; Uprety, Deepak Chandra; Haroon, Mohd; Banerjee, Dyuti; Shoran, Preety; Varshney, Yash; Sinha, Anurag; Sinha, Samarth; Mishra, Shantanu Kumar
Description
Wireless sensor networks constitute a foundational technology for ubiquitous monitoring and data acquisition across diverse application domains ranging from environmental surveillance to critical infrastructure management. The operational efficacy and longevity of these networks critically depend on strategic configuration of multiple design parameters including field coverage, sensors per cluster in-charge, sensor out-of-range error, overlaps per cluster in-charge, and network energy consumption. These objectives exhibit inherent trade-offs, rendering the optimization problem a complex multi-objective challenge characterized by conflicting criteria and high-dimensional search spaces. This research presents a novel adaptive hybrid multi-objective evolutionary algorithm that synergistically integrates opposition-based learning for enhanced population diversity and initialization, Levy Flight mutation for effective escape from local optima, and adaptive operator selection for dynamic adjustment of genetic operator probabilities. We conducted exhaustive empirical evaluation comprising independent runs with individuals evolved over multiple generations, benchmarking the proposed algorithm against three state-of-the-art approaches. Performance metrics were computed using global normalization with respect to theoretical problem bounds to ensure measurement validity and cross-algorithm comparability. Statistical analysis including non-parametric rank tests, pairwise comparisons, and effect size quantification confirm the proposed algorithm achieves statistically significant improvements with very large practical significance. The algorithm demonstrates superior convergence characteristics, solution diversity, and Pareto front quality, establishing a robust framework for automated wireless sensor network configuration in resource-constrained environments. 2026 The Author(s). Engineering Reports published by John Wiley & Sons Ltd.
Source
Engineering Reports;Volume;8;Issue;5;Article No.;e70719;
Date
01-01-2026
Publisher
John Wiley and Sons Inc
Subject
coverage optimization; evolutionary computation; levy flight; multi-objective optimization; network lifetime maximization; opposition-based learning; pareto optimality; wireless sensor networks
Coverage
Deshmukh S., MSE Inc., Lake Oswego, OR, United States; Uprety D.C., Department of Computer Science and Engineering, Noida Institute of Engineering and Technology, Greater Noida, India; Haroon M., Department of CSE, Integral University, Lucknow, India; Banerjee D., Artificial Intelligence and Data Science Department, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Guntur, India; Shoran P., Department of Computer Science, CHRIST University, Bengaluru, India; Varshney Y., Lodha Genius Programme, Ashoka University, Haryana, India; Sinha A., Department of Computer Applications, Sri Satya Sai University of Technology and Medical Sciences (SSSUTMS), Madhya Pradesh, Sehore, India, Department of Information Technology, Research Scholar, Guru Ghasidas Vishwavidyalaya, Chhattisgarh, Bilaspur, India; Sinha S., Department of Computer Science & Engineering, Techno International Newtown (MAKAUT University), Kolkata, India, STEM Department, University of Maldives, Maldives; Mishra S.K., Department of Computer Science, KIET Group of Institutions, Delhi-NCR, India
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 25778196;
Format
online
Language
English
Type
Article

Citation

Deshmukh, Sandeep; Uprety, Deepak Chandra; Haroon, Mohd; Banerjee, Dyuti; Shoran, Preety; Varshney, Yash; Sinha, Anurag; Sinha, Samarth; Mishra, Shantanu Kumar, “Adaptive Hybrid Multi-Objective Evolutionary Algorithm for Wireless Sensor Network Optimization: A Comprehensive Framework Integrating Opposition-Based Learning and Levy Flight Strategies,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/21769.

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