Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Deshmukh, Sandeep; Uprety, Deepak Chandra; Haroon, Mohd; Banerjee, Dyuti; Shoran, Preety; Varshney, Yash; Sinha, Anurag; Sinha, Samarth; Mishra, Shantanu Kumar Title A name given to the resource Adaptive Hybrid Multi-Objective Evolutionary Algorithm for Wireless Sensor Network Optimization: A Comprehensive Framework Integrating Opposition-Based Learning and Levy Flight Strategies Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Engineering Reports;Volume;8;Issue;5;Article No.;e70719; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/eng2.70719" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/eng2.70719</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036835271?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036835271?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 &amp; 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 Description An account of the resource 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 &amp; Sons Ltd. Subject The topic of the resource coverage optimization; evolutionary computation; levy flight; multi-objective optimization; network lifetime maximization; opposition-based learning; pareto optimality; wireless sensor networks Publisher An entity responsible for making the resource available John Wiley and Sons Inc Relation A related resource ISSN: 25778196; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online