Open Access

ARTICLE

From Budget-Aware Preferences to Optimal Composition: A Dual-Stage Framework for Wireless Energy Service Optimization

Haotian Zhang, Jing Li^*, Ming Zhu, Zhiyong Zhao, Hongli Su, Liming Sun

School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China

* Corresponding Author: Jing Li. Email:

Computers, Materials & Continua 2026, 86(3), 42 https://doi.org/10.32604/cmc.2025.072381

Received 25 August 2025; Accepted 21 October 2025; Issue published 12 January 2026

Abstract

In the wireless energy transmission service composition optimization problem, a key challenge is accurately capturing users’ preferences for service criteria under complex influencing factors, and optimally selecting a composition solution under their budget constraints. Existing studies typically evaluate satisfaction solely based on energy transmission capacity, while overlooking critical factors such as price and trustworthiness of the provider, leading to a mismatch between optimization outcomes and user needs. To address this gap, we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios, systematically incorporating service price, transmission capacity, and trustworthiness into the satisfaction assessment framework. Furthermore, we propose a Budget-Aware Preference Adjustment Model that predicts users’ baseline preference weights from historical data and dynamically adjusts them according to budget levels, thereby reflecting user preferences more realistically under varying budget constraints. In addition, to tackle the composition optimization problem, we develop a Reflective-Evolutionary Large Language Model—Guided Ant Colony Optimization algorithm, which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process. Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity, accurately predicts users’ preferences, and significantly enhances their satisfaction under complex constraints.

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Keywords

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