Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with an Add-Point Strategy for a Wireless Sensor Network

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 2; p. 317
• Main Authors: Pan, Jeng-Shyang, Zhang, Li-Gang, Chu, Shu-Chuan, Shieh, Chin-Shiuh, Watada, Junzo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09-02-2023; MDPI
• Subjects: add-point strategy; Algorithms; Approximation; Communication; Complexity; Control; differential evolutionary algorithm; Evolutionary algorithms; Evolutionary computation; Fitness; gannet optimization algorithm; Heuristic; Heuristic methods; Heuristic programming; Methods; Optimization; Optimization algorithms; Radial basis function; Sensors; surrogate model; Wireless sensor networks; China; surrogate model; differential evolutionary algorithm; add-point strategy; gannet optimization algorithm
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25020317

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems.