Heat-Pipe-Constrained IoT Device Layout via Multiobjective Differential Evolution

Abstract

Solving large-scale, constrained, and nonlinear optimization problems is crucial for the Internet of Things (IoT) due to its wide range of real-life applications. However, there is no unified approach for handling constraints and optimizing objective functions. This article proposes a tri-objective general framework (TriGF) and an efficient differential evolution (DE) method enhanced with adaptive gradient-based mutation (AGM), termed AGM-DE. Within the TriGF, AGM-DE explores the entire feasible region by considering both constraints and the objective function. The goal is to achieve global optimality and fast convergence for the self-assembly of satellite IoT devices under constraints. AGM is an adaptive refinement technique that uses gradient information to reduce the search space and speed up optimization. In our AGM approach, we incorporate gradient information from the objective function to mitigate the negative effects of classic constraint-based gradient descent and reduce its inherent greediness. To validate AGM-DE’s effectiveness, we conducted extensive simulations on 57 benchmark problems with diverse dimensions and constraints. The results demonstrate AGM-DE’s exceptional ability to manage constraints in 56 of these 57 test functions, outperforming five leading methods in optimization efficacy and consistency. We also assessed AGM-DE’s application in optimizing IoT device self-assembly within a satellite layout, subject to heat pipe constraints. Comparative analyses highlight AGM-DE’s robustness and superior search capabilities in deriving layout schemes. Remarkably, these schemes outperform existing best known solutions for IoT configurations involving 40 to 90 nodes with 80 to 180 variables, confirming AGM-DE’s suitability for a wide range of large-scale constrained IoT challenges.