Improving evolutionary algorithm performance for integer type multi-objective building system design optimization

• Two EA improvement strategies are proposed to solve design optimization problems.
• These strategies each show 30% and 60% time savings compared to conventional EAs.
• These strategies generate better convergence than the conventional EA.

Building system design optimization is becoming popular for design decision making. State-of-the-art technique that couples evolutionary algorithms with a building simulation engine, which is time consuming and often cannot reach the “true” optimal solutions. Studies addressing these issues focus on implementing strategies such as fine tuning optimization algorithm's parameters, hybrid evolutionary algorithms with a local search algorithm or optimizing meta-models. Unlike the previous studies, this paper proposes two improvement strategies for building system design optimization. The two strategies, adaptive operators approach and adaptive meta-model approach, modify the behaviors of conventional evolutionary algorithms to improve the optimization convergency and speed performance. To demonstrate the effectiveness of these two strategies compared to conventional algorithms, a case study was conducted. The case study observed high convergency performance from both strategies with 30% and 60% time savings respectively. Furthermore, this study examines the performance comparison in respect to convergency, diversity preservation and speed between these two strategies.