Beyond Trade-Offs: Integrating Metaheuristics and AHP for Performance-Driven Building Design Decisions

[2022 – Present]

This research addresses the intricate challenge of enhancing building performance across various dimensions. Multi-objective optimization involves finding optimal trade-offs among two or more conflicting objectives. Metaheuristic algorithms are used to find approximate solutions for optimization problems where exact solutions are impossible or difficult to obtain. Most of these algorithms, which typically involve repetition and randomness, draw inspiration from natural phenomena or human behaviour. This series of studies extensively utilizes various metaheuristic algorithms (e.g., GA, PSO, ACO, SA, NSGA II, and hybrid algorithms) to improve building performance.

One of the key contributions of this research is the proposal of a novel method that combines multi-objective optimization with the Analytical Hierarchy Process (AHP) to rank and sort solutions on the Pareto Front. (The Pareto front is a set of optimal solutions and trade-offs between objective functions.)

This body of research lays the groundwork for more efficient and environmentally friendly solutions.

[BUILDING PERFORMANCE]