A Comparative Study of Multi-Objective Optimization Algorithms for Energy Efficiency, Communication Path Design and Daily Light Doses

The spatial distribution of occupants shapes energy use and work-related performance. Algorithmic seating optimization can shorten communication distances, reduce electricity for lighting, and increase daily light exposure, whereas prior studies often targeted a single objective. Given the nondeterministic polynomial-time (NP)-hardness of key subproblems and the complexity of multi-objective search, this study evaluates heuristic and metaheuristic methods driven by sensor data from an open-plan office. Evolutionary, sampling-based, and model-based approaches are compared in terms of Pareto dominance, solution diversity, stability, convergence, and computation time. Results show that multi-criteria optimization with real-world data yields clear differences in performance profiles and search space exploration. Non-dominated Sorting Genetic Algorithm III (NSGA-III) contributes the highest share of Pareto solutions, while Hybrid Metaheuristics (HMH) achieves the largest target space coverage (hypervolume). Markov Chain Monte Carlo (MCMC) and Pareto Simulated Annealing (PSA) deliver particularly stable gains in light dose, while Bayesian optimization contributes no Pareto solutions in the present setting. Certain user pairings and spatial allocation patterns remain consistent across strategies, indicating persistent structural properties of the search space. Fast methods such as Deep Optimization (DO), Multi-Objective Pareto Simulated Annealing (PSA-Multi), Mulit-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), and MCMC are efficient, whereas NSGA-III offers the highest solution quality at greater computational cost. These findings advance understanding and support algorithm selection and space analysis for similar combinatorial allocation problems.