José Alfredo Brambila-Hernández

Housing acquisition decisions play a strategic role in shaping urban habitability and long-term sustainability, as they directly influence the quality of the built environment and users’ well-being. From an architectural and urban perspective, housing selection can be understood as an assessment of urban habitat quality, in which economic, spatial, social, environmental, and risk-related dimensions interact to define the conditions of livability. This study proposes a multi-objective decision-support framework that integrates evolutionary optimization algorithms (NSGA-II and MOEA/D) with multi-criteria decision analysis (TOPSIS) to support sustainable housing decisions. The model simultaneously considers four conflicting objectives: minimizing acquisition cost, minimizing spatial accessibility and disutility from essential services, maximizing socio-spatial safety and long-term habitat value, and minimizing environmental and territorial risk. A real-world case study in the Tampico metropolitan area demonstrates how the proposed approach generates Pareto-optimal housing alternatives that explicitly reveal trade-offs between habitability dimensions. The resulting non-dominated solutions are subsequently ranked using TOPSIS to reflect user-centered preferences and facilitate transparent decision-making. The results show that the proposed framework effectively operationalizes the concept of urban habitat quality through an explainable, customizable computational tool, thereby contributing to sustainable urban development, resilience, and informed housing choices. This research supports the technological enablement of habitat assessment and aligns with the objectives of SDG 11: Sustainable Cities and Communities, offering a replicable methodology for urban and architectural decision-making contexts.

In this research, two new methods for solving the Internet shopping optimization problem with sensitive prices are proposed, incorporating adaptive adjustment of control parameters. This problem is classified as NP-hard and is relevant to current electronic commerce. The first proposed solution method corresponds to a Memetic Algorithm incorporating improved local search and adaptive adjustment of control parameters. The second proposed solution method is a particle swarm optimization algorithm that adds a technique for diversification and adaptive adjustment of control parameters. We assess the effectiveness of the proposed algorithms by comparing them with the Branch and Bound algorithm, which presents the most favorable outcomes of the state-of-the-art method. Nine instances of three different sizes are used: small, medium, and large. For performance validation, the Wilcoxon and Friedman non-parametric tests are applied. The results show that the proposed algorithms exhibit comparable performance and outperform the Branch and Bound algorithm.

In this paper, we approach the Internet Shopping Optimization Problem with Shipping Costs (IShOP), an NP-hard-relevant problem in the current e-commerce environment. To our knowledge, several solution metaheuristic algorithms have been reported in the literature. In this paper, we propose a novel Particle Swarm Optimization algorithm (PSO) to solve the problem. PSO incorporates a neighborhood diversification technique (NDT), a local search (LS) technique, and an adaptive parameter tuning (APT) method. The proposed algorithm (NDTLSAA-PSO) includes two techniques at the end of each iteration to avoid premature convergence in the search process, balancing exploration and exploitation in selecting candidate solutions. A comparison of the performance of the proposed algorithm has been made against the performance of the best state-of-the-art memetic algorithm solution of the IShOP. A total of 90 instances classified into three groups of small, medium, and large sizes were resolved. The Wilcoxon and Holm non-parametric tests were applied to validate the significance of the differences observed between these two algorithms. The results show that the proposed NDTLSAA-PSO algorithm is superior to the memetic algorithm. In addition, the proposed algorithm obtains the best results in all the in-stances evaluated in terms of quality.

Novel Dynamic Decomposition-Based Multi-objective Evolutionary Algorithm Using Reinforcement Learning Adaptive Operator Selection (DMOEA/D-SL)

Multi-objective Evolutionary Algorithm based on Decomposition with Adaptive Adjustment of Control Parameters to Solve the Bi-objective Internet Shopping Optimization Problem (MOEA/D-AACPBIShOP)

This paper proposes a hybrid harmony search algorithm that incorporates a method of reinitializing harmonies memory using a particle swarm optimization algorithm with an improved opposition-based learning method (IOBL) to solve continuous optimization problems. This method allows the algorithm to obtain better results by increasing the search space of the solutions. This approach has been validated by comparing the performance of the proposed algorithm with that of a state-of-the-art harmony search algorithm, solving fifteen standard mathematical functions, and applying the Wilcoxon parametric test at a 5% significance level. The state-of-the-art algorithm uses an opposition-based improvement method (IOBL). Computational experiments show that the proposed algorithm outperforms the state-of-the-art algorithm. In quality, it is better in fourteen of the fifteen instances, and in efficiency is better in seven of fifteen instances.

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.