Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

In recent years, the contamination of the aquatic environment with antibiotics, including tetracyclines, has drawn much attention. Bottom ash (BA), a residue from the biomass power plant, was used to synthesize the magnetic mesoporous silica (MMS) and was utilized as an adsorbent for tetracycline (TC) removal from aqueous solutions. The MMS was characterized by Fourier transform-infrared (FTIR), X-ray diffraction (XRD) pattern, and scanning electron microscopy (SEM). Optimum conditions were obtained in overnight incubation at 60 °C, a pH of 6–8, and an adsorption capacity of 276.74 mg/g. The isotherm and kinetic equations pointed to a Langmuir isotherm model and pseudo-first-order kinetic optimum fitting models. Based on the very low values of entropy changes (ΔS°), the negative value of enthalpy changes (ΔH°) (−15.94 kJ/mol), and the negative Gibbs free-energy changes (ΔG°), the adsorption process was physisorption and spontaneous.

This study investigates the effects of post-weld heat treatment (PWHT) on the microstructures and mechanical properties of plasma arc-welded 316 stainless steel. The experimental parameters included the solid solution temperatures of 650 °C and 1050 °C, solid solution durations of 1 h and 4 h, and quenching media of water and air. The mechanical properties were evaluated using Vickers hardness testing, tensile testing, scanning electron microscopy (SEM), and optical microscopy (OM). The highest ultimate tensile strength (UTS) of 693.93 MPa and Vickers hardness of 196.4 in the welded zone were achieved by heat-treating at 650 °C for one hour, quenching in water, and aging at 500 °C for 24 h. Heat-treating at 650 °C for one hour, followed by quenching in water and aging at 500 °C for 24 h results in larger dendritic δ grains and contains more σ phase compared to the other conditions, resulting in increased strength and hardness. Additionally, it shows wider and shallower dimple structures, which account for its reduced impact toughness.

Demand response (DR) refers to changes in the electricity use patterns of end-users in response to incentive payment designed to prompt lower electricity use during peak periods. Typically, there are three players in the DR system: an electric utility operator, a set of aggregators and a set of end-users. The DR model used in this study aims to minimize the operator’s operational cost and offer rewards to aggregators, while profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users for altering their consumption profiles. This article presents the first application of two metaheuristics in the DR system: particle swarm optimization (PSO) and differential evolution (DE). The objective is to optimize the incentive payments during various periods to satisfy all stakeholders. The results show that DE significantly outperforms PSO, since it can attain better compensation rates, lower operational costs and higher aggregator profits.

S: This study presents a comprehensive comparison of two population-based metaheuristic algorithms, differential evolution (DE) and particle swarm optimization (PSO), applied to the optimization of mechanical component design under both single-objective and multi-objective formulations. The study’s novelty lies in its unified evaluation framework, which benchmarks DE and PSO across a set of standardized mechanical design problems and further extends to multi-objective variants (MODE and MOPSO) for full Pareto front analysis. In the single-objective case, both algorithms achieved solutions equal to or better than the best-known results in the literature. PSO generally provided superior solutions and faster convergence, while DE exhibited advantages in computational efficiency and solution stability. In the multi-objective setting, MODE produced broader and more diverse Pareto fronts with higher hypervolume scores, whereas MOPSO delivered competitive results but tended to concentrate around central trade-offs. Importantly, this study provides one of the first systematic comparisons of convergence behavior in single-objective optimization and Pareto front quality through hypervolume analysis in multi-objective mechanical design problems, offering a deeper understanding of convergence–diversity performance across both optimization settings. The findings highlight PSO’s strength in rapid exploration and DE’s robustness in broad trade-off discovery, providing practical guidance for selecting metaheuristic algorithms in engineering design optimization.