Application of modified seagull optimization algorithm with archives in urban water distribution networks: Dealing with the consequences of sudden pollution load.

This study focuses on the optimization of consequence management actions in the urban water distribution network. The EPANET simulation model is employed in combination with the multi-objective modified seagull optimization algorithm (MOMSOA) based on archives for a more efficient optimization process. Two objective functions are developed: minimizing reactive activities (cost reduction) and minimizing consumed pollution mass. The utilization of shut-off valves and hydrants for isolating the network and discharging pollution is explored. Without consequence management, 84.5 kg of pollution is consumed. With 18 reactive activities, pollution consumption was reduced to 59.8 kg. Also, to compare the proposed method with other algorithms, the interaction curve between reactive activities and the amount of pollutant mass consumed was obtained using other methods, including MOSOA, NSGA-II, MOPSO, and MOSMA. According to the obtained curve, the proposed method performed better in reducing the mass of consumed pollution. Extracting optimal activities using MOMSOA and a maximum of 18 activities takes about 80 min. The MOMSOA with archive technique significantly shortens this time for real-time consequence management. The proposed approach demonstrates that increasing the archive population decreases the extraction time of interaction curves between objectives by up to 60 %. A small archive capacity slightly increases the time required to extract optimal activities due to searching for similar solutions. However, utilizing the archive capacity enables real-time optimization and consequence management in the network.

Design optimization of office building envelope by developed farmland fertility algorithm for energy saving.

This study focuses on designing sustainable buildings with a specific emphasis on reducing energy consumption and optimizing costs. To address the time-consuming nature of simulation software like TRNSYS and Energy Plus, a novel meta-heuristic optimization algorithm called the Developed Optimization Algorithm of Farmland Fertility (DFFA) is provided. The DFFA algorithm is utilized to optimize parameters related to the building envelope, encompassing walls, windows, and glass curtain walls, aiming to minimize energy demand and construction expenses. Comparative analysis with other approaches such as EPO, LOA, MVO, and FFA demonstrates significant reductions in energy consumption and building design costs achieved by employing the proposed algorithm. Furthermore, the DFFA algorithm yields the desired results within fewer iterations. By increasing the surface area of the glass curtain wall and total window space, improvements in natural ventilation and interior lighting are observed. Despite similar window opening measurements across the compared methods, the suggested algorithm surpasses others when it comes to overall cost and energy efficiency. The total cost reduction compared to the initial design amounts to 39 %. Thus, the DFFA algorithm proves to be more effective in conserving energy in buildings compared to other analyzed procedures. This research serves as a case study and presents a promising method applicable to designing various building types under different weather conditions in future projects.