This article proposes an improved imperialistic competitive algorithm to solve multi-objective optimization problems. The proposed multi-objective imperialistic competitive algorithm (MOICA) uses the elitist strategy, based on the mutation and crossover as in genetic algorithms, and the Pareto concept to store simultaneously optimal solutions of multiple conflicting functions. Three performance metrics are used to evaluate the performance of the new algorithm: convergence to the true Pareto-optimal set, solution diversity and robustness, characterized by the variance over 10 runs. To validate the efficiency of the proposed algorithm, several multi-objective standard test functions with true solutions are used. The obtained results show that the MOICA outperforms most of the methods available in the literature. The proposed algorithm can also handle multi-objective engineering design problems with high dimensions.

Water is one of humanity’s most fundamental needs. The demand for freshwater rises in tandem with population expansion. Only 0.01 percent of freshwater is available as surface water in lakes, wetlands, and rivers. As a result, the only choice is to extract water from the oceans. Desalination is an effective option for this. This study focused on the multi-objective design optimization, fabrication, and thermal evaluation of an integrated desalination system combining a solar still with a flat plate collector (SS-FPC). The study investigated the trade-off between two competing objectives: maximizing the efficiency of the SS-FPC system while minimizing its total cost. A numerical code is written in MATLAB to simulate the influence of changing design parameters (DPs) on the SS-FPC performances. The optimal SS-FPC design, offering low costs and a high thermal efficiency, was identified using the multi-objective colonial competitive algorithm (MOCCA). This design was subsequently fabricated and experimentally evaluated under the climatic conditions of Unaizah in Al Qassim, Saudi Arabia. The optimal numerical results were compared with both the literature values and experimental measurements. The comparison revealed strong agreement with the experimental data, with a maximum relative error of 4%. Moreover, the obtained results indicate that the optimized SS-FPC design is capable of achieving a 31% increase in efficiency and a 49% reduction in total cost relative to those reported in the literature.

In industrial sewing, needle heating is a key problem that limits a further increase of sewing speed, and hence the productivity. A high temperature of the needle affects the quality and productivity of sewing. This study deals with the analytical modeling of needle temperature in an industrial sewing machine. Based on the friction heat generated between the needle and fabric and the needle and thread, the authors show that the needle temperature is dependent on the geometry of the sewing machine's driving mechanism and on the sewing speed. In the present paper, the objective is to analyze the heat transfer mechanism in an industrial sewing machine and to develop a simpler model for predicting a maximum temperature of the needle. An experimental method of determining the needle temperature by inserted thermocouples is described. The developed analytical model is validated through comparison with experimental results. Compared with experimental results, the proposed analytical model can better predict the needle heating than the models described in the literature. In fact, the relative error between experimental and analytical results does not exceed 6%.