In this study unrelated parallel machine scheduling problem (UPMSP) with preventive maintenance (PM) and sequence dependent setup times (SDST) is investigated. A novel imperialist competitive algorithm (NICA) with multi-elite individuals guidance is proposed to minimize makespan and total tardiness simultaneously. Initialization is done by two heuristics, each of which is built based on one objective. Multielite individuals guidance strategy is added in assimilation that colonies can move toward other imperialists, diversified strategies such as local search and estimation of distribution algorithm (EDA) are adopted based on solution quality in revolution and EDA is also used in imperialist competition. Empire aggression is added by local search of imperialist for plundering a randomly chosen colony. A number of experiments are conducted on the impact of new strategies and the comparisons among NICA and other algorithms. Computational results demonstrate the effectiveness and advantages of NICA in solving UPSMP with PM and SDST.

Multithreshold image segmentation plays a very important role in computer vision and pattern recognition. However, the computational complexity of multithreshold image segmentation increases exponentially with the increasing number of thresholds. Thus, in this paper, a novel imperialist competitive algorithm is proposed to solve the multithreshold image segmentation problem. Firstly, a new strategy of revolution and assimilation is adopted to improve the search efficiency of the algorithm. Secondly, imperialist self-learning and reserve country set are introduced to enhance the search of outstanding individuals in the population. Combining with the reserve country set, a novel imperialist competition strategy is proposed to remove the poorer individuals and improve the overall quality of the population. Finally, the sensitivity of the algorithm parameters is analyzed. Ten standard test pictures are selected to test. The experimental results show that the novel imperialist competitive algorithm has faster convergence speed, higher quality, and higher stability in solving multithreshold segmentation problems than methods from literature.