The Imperialist Competitive Algorithm (ICA) that was recently introduced has shown its good performance in optimization problems. This algorithm is inspired by competition mechanism among Imperialists and colonies, in contrast to evolutionary algorithms. This paper presents optimization of bead geometry in welding process using of ICA. Therefore, two case studies from literature are presented to show the effectiveness of the proposed algorithm. ICA has demonstrated excellent capabilities such as simplicity, accuracy, faster convergence and better global optimum achievement. The results of ICA were finally compared with the Genetic Algorithm (GA). The outcome shows the success of ICA in optimizing the weld bead geometry. Manuscript profile

Optimization of turning process is a non-linear optimization with constrains and it is difficult for theconventional optimization algorithms to solve this problem. The purpose of present study is todemonstrate the potential of Imperialist Competitive Algorithm (ICA) for optimization of multipassturning process. This algorithm is inspired by competition mechanism among imperialists andcolonies, in contrast to evolutionary algorithmsthat perform the exploration and exploitation in thesolution space aiming to efficiently find near optimal solutions using a finite sequence ofinstructions. To validate the proposed approach, the results of ICA were finally compared withGenetic Algorithm (GA).Based on the results; ICA has demonstrated excellent capabilities such assimplicity, accuracy, faster convergence and better global optimum achievement. The outcomeshows the success of ICA in optimizing the machining process indicating that data analysis methoddeveloped in this work can be effectively applied to optimize machining processes. Manuscript profile

Optimization of noisy non-linear problems plays a key role in engineering and design problems. These optimization problems can't be solved effectively by using conventional optimization methods. However, metaheuristic algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) seem very efficient to approach in these problems and became very popular. The efficiency of these methods against many new metaheuristic optimization algorithms has been proved in previous works, however a robust comparison between GA and PSO to solve noisy nonlinear problems has not been reported yet. Therefore, in this paper GA and PSO are adapted to find optimal solutions of some noisy mathematical models. Based on the obtained results, GA shows a promising potential in terms of number of iteration to converge and solutions found so far for either for optimization of low or elevated levels of noise. Manuscript profile