Imperialist Competitive Algorithm (ICA) is considered as prime meta-heuristic Algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA، at run time، the suggested method (ACICA) finds the optimum number of clusters while optimal clustering of the data simultaneously. To increase the accuracy and speed of convergence، the structure of ICA changes. The proposed Algorithm requires no background knowledge to classify the data. In addition، the proposed method is more accurate in comparison with other clustering methods based on evolutionary Algorithms. DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method، the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic clustering Algorithms based on evolutionary Algorithms.

Imperialist Competitive Algorithm (ICA) is one of the recent meta-heuristic Algorithms proposed to solve optimization problems. The Imperialist Competitive Algorithm is based on a socio-politically inspired optimization strategy. This paper presents four different variants of this Algorithm. These methods are applied to some engineering design problems and a comparison is made among the results of these Algorithms and other meta-heuristics. The results show the efficiency and capabilities of the ICA in finding the optimum design.

The main enabling technology for cloud computing is the use of virtual machines. After making the decision of their placement on hosts, they will be set to run. This process is called virtual machine placement. This process has a great importance on energy consumption and resource wastage avoidance. On the other hand, the growing complexity of cloud infrastructure compounds the problem. In this article, the problem of virtual machine placement is transformed to an optimization problem. The goal is minimizing energy consumption and maximizing the profit of placement, simultaneously. A newly emerged optimization method, called Imperialist Competitive Algorithm is applied in this paper. In addition, a unique method for generating new solutions based on already discovered ones, proposed. Finally the success of the proposed Algorithm is confirmed by simulation results and its evaluation is compared with GGA and FFD Algorithm.

In this study, a new approach is introduced to solve Blasius differential equation using of Imperialist Competitive Algorithm (ICA). This Algorithm is inspired by competition mechanism among Imperialists and colonies and has demonstrated excellent capabilities such as simplicity, accuracy, faster convergence and better global optimum achievement in contrast to other evolutionary Algorithms. The obtained results have been compared with the exact solution of Blasius equation and another result obtained in previous works and show higher accuracy and less computational requirements. In addition, the method presented with details can beeasily extended to solve a wide range of nonlinear problems.

Solving complex engineering problems using meta-heuristics requires powerful operators to maintain su cient diversi cation as well as proper intensi cation during the search. Standard Imperialist Competitive Algorithm, ICA, delays search intensi cation by propagating it via a number of arti cial empires that compete each other until one concurs with the others. An Enhanced Imperialist Competitive Algorithm (EICA) is developed here by adding an evolutionary operator to the standard ICA followed by greedy replacement in order to improve its e ectiveness. The new operator introduces a walking step directed from the less signi cant  t with a  tter individual in each pair of the search agents together with a random scaling and pick-up scheme. EICA performance is then compared with ICA as well as genetic Algorithm, particle swarm optimization, di erential evolution, colliding bodies optimization, teaching-learning-based optimization, symbiotic organisms search in a set of  fteen test functions. Second, a variety of continuous and discrete engineering benchmarks and structural sizing problems are solved to evaluate EICA in constrained optimization. In this regard, a diversity index and other convergence metrics are traced. The results exhibit a considerable improvement on the Algorithm using the proposed features of EICA and its Competitive performance, compared to other treated methods.

Determination of stope boundary is a critical step in underground mine design, directly impacting project profitability and operational plans. Developing efficient and accurate Algorithms for solving the Stope Boundary Optimization (SBO) problem has been a challenging task. In this study, a metaheuristic Discrete Imperialist Competitive Algorithm (DICA) was introduced for the SBO problem. The DICA Algorithm, following a simulation-based approach, provides initial solutions in the search space and identifies the optimal solution after evaluating each one. Tested with different operators such as assimilation and revolution, and various initial populations, the suggested Algorithm was applied to a 5*5*5 m block model of a copper deposit comprising 15,945 blocks. The results demonstrated the Algorithm’s capability to determine stope boundaries within a reasonable computational time. For validity check, the results were compared with those obtained from the Maximum Value Neighborhood (MVN) and floating stope Algorithms. The comparison revealed that DICA outperformed both Algorithms.

In today's design, system complexity and increasing demand for safer, more efficient and less costly systems have created new challenges in science and engineering. Locomotives are products which are designed according to market order and technical needs of customers. Accordingly, targets of companies, especially designers and manufacturers of locomotives, have always been on the path of progress and seek to offer products with higher technology than other competitors. Quality of body structures is based on indicators such as natural frequency, displacement, fatigue life and maximum stress. Natural frequency of various components of the system and their adaption to each other are important for avoiding the phenomenon of resonance. In this study, body structures of ER24 locomotive (Iran Safir Locomotive) was studied. A combination of Imperialist Competitive Algorithm (ICA) and artificial neural network was proposed to find optimal weight of structures while natural frequencies were in the determined range. Optimization of locomotive's structure was performed with an emphasis on maintaining locomotive abilities in static and dynamic fields. The results indicated that use of optimization techniques in the design process was a powerful and effective tool for identifying and improving main dynamic characteristics of structures and also optimizing performance in stress, noise and vibration fields.