imperialist competitive algorithm

In this paper, we present an improved imperialist competitive algorithm for solving an inverse form of the Huxley equation, which is a nonlinear partial differential equation. To show the effectiveness of our proposed algorithm, we conduct a comparative analysis with the original imperialist competitive algorithm and a genetic algorithm. The improvement suggested in this study makes the original imperialist competitive algorithm a more powerful method for function approximation. The numerical results show that the improved imperialist competitive algorithm is an efficient algorithm for determining the unknown boundary conditions of the Huxley equation and solving the inverse form of nonlinear partial differential equations.

Reservoir sedimentation increases economic cost and overflow of dam water. An optimal control problem (OCP) with singularly perturbed equations of motion is perused in the fields of sediment management during a finite lifespan. Subsequently the OCP is transformed to a nonlinear programming problem by utilizing a collocation approach, and then we employed the imperialist competitive algorithm to improve the execution time and decision. So, the solutions of the optimal control and fast state as well as the maximization of net present value of dam operations are obtained. An illustrative practical study demonstrated that sedimentation management is economically favourable for volume of confined water and total amount in remaining storage and effectiveness of the propounded approach.

The mixed fuzzy relation programming with a nonlinear objective function and two operators of max-product and max-min composition is studied in this paper. Its feasible domain structure is investigated and some simplification procedures are presented to reduce the dimension of the original problem. We intend to modify the assimilation and revolution operators of the imperialist competitive algorithm in order to prevent the generation of infeasible solutions. The modified imperialist competitive algorithm (MICA) is compared with a real-value genetic algorithm to solve the original problem. Several test problems are presented to compare its performance with respect to the performance of the genetic algorithm. Their results show the superiority of the proposed algorithm over the genetic algorithm.

In this paper, the investment portfolio is formed based on the data mining algorithm of CHAID on the basis of the risk status criteria. In the next step, the second investment portfolio is created based on the decision rules extracted by the DEA-BCC model. The final portfolio is created through a two-objective mathematical programming model based on the Imperialist Competitive algorithm.

With the rapid expansion of the information on the Internet, recommender systems play an important role in terms of trade and research. Recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. In the past decades, many studies have been done in the field of recommender systems, most of which have focused on designing new recommender algorithms based on computational intelligence algorithms. The success of a recommender system besides the quality of the algorithm depends on other factors such as: Sparsity, Cold start and Scalability in the performance of a recommender system, which can affect the quality of the recommendation. Consequently, the main motivation for this research is to providing an effective meta heuristic algorithm based on a combination of imperialist competitive and firefly algorithms using clustering technique. The simulation results of the proposed algorithm on real data sets Move Lens and Film Trust have shown better forecast accuracy in the item recommendation to users than other algorithms presented in subject literature. Also the proposed algorithm can choose appropriate items among the wide range of data and give it to output in a reasonable time.

This paper proposes two methods to predict the efficiency of photochemical removal of AY23 by UV/Ag-TiO$_{2}$ process. In this work the potential of the particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) modeling approaches are presented to forecast the photocatalytic removal of AY23 in the presence of Ag-TiO$_{2}$ nanoparticles prepared under desired conditions. To validate the techniques, a total of 100 data are used that randomly splitted in two parts, 80 samples for the training the models and 20 for testing of the models. Experimental results on datasets show that ICA approach is better than PSO approach. Remarkable analysis results reveal that AY23 initial concentration is the most significant factors that influence on the AY23 removal ýefficiency.ý

The capacitated clustering problem (CCP) is a data mining technique utilized to categorize a number of objects with known demands into k distinct clusters such that the capacity of each cluster is not violated, every object is allocated to exactly one cluster and sum of distances from all cluster centers to all other nodes is minimized. The CCP is an NP-hard combinatorial optimization problem. Therefore, practical large-scale instances of this problem cannot be solved by exact solution methodologies within acceptable computational time. Our interest was therefore focused on meta-heuristic solution approaches. For this reason, a modified imperialist competitive algorithm (MICA) is proposed for the CCP In this paper. The proposed MICA iterates steps between three basic phases, i.e., the random assignment phase to form clusters, the seed relocation phase to find a better median, and the local improvement phase to make a revision of the solution. The proposed algorithm is tested on several standard instances available from the literature. The computational results confirm the effectiveness of the presented algorithm and show that the proposed algorithm is competitive with other meta-heuristic algorithms for solving the CCP.