A Hybrid Proposed Imperialist Competitive Algorithm with Conjugate Gradient Approach for Large Scale Global Optimization

This paper presents a novel hybrid imperialist competitive algorithm called ICA-CG algorithm. Such an algorithm combines the evolution ideas of the imperialist competitive algorithm and the classic optimization ideas of the conjugate gradient, based on the compensation for solving the large scale optimization. In the ICA-CG algorithm, the process of every iteration is divided into two stages. In the first stage, the randomly, rapidity and wholeness of the imperialist competitive Algorithm are used. In the second stage, one of the common optimization classical techniques, that called conjugate gradient to move imperialist countries, is used. Experimental results for five well known test problems have shown the superiority of the new ICA-CG algorithm, in large scale optimization, compared with the classical GA, ICA, PSO and ABC algorithms, with regard to the convergence of speed and quality of obtained solutions

A Hybrid Meta-Heuristic Algorithm based on Imperialist Competition Algorithm

The human has always been to find the best in all things. This Perfectionism has led to the creation of optimization methods. The goal of optimization is to determine the variables and find the best acceptable answer Due to the limitations of the problem, So that the objective function is minimum or maximum. One of the ways inaccurate optimization is meta-heuristics so that Inspired by nature, usually are looking for the optimal solution. in recent years, much effort has been done to improve or create metaheuristic algorithms. One of the ways to make improvements in meta-heuristic methods is using of combination. In this paper, a hybrid optimization algorithm based on imperialist competitive algorithm is presented. The used ideas are: assimilation operation with a variable parameter and the war function that is based on mathematical model of war in the real world. These changes led to increase the speed find the global optimum and reduce the search steps is in contrast with other metaheuristic. So that the evaluations done more than 80% of the test cases, in comparison to Imperialist Competitive Algorithm, Social Based Algorithm , Cuckoo Optimization Algorithm and Genetic Algorithm, the proposed algorithm was superior

An Improved Imperialist Competitive Algorithm based on a new assimilation strategy

Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorithm has been called Imperialist Competitive Algorithm (ICA). The ICA is a population-based algorithm where the populations are represented by countries that are classified as colonies or imperialists. This paper is going to present a modified ICA with considerable accuracy, referred to here as ICA2. The ICA2 is tested with six well-known benchmark functions. Results show high accuracy and avoidance of local optimum traps to reach the minimum global optimal.Three important policies are in the ICA, and assimilation policy is the most important of them. This research focuses on an assimilation policy in the ICA to propose a meta-heuristic optimization algorithm for optimizing function with high accuracy and avoiding to trap in local optima rather than using original ICA by a new assimilation strategy