Hybrid Imperialist Competitive Algorithm Research Articles

Hybrid imperialist competitive algorithm, variable neighborhood search, and simulated annealing for dynamic facility layout problem

Today's manufacturing plants tend to be more flexible due to rapid changes in product mix and market demand. Therefore, this paper investigates the problem of location and relocation (when there ar...

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.

Two-sided assembly line balancing with operator number and task constraints: a hybrid imperialist competitive algorithm

Two-sided assembly line balancing problems usually occur in plants producing large-sized high-volume products such as buses, trucks, or cars. Comparing to traditional simple assembly lines, the two-sided assembly line can shorten the length of line, enhance the utilization of tools and fixtures, and improve the production rate of operators, etc. Whereas in a two-sided assembly line, due to a couple of constraints such as the operation direction constraint and the sequence-dependent finish time constraint, the balancing procedure is much more complex than for a simple assembly line. In this paper, besides some fundamental constraints of the two-sided assembly lines, we put forward additionally the restriction of operator number at each workstation, and take into account concurrently more constraints such as the positional constraints, zoning constraints, and the synchronous task constraints. A novel hybrid imperialist competitive algorithm (ICA) combined with the late acceptance hill-climbing (LAHC) algorithm is employed to solve the multiconstraint problem. For the evaluation of the proposed algorithm, the performance of the hybrid ICA is examined over benchmarks from the literature and compared with the LAHC algorithm and the lower bounds of the instances. Furthermore, a practical two-sided assembly line balancing problem from an engineering machinery company is solved with the proposed approach. The results confirm that the hybrid ICA can effectively shorten the line length and reduce the amount of operators for the existing two-sided assembly line.

Hybrid imperialist competitive-sequential quadratic programming (HIC-SQP) algorithm for solving economic load dispatch with incorporating stochastic wind power: A comparative study on heuristic optimization techniques

The paper presents a new method based on a hybrid algorithm consisting of imperialist competitive algorithm (ICA) and sequential quadratic programming (SQP) technique to solve the power system economic load dispatch (ELD) problem. ICA could be taken into account as a powerful technique. Nevertheless, it may be trapped in local optima especially when numbers of imperialists increase. To alleviate this drawback, SQP is used to fine-tune the results of ICA to increase confidence in the solution. Renewable sources and wind energy especially have recently been getting more interest because of various environmental and economical considerations. So, wind power is included in the problem formulation. The incomplete gamma function (IGF) is used to characterize the impact of wind power. The hybrid imperialist competitive algorithm (ICA) and sequential quadratic programming (SQP) technique (HIC-SQP) is applied to solve economic load dispatch with incorporating stochastic wind power. To evaluate its effectiveness, the proposed method is tested on various power systems with 6, 13, 15, and 40 power plants with and without wind power. Simulation results of proposed method are compared with state-of-the-art heuristic optimization methods. It can be clearly seen that the proposed method improves the solution of ELD problem.

A New Method on Data Clustering Based on Hybrid K-Harmonic Means and Imperialist Competitive Algorithm

Data clustering is one of the commonest data mining techniques. The K-means algorithm is one of the most wellknown clustering algorithms thatare increasingly popular due to the simplicity of implementation and speed of operation. However, its performancecouldbe affected by some issues concerningsensitivity to the initialization and getting stuck in local optima. The K-harmonic means clustering method manages the issue of sensitivity to initialization but the local optimaissue still compromises the algorithm. Particle Swarm Optimization algorithm is a stochastic global optimization technique which is a good solution to the above-mentioned problems. In the present article, the PSOKHM, a hybrid algorithm which draws upon the advantages of both of the algorithms, strives not only to overcome the issue of local optima in KHM but also the slow convergence speed of PSO. In this article, the proposed GSOKHM method, which is a combination of PSO and the evolutionary genetic algorithmwithin PSOKHM,has been positedto enhancethe PSO operation. To carry out this experiment, four real datasets have been employed whose results indicate thatGSOKHMoutperforms PSOKHM.

Hybrid ICA–PSO algorithm for continuous optimization

This paper proposes a new hybrid ICA–PSO algorithm designed to solve mono-objective and multi-objective problems. This continuous optimization algorithm combines an imperialist competitive algorithm (ICA) and a particle swarm optimization (PSO) to improve the exploration ability of ICA. Moreover, ICA–PSO employs the crowding distance to maintain diversity in the Pareto front found by the algorithm as well as a crossover operator to improve the quality of the solutions in the memory of each individual. We test the performance of the proposed algorithm on mono-objective and multi-objective benchmark functions and three engineering design problems, thus showing its ability to optimize different types of optimization problems. Numerical results indicate that our approach outperforms several recently published algorithms, such as NSGA-II and SPEA2.

A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop

In this paper a new mixed-integer linear programming (MILP) model is proposed for the multi-processor open shop scheduling (MPOS) problems to minimize the makespan with considering independent setup time and sequence dependent removal time. A hybrid imperialist competitive algorithm (ICA) with genetic algorithm (GA) is presented to solve this problem. The parameters of the proposed algorithm are tuned by response surface methodology (RSM). The performance of the algorithm to solve small, medium and large sized instances of the problem is evaluated by introducing two performance metrics. The quality of obtained solutions is compared with that of the optimal solutions for small sized instances and with the lower bounds for medium sized instances. Also some computational results are presented for large sized instances.

A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility

We were inspired to furnish information concerning the promising applicability of a hybrid approach involving artificial neural networks (ANNs), with manifold network functions, and a meta-heuristic optimization algorithm for prediction of soil compaction indices. The employed network functions were the prevailed feed-forward network and the novel cascade-forward network algorithms to accommodate multivariate inputs of wheel load, tire inflation pressure, number of passage, slippage, and velocity each at three different levels for estimating the study objectives of soil compaction (i.e. penetration resistance and soil sinkage). The experimentations were carried out in a soil bin facility utilizing a single wheel-tester. Each ANN trials was developed merely and then by merging with the recently introduced evolutionary optimization technique of imperialist competitive algorithm (ICA). The results were compared on the basis of a modified performance function (MSEREG) and coefficient of determination (R2). Our results elucidated that hybrid ICA–ANN further succeeded to denote lower modeling error amongst which, cascade-forward network optimized by ICA managed to yield the highest quality solutions.

A hybrid imperialist competitive ant colony algorithm for optimum geometry design of frame structures

This paper describes new optimization strategy that offers significant improvements in performance over existing methods for geometry design of frame structures. In this study, an imperialist competitive algorithm (ICA) and ant colony optimization (ACO) are combined to reach to an efficient algorithm, called Imperialist Competitive Ant Colony Optimization (ICACO). The ICACO applies the ICA for global optimization and the ACO for local search. The results of optimal geometry for three benchmark examples of frame structures, demonstrate the effectiveness and robustness of the new method presented in this work. The results indicate that the new technique has a powerful search strategies due to the modifications made in search module of ICACO. Higher rate of convergence is the superiority of the presented algorithm in comparison with the conventional mathematical methods and non hybrid heuristic methods such as ICA and particle swarm optimization (PSO).

A hybrid imperialist competitive algorithm for single-machine scheduling problem with linear earliness and quadratic tardiness penalties

In this paper, we study single-machine scheduling problem when each job is considered with linear earliness and quadratic tardiness penalties with no machine idle time. Here the objective is to find the best sequence of jobs in the reasonable time. This model was studied in several researches, and some algorithms were proposed to solve it such as genetic algorithm. As the size of problem increased, such algorithms were not effective and efficient. Hence, we proposed the hybrid imperialist competitive algorithm. The proposed algorithm is based upon the imperialist competitive algorithm and genetic algorithm concepts. This algorithm was tested in problems with different sizes. The results denoted that the hybrid algorithm can solve different size of problem in reasonable time. This procedure showed its efficiency in medium- and large-sized problems as compared with other available methods.

A new hybrid imperialist competitive algorithm on data clustering

Clustering is a process for partitioning datasets. This technique is very useful for optimum solution. k-means is one of the simplest and the most famous methods that is based on square error criterion. This algorithm depends on initial states and converges to local optima. Some recent researches show that k-means algorithm has been successfully applied to combinatorial optimization problems for clustering. In this paper, we purpose a novel algorithm that is based on combining two algorithms of clustering; k-means and Modify Imperialist Competitive Algorithm. It is named hybrid K-MICA. In addition, we use a method called modified expectation maximization (EM) to determine number of clusters. The experimented results show that the new method carries out better results than the ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and k-means.