Data Analysis by Combining the Modified K-Means and Imperialist Competitive Algorithm

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Mohammad Babrdelbonb ◽  
Siti Zaiton Mohd Hashim Mohd Hashim ◽  
Nor Erne Nazira Bazin
Keyword(s):  
Political Development ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Imperialist Competitive Algorithm ◽  
Alternative Methods ◽  
Evolutionary Computations ◽  
Suggested Approach ◽  
Standard Data ◽  
Competitive Algorithm ◽  
Optimum Solution

Data Clustering is one of the most used methods of data mining. The k-means Clustering Approach is one of the main algorithms in the literature of Pattern Recognition and Data Machine Learning which it very popular because of its simple application and high operational speed. But some obstacles such as the adherence of results to initial cluster centers or the risk of getting trapped  into local optimality hinders its performance. In this paper, inspired by the Imperialist Competitive Algorithm based on the k-means method, a new approach is developed, in which cluster centers are selected and computed appropriately. The Imperialist Competitive Algorithm (ICA) is a method in the field of evolutionary computations, trying to find the optimum solution for diverse optimization problems. The underlying traits of this algorithm are taken from the evolutionary process of social, economic and political development of countries so that by partly mathematical modeling of this process some operators are obtained in regular algorithmic forms. The investigated results of the suggested   approach over using standard data sets and comparing it with alternative methods in the literature reveals out that the proposed algorithm outperforms the k-means algorithm and other candidate algorithms in the pool.  

Structural Non-Probabilistic Reliability Analysis Based on Imperialist Competitive Algorithm

2013 ◽  
Vol 712-715 ◽  
pp. 1501-1505
Author(s):  
Yan Hao ◽  
Guang Wei Meng ◽  
Feng Li ◽  
Li Ming Zhou
Keyword(s):  
Reliability Analysis ◽  
Optimization Problems ◽  
Limit State ◽  
Imperialist Competitive Algorithm ◽  
Global Optimum ◽  
State Function ◽  
Penalty Function Method ◽  
Analysis Model ◽  
Competitive Algorithm ◽  
Optimum Solution

A structural non-probabilistic reliability analysis model based on the imperialist competitive algorithm (ICA) is proposed. In practical engineering, the independent variables of the limit state function are usually the structural responses, which, together with the gradients, need to be resolved. The proposed model could find out the global optimum solution through the competition among the empires, without any additional gradient information, showing a good feasibility in many kinds of optimization problems. When utilizing the penalty function method, the constraint domain is enlarged to the failure domain, overcome the difficulties of searching the optimum due to the former narrower constraint domain. A numerical example verifies the high precision and good feasibility of the model.

Optimization of Cold-Formed Channel Sections Using Imperialist Competitive Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 493-496
Author(s):  
Roya Kohandel ◽  
Behzad Abdi ◽  
Poi Ngian Shek ◽  
M.Md. Tahir ◽  
Ahmad Beng Hong Kueh
Keyword(s):  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Computational Method ◽  
Compression Force ◽  
Sqp Method ◽  
Competitive Algorithm ◽  
Different Types ◽  
Formed Channel ◽  
Good Agreement

The Imperialist Competitive Algorithm (ICA) is a novel computational method based on the concept of socio-political motivated strategy, which is usually used to solve different types of optimization problems. This paper presents the optimization of cold-formed channel section subjected to axial compression force utilizing the ICA method. The results are then compared to the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) algorithm for validation purpose. The results obtained from the ICA method is in good agreement with the GA and SQP method in terms of weight but slightly different in the geometry shape.

scholarly journals Improvement of Imperialist Competitive Algorithm based on the Cosine Similarity Criterion of Neighboring Objects

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Maryam Houtinezhad ◽  
Hamid Reza Ghaffary
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Imperialist Competitive Algorithm ◽  
Cosine Similarity ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Competitive Algorithm ◽  
Combined Algorithm

The goal of optimizing the best acceptable answer is according to the limitations and needs of the problem. For a problem, there are several different answers that are defined to compare them and select an optimal answer; a function is called a target function. The choice of this function depends on the nature of the problem. Sometimes several goals are together optimized; such optimization problems are called multi-objective issues. One way to deal with such problems is to form a new objective function in the form of a linear combination of the main objective functions. In the proposed approach, in order to increase the ability to discover new position in the Imperialist Competitive Algorithm (ICA), its operators are combined with the particle swarm optimization. The colonial competition optimization algorithm has the ability to search global and has a fast convergence rate, and the particle swarm algorithm added to it increases the accuracy of searches. In this approach, the cosine similarity of the neighboring countries is measured by the nearest colonies of an imperialist and closest competitor country. In the proposed method, by balancing the global and local search, a method for improving the performance of the two algorithms is presented. The simulation results of the combined algorithm have been evaluated with some of the benchmark functions. Comparison of the results has been evaluated with respect to metaheuristic algorithms such as Differential Evolution (DE), Ant Lion Optimizer (ALO), ICA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

Imperialist Competitive Algorithm with Updated Assimilation for the Solution of Real Valued Optimization Problems

2018 ◽  
Vol 27 (02) ◽  
pp. 1850005
Author(s):  
Zhavat Sherinov ◽  
Ahmet Ünveren ◽  
Adnan Acan
Keyword(s):  
Euclidean Distance ◽  
Optimization Problems ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Imperialist Competitive Algorithm ◽  
Benchmark Problems ◽  
Pearson Correlation Coefficient ◽  
Competitive Algorithm ◽  
Imperialistic Competitive Algorithm ◽  
Better Than

In this paper, an improved imperialistic competitive algorithm is presented for real-valued optimization problems. A new method is introduced for the movement of colonies towards their imperialist, which is called assimilation. The proposed method uses Euclidean distance along with Pearson correlation coefficient as an operator for assimilating colonies with respect to their imperialists. Applications of the proposed algorithm to classical and recently published hard benchmark problems, and statistical analysis associated with the corresponding experimental results illustrated that the achieved success is significantly better than a number of state-of-the art methods.

AN ENHANCED IMPERIALIST COMPETITIVE ALGORITHM FOR OPTIMUM DESIGN OF STEEL FRAMES

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Mahmoud R. Maheri ◽  
M. Talezadeh
Keyword(s):  
Optimum Design ◽  
Structural Engineering ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Added Value ◽  
Fast Time ◽  
Design Quality ◽  
Competitive Algorithm ◽  
Sway Frames

Development of efficient and robust optimization methods for structural design is one of the most active research fields in structural engineering. Imperialist Competitive Algorithm (ICA) is one of the recent meta-heuristic algorithms proposed to solve optimization problems. In this paper, an Enhanced Imperialist Competitive Algorithm (EICA) is proposed which increases the search space and enables the ICA algorithm to escape from local optima in a fast time. In this algorithm added value is given to a slightly unfeasible solution, based on its distance from the relative imperialist. The performance of the proposed EICA algorithm in optimum design of side sway frames is investigated by comparing the EICA optimum designs of two benchmark side sway frames with the best designs obtained using a number of other meta-heuristic solutions. Results indicate that, in terms of both the design quality and the solution speed, EICA compares favorably with a number of other meta-heuristic optimizers, including the basic ICA.

scholarly journals A Modification of the Imperialist Competitive Algorithm with Hybrid Methods for Multi-Objective Optimization Problems

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 173
Author(s):  
Jianfu Luo ◽  
Jinsheng Zhou ◽  
Xi Jiang ◽  
Haodong Lv
Keyword(s):  
Evaluation Method ◽  
Optimization Problems ◽  
Hybrid Methods ◽  
Comprehensive Evaluation ◽  
Imperialist Competitive Algorithm ◽  
Multi Objective Optimization ◽  
Allocation Mechanism ◽  
Multi Objective ◽  
Competitive Algorithm ◽  
Radar Map

This paper proposes a modification of the imperialist competitive algorithm to solve multi-objective optimization problems with hybrid methods (MOHMICA) based on a modification of the imperialist competitive algorithm with hybrid methods (HMICA). The rationale for this is that there is an obvious disadvantage of HMICA in that it can only solve single-objective optimization problems but cannot solve multi-objective optimization problems. In order to adapt to the characteristics of multi-objective optimization problems, this paper improves the establishment of the initial empires and colony allocation mechanism and empire competition in HMICA, and introduces an external archiving strategy. A total of 12 benchmark functions are calculated, including 10 bi-objective and 2 tri-objective benchmarks. Four metrics are used to verify the quality of MOHMICA. Then, a new comprehensive evaluation method is proposed, called “radar map method”, which could comprehensively evaluate the convergence and distribution performance of multi-objective optimization algorithm. It can be seen from the four coordinate axes of the radar maps that this is a symmetrical evaluation method. For this evaluation method, the larger the radar map area is, the better the calculation result of the algorithm. Using this new evaluation method, the algorithm proposed in this paper is compared with seven other high-quality algorithms. The radar map area of MOHMICA is at least 14.06% larger than that of other algorithms. Therefore, it is proven that MOHMICA has advantages as a whole.

scholarly journals Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 635
Author(s):  
Zong-Sheng Wang ◽  
Jung Lee ◽  
Chang Geun Song ◽  
Sun-Jeong Kim
Keyword(s):  
Global Optimization ◽  
Computational Complexity ◽  
Chaotic Maps ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Imperialist Competitive Algorithm ◽  
Excellent Performance ◽  
Competitive Algorithm ◽  
Simulation Results ◽  
Search Capability

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

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