Imperialist Competitive Algorithms with Perturbed Moves for Global Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 3135-3139 ◽  
Author(s):  
Jun Lin Lin ◽  
Chun Wei Cho ◽  
Hung Chjh Chuan
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Population Based ◽  
Experimental Results ◽  
Local Optimum ◽  
Competitive Algorithm ◽  
Competitive Algorithms

Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. Previous works have shown that ICA converges quickly but often to a local optimum. To overcome this problem, this work proposed two modifications to ICA: perturbed assimilation move and boundary bouncing. The proposed modifications were applied to ICA and tested using six well-known benchmark functions with 30 dimensions. The experimental results indicate that these two modifications significantly improve the performance of ICA on all six benchmark functions.

scholarly journals Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 445 ◽  
Author(s):  
Yu Qiao ◽  
Thi-Kien Dao ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu ◽  
Trong-The Nguyen
Keyword(s):  
Global Optimization ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Team Member ◽  
Trading Strategy ◽  
Experimental Results ◽  
The Other ◽  
Wireless Sensor ◽  
Local Optimum ◽  
Applied Optimization

The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.

Diagnosis of colorectal cancer based on imperialist competitive algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5359-5368
Author(s):  
B Ratna Raju ◽  
G.N Swamy ◽  
K. Padma Raju
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Feature Selection ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Imperialist Competitive Algorithm ◽  
Experimental Results ◽  
K Nearest Neighbor ◽  
Competitive Algorithm ◽  
Knn Classifier

The Colorectal cancer leads to more number of death in recent years. The diagnosis of Colorectal cancer as early is safe to treat the patient. To identify and treat this type of cancer, Colonoscopy is applied commonly. The feature selection based methods are proposed which helps to choose the subset variables and to attain better prediction. An Imperialist Competitive Algorithm (ICA) is proposed which helps to select features in identification of colon cancer and its treatment. Also K-Nearest Neighbor (KNN) classifier is used to retain a minimal Euclidean distance between the feature of query vector and all the data in the nature of prototype training. Experimental results have proved that the proposed method is superior when compared to other methods in its metrics of performance. Better accuracy is achieved by the proposed method.

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.

A fuzzy imperialistic competitive algorithm for optimizing convex functions

2014 ◽  
Vol 7 (2) ◽  
pp. 192-208 ◽  
Author(s):  
Mahsan Esmaeilzadeh Tarei ◽  
Bijan Abdollahi ◽  
Mohammad Nakhaei
Keyword(s):  
Fuzzy Logic ◽  
Convex Functions ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Optimization Techniques ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Local Optima ◽  
Content Type ◽  
Competitive Algorithm

Purpose – The purpose of this paper is to describe imperialist competitive algorithm (ICA), a novel socio-politically inspired optimization strategy for proposing a fuzzy variant of this algorithm. ICA is a meta-heuristic algorithm for dealing with different optimization tasks. The basis of the algorithm is inspired by imperialistic competition. It attempts to present the social policy of imperialisms (referred to empires) to control more countries (referred to colonies) and use their sources. If one empire loses its power, among the others making a competition to take possession of it. Design/methodology/approach – In fuzzy imperialist competitive algorithm (FICA), the colonies have a degree of belonging to their imperialists and the top imperialist, as in fuzzy logic, rather than belonging completely to just one empire therefore the colonies move toward the superior empire and their relevant empires. Simultaneously for balancing the exploration and exploitation abilities of the ICA. The algorithms are used for optimization have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. FICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing fuzzy logic on it. Findings – Therefore several solution procedures, including ICA, FICA, genetic algorithm, particle swarm optimization, tabu search and simulated annealing optimization algorithm are considered. Finally numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures. Test results present the suitability of the proposed fuzzy ICA for convex functions with little fluctuations. Originality/value – The proposed evolutionary algorithm, FICA, can be used in diverse areas of optimization problems where convex functions properties are appeared including, industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning (optimization techniques; fuzzy logic; convex functions).

Imperialist competitive algorithm combined with chaos for global optimization

2012 ◽  
Vol 17 (3) ◽  
pp. 1312-1319 ◽  
Author(s):  
S. Talatahari ◽  
B. Farahmand Azar ◽  
R. Sheikholeslami ◽  
A.H. Gandomi
Keyword(s):  
Global Optimization ◽  
Imperialist Competitive Algorithm ◽  
Competitive Algorithm

scholarly journals Economic Dispatch Optimization Using Imperialist Competitive Algorithm (ICA) and compare with PSO algorithm result

2015 ◽  
Vol 15 (2) ◽  
pp. 6541-6545
Author(s):  
Saeid Jalilzadeh ◽  
Saman Nikkhah
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Imperialist Competitive Algorithm ◽  
Pso Algorithm ◽  
Population Based ◽  
Test System ◽  
Competitive Algorithm ◽  
Wide Range ◽  
Loading Effects

Measurement Imperialist Competitive Algorithm (ICA) is a  population based stochastic optimization technique, originallydeveloped by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In ICA, the system (imperialists) is initialized with a population of random solutions (colonies) and searches for optimal using cognitive and social factors by updating generations. ICA has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the ICA, this paper discusses the use of ICA approach to optimize performance of economic dispatch problems. The proposed method is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects

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