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

2014 ◽  
Vol 2 (1) ◽  
pp. 184-195
Author(s):  
Ban Mitras ◽  
Jalal Sultan
Keyword(s):  
Global Optimization ◽  
Conjugate Gradient ◽  
Large Scale ◽  
Imperialist Competitive Algorithm ◽  
Competitive Algorithm ◽  
Gradient Approach

scholarly journals An improved imperialist competitive algorithm for global optimization

2019 ◽  
Vol 27 (5) ◽  
pp. 3567-3581 ◽  
Author(s):  
Ting YOU ◽  
Yueli HU ◽  
Peijiang LI ◽  
Yinggan TANG
Keyword(s):  
Global Optimization ◽  
Imperialist Competitive Algorithm ◽  
Competitive Algorithm

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 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.

Simultaneous identification of multi-irregular interfacial boundary configurations in non-homogeneous body using surface displacement measurements

2016 ◽  
Vol 231 (13) ◽  
pp. 2472-2483 ◽  
Author(s):  
Mohammad H Mozaffari ◽  
Mahmud Khodadad ◽  
Mohsen Dashti Ardakani
Keyword(s):  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Simplex Method ◽  
Imperialist Competitive Algorithm ◽  
Local Optimization ◽  
Optimization Techniques ◽  
Competitive Algorithm ◽  
Simple Tension ◽  
Displacement Measurements

The purpose of this work is to identify simultaneously two irregular interfacial boundaries configurations between the components of three connected domains using a discrete number of displacement measurements obtained by a simple tension test. A unique combination of a global optimization method, i.e., the imperialist competitive algorithm and two local optimization methods, i.e., the conjugate gradient method and Simplex method along with the inverse application of the boundary elements method are employed in an inverse software package. A fitness function, which is the summation of squared differences between calculated displacements and measured displacements at identical locations on the outer boundary, is minimized. Due to the complexity and the ill-posed nature of this identification problem, imperialist competitive algorithm is used to find the best initial guesses of the unknown interface boundaries in order to be used by the local optimization techniques, i.e., conjugate gradient method and then Simplex method to accurately converge to the optimal shape of two irregular interfacial boundaries between the components of an inhomogeneous body. Several examples are selected, and the accuracy of the obtained results is discussed. The effect of experimental measurement errors and the influence of material properties of the sub regions on the identification process are also investigated.

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 Color Revolution: A Novel Operator for Imperialist Competitive Algorithm in Solving Cloud Computing Service Composition Problem

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 177
Author(s):  
Amin Jula ◽  
Elankovan A. Sundararajan ◽  
Zalinda Othman ◽  
Narjes Khatoon Naseri
Keyword(s):  
Cloud Computing ◽  
Service Time ◽  
Service Composition ◽  
Large Scale ◽  
Cost Optimization ◽  
Imperialist Competitive Algorithm ◽  
Time Cost ◽  
Competitive Algorithm ◽  
Cloud Computing Service ◽  
Color Revolution

In this paper, a novel high-performance and low-cost operator is proposed for the imperialist competitive algorithm (ICA). The operator, inspired by a sociopolitical movement called the color revolution that has recently arisen in some countries, is referred to as the color revolution operator (CRO). The improved ICA with CRO, denoted as ICACRO, is significantly more efficient than the ICA. On the other hand, cloud computing service composition is a high-dimensional optimization problem that has become more prominent in recent years due to the unprecedented increase in both the number of services in the service pool and the number of service providers. In this study, two different types of ICACRO, one that applies the CRO to all countries of the world (ICACRO-C) and one that applies the CRO solely to imperialist countries (ICACRO-I), were used for service time-cost optimization in cloud computing service composition. The ICACRO was evaluated using a large-scale dataset and five service time-cost optimization problems with different difficulty levels. Compared to the basic ICA and niching PSO, the experimental and statistical tests demonstrate that the ability of the ICACRO to approach an optimal solution is considerably higher and that the ICACRO can be considered an efficient and scalable approach. Furthermore, the ICACRO-C is stronger than the ICACRO-I in terms of the solution quality with respect to execution time. However, the differences are negligible when solving large-scale problems.

A New Stochastic/Perturbation Method for Large-Scale Global Optimization and Its Application to Water Cluster Problems

1993 ◽  
Author(s):  
Richard H. Byrd ◽  
Thomas Derby ◽  
Elizabeth Eskow ◽  
Klaas P. Oldenkamp ◽  
Robert B. Schnabel
Keyword(s):  
Global Optimization ◽  
Perturbation Method ◽  
Water Cluster ◽  
Large Scale ◽  
Stochastic Perturbation ◽  
Stochastic Perturbation Method
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