Improving Imperialist Competitive Algorithm with Local Search for Global Optimization

2013 ◽  
Author(s):  
Jun-Lin Lin ◽  
Hung-Chjh Chuan ◽  
Yu-Hsiang Tsai ◽  
Chun-Wei Cho
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
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.

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 Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Kamran Chapi ◽  
Nhat-Duc Hoang ◽  
...  
Keyword(s):  
Spatial Modeling ◽  
Imperialist Competitive Algorithm ◽  
Integrated Approach ◽  
Relevance Vector Machine ◽  
Shallow Landslides ◽  
Competitive Algorithm

The authors wish to make the following corrections to this paper [...]

MOICA: A novel multi-objective approach based on imperialist competitive algorithm

2013 ◽  
Vol 219 (17) ◽  
pp. 8829-8841 ◽  
Author(s):  
Rasul Enayatifar ◽  
Moslem Yousefi ◽  
Abdul Hanan Abdullah ◽  
Amer Nordin Darus
Keyword(s):  
Imperialist Competitive Algorithm ◽  
Multi Objective ◽  
Competitive Algorithm
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