Fuzzy adaptive imperialist competitive algorithm for global optimization

2014 ◽  
Vol 26 (4) ◽  
pp. 813-825 ◽  
Author(s):  
Abdullah Al Khaled ◽  
Seyedmohsen Hosseini
2013 ◽  
Vol 284-287 ◽  
pp. 3135-3139 ◽  
Author(s):  
Jun Lin Lin ◽  
Chun Wei Cho ◽  
Hung Chjh Chuan

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.


Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 635
Author(s):  
Zong-Sheng Wang ◽  
Jung Lee ◽  
Chang Geun Song ◽  
Sun-Jeong Kim

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.


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

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Kamran Chapi ◽  
Nhat-Duc Hoang ◽  
...  

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


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

2012 ◽  
Vol 166-169 ◽  
pp. 493-496
Author(s):  
Roya Kohandel ◽  
Behzad Abdi ◽  
Poi Ngian Shek ◽  
M.Md. Tahir ◽  
Ahmad Beng Hong Kueh

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.


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