Imperialist competitive algorithm: Difference between revisions

Content deleted Content added
Ruud Koot (talk | contribs)
m Algorithm: clean up spacing around commas and other punctuation fixes, replaced: ,x → , x (2)
 
(21 intermediate revisions by 16 users not shown)
Line 1:
{{short description|Computational method used to solve optimization problems of different types}}
 
In [[computer science]], '''imperialist competitive algorithms''' are a type of computational method used to solve [[optimization problem]]s of different types.<ref name=ica_en_2007_cnf_atashpaz_ica_ica>{{cite conference
{{Orphan|date=April 2016}}
 
In [[computer science]], '''Imperialist Competitive Algorithm (ICA)'''<ref name=ica_en_2007_cnf_atashpaz_ica_ica /> is a computational method that is used to solve [[optimization problem]]s of different types. Like most of the methods in the area of [[evolutionary computation]], ICA does not need the gradient of the function in its optimization process.
 
From a specific point of view, ICA can be thought of as the social counterpart of [[genetic algorithms]] (GAs). ICA is the mathematical model and the computer simulation of human [[social evolution]], while GAs are based on the [[biological evolution]] of species.
 
Applications, advantages and disadvantages of ICA are discussed with details in <ref name=ICA_2014_Survey />
 
== Algorithm ==
[[File:Imperialist-competitive-algorithm-flowchart.jpg|thumb|420px|Figure 1: Flowchart of Imperialist Competitive Algorithm (ICA)]]
Figure 1 shows the flowchart of the Imperialist Competitive Algorithm. This algorithm starts by generating a set of candidate random solutions in the search space of the optimization problem. The generated random points are called the initial ''Countries''. Countries in this algorithm are the counterpart of ''Chromosome''s in GAs and ''Particle''s in [[Particle Swarm Optimization]] (PSO) and it is an array of values of a candidate solution of optimization problem. The [[Loss function|cost function]] of the optimization problem determines the power of each country. Based on their power, some of the best initial countries (the countries with the least cost function value), become ''Imperialists'' and start taking control of other countries (called ''colonies'') and form the initial ''Empires''.<ref name=ica_en_2007_cnf_atashpaz_ica_ica />
 
Two main operators of this algorithm are ''Assimilation'' and ''Revolution''. Assimilation makes the colonies of each empire get closer to the imperialist state in the space of socio-political characteristics (optimization search space). Revolution brings about sudden random changes in the position of some of the countries in the search space. During assimilation and revolution a colony might reach a better position and has the chance to take the control of the entire empire and replace the current imperialist state of the empire.<ref name=ica_en_2010_jnl_nazari_integrated_product_mix_outsourcing />
 
''Imperialistic Competition'' is another part of this algorithm. All the empires try to win this game and take possession of colonies of other empires. In each step of the algorithm, based on their power, all the empires have a chance to take control of one or more of the colonies of the weakest empire.<ref name=ica_en_2007_cnf_atashpaz_ica_ica />
 
Algorithm continues with the mentioned steps (Assimilation, Revolution, Competition) until a stop condition is satisfied.
 
== Pseudocode ==
The above steps can be summarized as the below [[pseudocode]].<ref name=ICA_2014_Survey /><ref name=ica_en_2010_jnl_nazari_integrated_product_mix_outsourcing />
0) Define objective function: <math>f(\mathbf{x}), \quad \mathbf{x}=(x_1,x_2,\dots,x_d); \, </math>
1) Initialization of the algorithm. Generate some random solution in the search space and create initial empires.
2) Assimilation: Colonies move towards imperialist states in different in directions.
3) Revolution: Random changes occur in the characteristics of some countries.
4) Position exchange between a colony and Imperialist. A colony with a better position than the imperialist,
has the chance to take the control of empire by replacing the existing imperialist.
5) Imperialistic competition: All imperialists compete to take possession of colonies of each other.
6) Eliminate the powerless empires. Weak empires lose their power gradually and they will finally be eliminated.
7) If the stop condition is satisfied, stop, if not go to 2.
8) End
 
== Variants ==
Like for [[Particle Swarm Optimization|PSO]], the first version of ICA was proposed for solving continuous optimization problems. Then in other works different variants of ICA were proposed for solving both discrete and continuous problems. For example Chaotic ICA is proposed by Duan, etal.<ref name=ica_en_2009_jnl_duan_template_matching_chaotic_ica /> and also a version of this algorithm for handling constrained optimization problems is proposed by Zhang, etal.<ref name=ica_en_2009_cnf_zhang_improved_ica_constrained_optimization />
 
== Some new points on Meta-heuristic Algorithm ==
In January 2016, by calling “Imperialist Competitive Algorithm” a worldwide algorithm, Sepehr Ghaderi, B.Sc. in Industrial Engineering from Iran, changed it into an independent subject. He added two new branches to this type of algorithm called: “speech coding” and “acceptance of failure”. He studied this type of meta-heuristic algorithm in an article titled “Masonic Worldview Based on Three Historical Periods” from a different point of view. By studying the basic patterns written and used by American-Jewish governor-scientists before and after World War II, he analyzed some samples of International political policies in three historical periods.
 
== References ==
{{Reflist|refs=
 
<ref name=ica_moslem_2011>{{cite conference
|last1= Yousefi
|first1= Moslem
|last2= Mohammadi
|first2= Hossein
|title= Second law based optimization of a plate fin heat exchanger using Imperialist Competitive Algorithm
|booktitle= International Journal of the Physical Sciences
|year= 2011
|volume= 6
|pages= 4749–4759
}}</ref>
 
<ref name=ica_en_2007_cnf_atashpaz_ica_ica>{{cite conference
|last1= Atashpaz-Gargari
|first1= E.
Line 58 ⟶ 6:
|first2= C
|title= Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition
|booktitlebook-title= IEEE Congress on Evolutionary Computation
|year= 2007
|volume= 7
|pages= 4661–4666
|url=http://www.academia.edu/download/3930081/imperialistic_competitive_algorithm__ica__ieee_cec_2007.pdf
}}</ref>
}}{{dead link|date=July 2022|bot=medic}}{{cbignore|bot=medic}}</ref><ref name=ICA_2014_Survey>{{cite journal
 
<ref name=Fuzzy_and_Intelligent_Systems_2008>{{cite conference
|last1= Jasour
|first1= A.
|last2= Atashpaz-Gargari
|first2= E.
|last3= Lucas
|first3= C.
|title= Vehicle fuzzy controller design using imperialist competitive algorithm
|booktitle= Second First Iranian Joint Congress on Fuzzy and Intelligent Systems
|year= 2008
|volume=
|pages=
}}</ref>
 
<ref name= ica_en_2009_jnl_duan_template_matching_chaotic_ica>{{cite journal
|last1= Duan
|first1=H.
|last2=Xu
|first2=C.
|last3=Liu
|first3=S.
|last4=Shao
|first4=S.
|title=Template matching using chaotic imperialist competitive algorithm
|journal=Pattern Recognition Letters
|year=2009
|volume=39
|issue=6
|pages=1362–1381
}}</ref>
 
<ref name=ICA_2014_Survey>{{cite journal
|last1= Hosseini
|first1=S.
Line 105 ⟶ 21:
|volume=24
|pages=1078–1094
|doi=10.1016/j.asoc.2014.08.024
}}</ref>
}}</ref> Like most of the methods in the area of [[evolutionary computation]], ICA does not need the gradient of the function in its optimization process. From a specific point of view, ICA can be thought of as the social counterpart of [[genetic algorithms]] (GAs). ICA is the mathematical model and the computer simulation of human [[Sociocultural evolution|social evolution]], while GAs are based on the [[biological evolution]] of species.
 
== Metaphor ==
<ref name=Fuzzy_ICA_2014_NC>{{cite journal
[[File:Imperialist-competitive-algorithm-flowchart.jpg|thumb|420px|Figure 1: Flowchart of Imperialist Competitive Algorithm (ICA)]]
|last1= Al Khaled
Figure 1 shows the flowchart of the Imperialist Competitive Algorithm. This algorithm starts by generating a set of candidate random solutions in the search space of the optimization problem. The generated random points are called the initial ''Countries''. Countries in this algorithm are the counterpart of ''Chromosome''s in GAs and ''Particle''s in [[Particle Swarm Optimization]] (PSO) and it is an array of values of a candidate solution of the optimization problem. The [[Loss function|cost function]] of the optimization problem determines the power of each country. Based on their power, some of the best initial countries (the countries with the least cost function value), become ''Imperialists'' and start taking control of other countries (called ''colonies'') and form the initial ''Empires''.<ref name=ica_en_2007_cnf_atashpaz_ica_ica />
|first1=A.
|last2=Hosseini
|first2=S.
|title=Fuzzy adaptive imperialist competitive algorithm for global optimization
|journal=Neural Computing and Applications
|year=2015
|volume=26
|issue= 4
|pages=813–825
}}</ref>
 
<ref name=ica_en_2009_cnf_zhang_improved_ica_constrained_optimization>{{cite conference
|last1=Zhang
|first1=Yang
|last2=Wang
|first2=Yong
|last3=Peng
|first3=Cheng
|title=Improved Imperialist Competitive Algorithm for Constrained Optimization
|booktitle=Computer Science-Technology and Applications, IFCSTA
|year=2009
}}</ref>
 
<ref name=ica_en_2008_en_conf_rajabioun_decentralized_pid_controller_design_mimo_evaporator>{{cite conference
|last1=Rajabioun
|first1=R.
|last2=Hashemzadeh
|first2=F.
|last3=Atashpaz-Gargari
|first3=E.
|last4=Mesgari
|first4=B.
|last5=Rajaei Salmasi
|first5=F.
|title=Identification of a MIMO evaporator and its decentralized PID controller tuning using Colonial Competitive Algorithm
|booktitle=In the proceeding of IFAC World Congress, Seoul, Korea
|year=2008
|pages=9952–9957
}}</ref>
 
<ref name=ica_en_2008_jnl_atashpaz_ijicc_pid_mimo_distillation_column_process>{{cite journal
|last1= Atashpaz-Gargari
|first1= E.
|last2= Hashemzadeh
|first2= F.
|last3= Rajabioun
|first3= R.
|last4= Lucas
|first4= C.
|title= Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process
|journal= International Journal of Intelligent Computing and Cybernetics
|year= 2008
|volume= 1
|issue= 3
|pages= 337–355
|doi=10.1108/17563780810893446
}}</ref>
 
<ref name=ica_en_2007_cnf_atashpaz_optimal_pid_controller_isfs2007>{{cite conference
|last1= Atashpaz-Gargari
|first1= E.
|last2= Lucas
|first2= C.
|title= Designing an optimal PID controller using Colonial Competitive Algorithm
|booktitle= Proceeding of First Iranian Joint Congress on Intelligent and Fuzzy Systems
|year= 2007
}}</ref>
 
<ref name=ica_en_2008_en_bchtr_rajabioun_nash_equilibrium_point_achievement>{{cite conference
|last1= Rajabioun
|first1= R.
|last2= Atashpaz-Gargari
|first2= E.
|last3= Lucas
|first3= C.
|title= Colonial competitive algorithm as a tool for Nash equilibrium point achievement
|booktitle= Computational Science and Its Applications–ICCSA
|year= 2008
|pages= 680–695
}}</ref>
 
<ref name=ica_en_2009_jnl_atashpaz_decentralized_pid_controller_optimal_shrinkage_gershgorin_bands>{{cite journal
|last1= Atashpaz-Gargari
|first1= E.
|last2= Rajabioun
|first2= R.
|last3= Hashemzadeh
|first3= F.
|last4= R. Salmasi
|first4= F.
|title= A Decentralized PID controller based on Optimal Shrinkage of Gershgorin Bands and PID tuning Using Colonial Competitive Algorithm
|journal= International Journal of Innovative Computing, Information and Control
|year= 2009
|volume= 5
|issue= 10(A)
}}</ref>
 
<ref name=ica_en_2008_cnf_sepehrirad_recommender_systems>{{cite conference
|last1= Sepehri Rad
|first1= H.
|last2= Lucas
|first2= C.
|title= Application of Imperialistic Competition Algorithm in recommender systems
|booktitle= In 13th Int'l CSI Computer Conference (CSICC'08)
|year= 2008
}}</ref>
 
<ref name=ica_en_2009_jnl_khabbazi_minimum_bit_error_rate_beamforming>{{cite journal
|last1= Khabbazi
|first1= Arash
|last2= Atashpaz-Gargari
|first2= Esmaeil
|last3= Lucas
|first3= Caro
|title= Imperialist competitive algorithm for minimum bit error rate beamforming
|journal= International Journal of Bio-Inspired Computation
|year= 2009
|volume= 1
|issue= 1–2
|pages= 125–133
|doi=10.1504/ijbic.2009.022781
}}</ref>
 
<ref name=ica_2010_en_jnl_Alikhani_Evaluation_of_Image_Segmentation_Methods>{{cite journal
|last1= Alikhani koupaei
|first1= Javad
|last2= Abdechiri
|first2= Marjan
|title= An Optimization Problem for Evaluation of Image Segmentation Methods
|journal= International Journal of Computer and Network Security
|year= 2010
|volume= 2
|issue= 6
}}</ref>
 
<ref name=ica_2010_en_cnf_Sayadnavard_Wireless_sensor_network_localization>{{cite conference
|last1= H. Sayadnavard
|first1= Monireh
|last2= T. Haghighat
|first2= Abolfazl
|last3= Abdechiri
|first3= Marjan
|title= Wireless sensor network localization using Imperialist Competitive Algorithm
|booktitle= 3rd IEEE International Conference on Computer Science and Information Technology
|year= 2010
}}</ref>
 
<ref name=ica_en_2009_jnl_jolai_pareto_simulated_annealing_offline_scheduling_problem_with_rejection>{{cite journal
|last1= Jolai
|first1= F.
|last2= Sangari
|first2= M.
|last3= Babaie
|first3= M.
|title= Pareto simulated annealing and colonial competitive algorithm to solve an offline scheduling problem with rejection
|journal= Journal of Engineering Manufacture
|year= 2010
|volume= 224
|issue= 7
|pages= 1119–1131
|doi=10.1243/09544054jem1746
}}</ref>
 
<ref name=ica_en_2010_jnl_shokrollahpour_bi_criteria_flowshop>{{cite journal
|last1= Shokrollahpour
|first1= E.
|last2= Zandieh
|first2= M.
|last3= Dorri
|first3= Behrouz
|title= A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem
|journal= International Journal of Production Research
|year= 2010
}}</ref>
 
The two main operators of this algorithm are ''Assimilation'' and ''Revolution''. Assimilation makes the colonies of each empire get closer to the imperialist state in the space of socio-political characteristics (optimization search space). Revolution brings about sudden random changes in the position of some of the countries in the search space. During assimilation and revolution a colony might reach a better position and has the chance to take the control of the entire empire and replace the current imperialist state of the empire.<ref name=ica_en_2010_jnl_nazari_integrated_product_mix_outsourcing>{{cite journal
<ref name=ica_en_2010_jnl_nazari_integrated_product_mix_outsourcing>{{cite journal
|last1= Nazari-Shirkouhi
|first1= S.
Line 304 ⟶ 48:
}}</ref>
 
''Imperialistic Competition'' is another part of this algorithm. All the empires try to win this game and take possession of colonies of other empires. In each step of the algorithm, based on their power, all the empires have a chance to take control of one or more of the colonies of the weakest empire.<ref name=ica_en_2007_cnf_atashpaz_ica_ica />
<ref name=ica_2010_en_jnl_Forouharfard_schedule_cross_docking_systems>{{cite journal
|last1= Forouharfard
|first1= S.
|last2= Zandieh
|first2= M.
|title= An imperialist competitive algorithm to schedule of receiving and shipping trucks in cross-docking systems
|journal= International Journal of Advanced Manufacturing Technology
|year= 2010
}}</ref>
 
Algorithm continues with the mentioned steps (Assimilation, Revolution, Competition) until a stop condition is satisfied.
<ref name=ica_2010_en_jnl_Karimi_scheduling_flexible_flow_shops_ica_electromagnetic>{{cite journal
|last1= Karimi
|first1= N.
|last2= Zandieh
|first2= M.
|title= Group scheduling in flexible flow shops: A hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism
|journal= International Journal of Production Research
|year= 2010
|volume=49
|issue=16
|pages=4965
|doi=10.1080/00207543.2010.481644
}}</ref>
 
== Algorithm ==
<ref name=ica_2010_en_jnl_Bagher_Balancing_of_stochastic_U_Type_assembly_lines>{{cite journal
The above steps can be summarized as the below [[pseudocode]].<ref name=ICA_2014_Survey /><ref name=ica_en_2010_jnl_nazari_integrated_product_mix_outsourcing />
|last1= Bagher
0) Define objective function: <math>f(\mathbf{x}), \quad \mathbf{x}=(x_1, x_2,\dots, x_d); \, </math>
|first1= M.
1) Initialization of the algorithm. Generate some random solution in the search space and create initial empires.
|last2= Zandieh
2) Assimilation: Colonies move towards imperialist states in different directions.
|first2= M.
3) Revolution: Random changes occur in the characteristics of some countries.
|last3= Farsijani
4) Position exchange between a colony and Imperialist. A colony with a better position than the imperialist,
|first3= H.
has the chance to take the control of empire by replacing the existing imperialist.
|title= Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm
5) Imperialistic competition: All imperialists compete to take possession of colonies of each other.
|journal= International Journal of Advanced Manufacturing Technology
6) Eliminate the powerless empires. Weak empires lose their power gradually and they will finally be eliminated.
|year= 2010
7) If the stop condition is satisfied, stop, if not go to 2.
|volume=
8) End
|issue=
|pages=
}}</ref>
 
== See also ==
<ref name=ica_2010_en_jnl_Sarayloo_ica_Dynamic_Cell_Formation>{{cite journal
* [[List of metaphor-based metaheuristics]]
|last1= Sarayloo
|first1= Fatemeh
|last2= Tavakkoli-Moghaddam
|first2= Reza
|title= Imperialistic Competitive Algorithm for Solving a Dynamic Cell Formation Problem with Production Planning
|journal= Advanced Intelligent Computing Theories and Applications, Lecture Notes in Computer Science
|year= 2010
|volume= 6215
|issue=
|pages= 266–276
|doi=10.1007/978-3-642-14922-1_34
|series= Lecture Notes in Computer Science
|isbn= 978-3-642-14921-4
}}</ref>
 
== References ==
<ref name=ica_en_2008_jnl_oskouyi_material_properties_characterization_sharp_indentation_test>{{cite journal
{{Reflist|30em}}
|last1= Biabangard-Oskouyi
|first1= A.
|last2= Atashpaz-Gargari
|first2= E.
|last3= Soltani
|first3= N.
|last4= Lucas
|first4= C.
|title= Application of Imperialist Competitive Algorithm for Material Properties Characterization from Sharp Indentation Test
|journal= Int J Eng Simul
|year= 2009
|volume= 10
|issue= 1
|pages=
}}</ref>
 
<ref name=ica_en_2009_cnf_mahmoudi_ann_weights_optimization>{{cite conference
|last1= T.
|first1= Maryam
|last2= F.
|first2= Nafiseh
|last3= L.
|first3= Caro
|last4= T.
|first4= Fattaneh
|title= Artificial Neural Network Weights Optimization based on Imperialist Competitive Algorithm
|booktitle= Seventh International Conference on Computer Science and Information Technologies
|year= 2009
}}</ref>
 
<ref name=ica_en_2010_jnl_lucas_linear_induction_motor>{{cite journal
|last1= Lucas
|first1= C.
|last2= Nasiri-Gheidari
|first2= Z.
|last3= Tootoonchian
|first3= F.
|title= Application of an imperialist competitive algorithm to the design of a linear induction motor
|journal= Energy Conversion and Management
|year= 2010
|volume= 51
|issue= 7
|pages= 1407–1411
|doi=10.1016/j.enconman.2010.01.014
}}</ref>
 
<ref name=ica_2010_en_jnl_omid_phev>{{cite journal
|last1= Movahhedi
|first1= Omid
|last2= R. Salmasi
|first2= Farzad
|title= Optimal Design of Propulsion System with Adaptive Fuzzy Controller for a PHEV Based on Non-dominated Sorting Genetic and Colonial Competitive Algorithms
|journal= International Review of Automatic Control
|year= 2009
}}</ref>
 
<ref name=ica_2010_en_jnl_niknam_k_means_clustering>{{cite journal
|last1= Niknam
|first1= Taher
|last2= Taherian Fard
|first2= Elahe
|last3= Pourjafarian
|first3= Narges
|last4= Rousta
|first4= Alireza
|title= An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering
|journal= Engineering Applications of Artificial Intelligence
|year= 2010
}}</ref>
 
<ref name=ica_2010_en_jnl_mozafari_thin_interphase>{{cite journal
|last1= Mozafari
|first1= Hamid
|last2= Abdi
|first2= Behzad
|last3= Ayob
|first3= Amran
|title= Optimization of Transmission Conditions for Thin Interphase Layer Based on Imperialist Competitive Algorithm
|journal= International Journal on Computer Science and Engineering
|year= 2010
|volume= 2
|issue= 7
|pages= 2486–2490
}}</ref>
}}
 
[[Category:Nature-inspired metaheuristics]]