Imperialist competitive algorithm combined with chaos for global optimization

Talatahari, Siamak; Azar, Bahman Farahmand; Sheikholeslami, Razi; Gandomi, Amir H.

doi:10.1016/j.cnsns.2011.08.021

Cited by 230 publications

(109 citation statements)

References 19 publications

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“…For dealing with premature convergence in the ICA, some previous works applied an improved assimilation operator for doing better local search and keeping diversity in the population [9][10][11]. Other methods take advantage of the strength of other EAs and ICA together in an algorithm (e.g., [12][13][14]).…”

Section: Related Workmentioning

confidence: 99%

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GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

Abdi¹,

Lak²,

Seyfari³

2017

Turk J Elec Eng & Comp Sci

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Abstract:The imperialist competitive algorithm (ICA) is a recent global search strategy developed based on human social evolutionary phenomena in the real world. However, the ICA has the drawback of trapping in local optimum solutions when used for high-dimensional or complex multimodal functions. In order to deal with this situation, in this paper an improved ICA, named GICA, is proposed that can enhance ICA performance by using a new assimilation method and establishing a relationship between countries inspired by the globalization concept in the real world. The proposed algorithm is evaluated using a set of well-known benchmark functions for global optimization. Obtained results show the efficiency and effectiveness of the method and show that this strategy can deal with the local optimum problem.

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Section: Related Workmentioning

confidence: 99%

“…Talatahari et al [10] introduced a chaotic imperialist competitive algorithm. In this study different chaotic maps were utilized to determine the moving direction for the assimilation operation.…”

Section: Related Workmentioning

confidence: 99%

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

Abdi¹,

Lak²,

Seyfari³

2017

Turk J Elec Eng & Comp Sci

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“…Therefore, researchers have paid much attention to chaotic system to make use of its special advantages. It has been introduce to evolutionary algorithms to improve their global search ability 36,37,38 . Recently, chaotic systems have been embedded into PSO to promote population diversity and enhance the optimization performance.…”

Section: Chaotic Jump Operatormentioning

confidence: 99%

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

Qiu¹

2018

IJCIS

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Feature selection (FS) is a crucial data pre-processing process in classification problems. It aims to reduce the dimensionality of the problem by eliminating irrelevant or redundant features while achieve similar or even higher classification accuracy than using all the features. As a variant of particle swarm optimization (PSO), Bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimizer. However, it also suffers from premature convergence like other PSO algorithms, especially in high-dimensional optimization problems. In order to improve its performance in FS problems, this paper proposes a novel BBPSO based FS method called BBPSO-ACJ. An adaptive chaotic jump strategy is designed to help the stagnated particles make a large change in their searching trajectory. It can enrich the search behavior of BBPSO and prevent the particles from being trapped into local attractors. A new global best updating mechanism is employed to reduce the size of obtained feature subset. The proposed BBPSO-ACJ is compared with eight evolutionary computation (EC) based wrapper methods and two filter methods on nine benchmark datasets with different number of dimensions and instances. The experimenta l results indicate that the proposed method can select the most discriminative features from the entire feature set and achieve significantly better classification performance than other comparative methods.

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“…During competition, weaker empires are neglected and positions are collapsed and positions are updated. This may lead to converge to a state with one empire [39][40][41][42][43][44][45][46][47][48][49].…”

Section: Imperialistic Competitive Algorithm (Ica)mentioning

confidence: 99%

“…Dynamic Programming is good for a variety of sizes, but more complex hence the computation time more [27]. A new algorithm called an imperialistic competitive algorithm (ICA) inspired by imperialistic competition, which causes the colonies to converge to global minimum cost function [39][40][41][42][43][44][45][46][47][48][49][50] has been attempted in this research…”

Section: Introductionmentioning

confidence: 99%

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

Sudhakar¹,

Balasubramanian²,

Padmanaban³

2018

IJIES

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Abstract:The integration of wind power to the grid is essential to meet the growing power demand and possess a challenge to unit commitment to meet the load demand at low cost for scheduling the generating units. A new imperialistic competitive algorithm (ICA) has been developed for the above mentioned unit commitment problem for solving deterministic and stochastic condition. This imperialistic competitive algorithm has been developed with constraints, including generation limits, start up and shut down cost, minimum up and downtime along with ramp constraint. The ICA has been studied initially on the 10 units 24 hour system and 40 units 24 hour system for the deterministic load. Later, a wind generator has been added to the 10 units 24 hour system with wind data for 24 hours and the results are compared with genetic algorithm (GA), particle swarm optimization (PSO), hybrid particle swarm optimisation (HPSO), shuffled frog leaping algorithm (SFLA), invasive weed optimization (IWO). The comparison result is presented in this paper and it shows that the ICA gives optimized fuel cost of $482246.2 which is less than the LR, GA, BFOA, SFLA, IWO for stochastic unit commitment.

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Imperialist competitive algorithm combined with chaos for global optimization

Cited by 230 publications

References 19 publications

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

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