Template matching using chaotic imperialist competitive algorithm

Duan, Haibin; Xu, Chunfang; Liu, Senqi; Shan, Shao Fu

doi:10.1016/j.patrec.2009.12.005

Cited by 90 publications

(48 citation statements)

References 7 publications

(5 reference statements)

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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%

“…Then the imperialistic competition begins to obtain the global optimal solution. ICA has been implemented in solving various optimization problems like DG planning [39], template matching [40], optimal design of plate-fin heat exchangers [41] electromagnetic problems [42], power system stabilizer design [43], linear induction motor design [44], economic dispatch [45] and model reduction of a detailed transformer model [46]. Recently Imperialist Competitive algorithm [47] has also been implemented for solving unit commitment with a thermal unit alone.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Imperialistic Competitive Algorithm (Ica)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

Sudhakar¹,

Balasubramanian²,

Padmanaban³

2018

IJIES

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“…Much research was implemented on ICA with = 2 as an appropriate value [12,48,51]. Moreover, is a random variable with a uniform distribution between and :…”

Section: -Step 1: Generating Initial Empiresmentioning

confidence: 99%

“…Their algorithm is compared with the original ICA and Genetic Algorithm (GA) on the circuit-related case study. Duan et al [48] presented a chaotic ICA. They showed that the chaos can improve the global convergence of ICA and avoid getting stuck at local minima.…”

Section: Introductionmentioning

confidence: 99%

A prototype-based classification using extended imperialist competitive algorithm

et al. 2017

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KEYWORDSPrototype-based classi cation; Imperialist competitive algorithm; UCI machine learning repository.Abstract. Classi cation is an important machine learning technique used to predict group membership for data instances. In this paper, we propose an e cient prototypebased classi cation approach in the data classi cation literature by a novel soft-computing approach based on extended imperialist competitive algorithm. The novel classi er is called EICA. The goal is to determine the best places of the prototypes. EICA is evaluated under three di erent tness functions on twelve typical test datasets from the UCI Machine Learning Repository. The performance of the proposed EICA is compared with well-developed algorithms in classi cation including original Imperialist Competitive Algorithm (ICA), the Arti cial Bee Colony (ABC), the Fire y Algorithm (FA), the Particle Swarm Optimization (PSO), the Gravitational Search Algorithm (GSA), the Grouping Gravitational Search Algorithm (GGSA), and nine well-known classi cation techniques in the literature. The analysis results show that EICA provides encouraging results in contrast to other algorithms and classi cation techniques.

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A Modified Imperialist Competitive Algorithm for Multi‐Robot Stick‐Carrying Application

2020

Multi‐Agent Coordination

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Template matching using chaotic imperialist competitive algorithm

Cited by 90 publications

References 7 publications

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

A Novel Imperialistic Competitive Algorithm for Unit Commitment with Wind Generation

A prototype-based classification using extended imperialist competitive algorithm

A Modified Imperialist Competitive Algorithm for Multi‐Robot Stick‐Carrying Application

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