A survey on the Imperialist Competitive Algorithm metaheuristic: Implementation in engineering domain and directions for future research

Hosseini, Seyedmohsen; Khaled, Abdullah Al

doi:10.1016/j.asoc.2014.08.024

Cited by 251 publications

(89 citation statements)

References 50 publications

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“…The ICA algorithm has been successfully applied to a variety of optimization problems [6][7][8]. The results reported in these studies confirm its competitiveness over other EAs.…”

Section: Introductionsupporting

confidence: 58%

“…The results reported in these studies confirm its competitiveness over other EAs. The ease of performing neighborhood movement, less dependency on initial solutions, and having a better convergence rate are other advantages of the ICA, but due to some insufficiencies, it often gets trapped in the local optimum area, especially in multimodal and high-dimensional problems [7,9].…”

Section: Introductionmentioning

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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“…The ICA algorithm has been successfully applied to a variety of optimization problems [6][7][8]. The results reported in these studies confirm its competitiveness over other EAs.…”

Section: Introductionsupporting

confidence: 58%

Section: Introductionmentioning

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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“…Selected values for the parameters used in the Petaling Jaya study area. The WCA and ICA techniques have demonstrated their viability as powerful optimization tools with great potential for solving optimization problems [31][32][33][34]. The proposed model and the corresponding optimization methods were coded and run in MATLAB programming software.…”

Section: Resultsmentioning

confidence: 99%

Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

et al. 2016

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Abstract:One of the serious concerns in network design is creating an efficient and appropriate network capable of efficiently migrating the passenger's mode of transportation from private to public. The main goal of this study is to present an improved model for combining the feeder bus network design system and the railway transit system while minimizing total cost. In this study, the imperialist competitive algorithm (ICA) and the water cycle algorithm (WCA) were employed to optimize feeder bus and railway services. The case study and input data were based on a real transit network in Petaling Jaya, Kuala Lumpur, Malaysia. Numerical results for the proposed model, including the optimal solution, statistical optimization results and the convergence rate, as well as comparisons are discussed in detail.

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“…[48][49][50] Among the metaheuristic approaches, the imperialist competitive algorithm (ICA) is a population-based algorithm that was presented by Atashpaz-Gargari and Lucas 51 So far, the ICA-based approaches have been applied on different multiobjective optimization problems that were aimed at providing a set of Pareto optimal solutions, such as assembly line design 52 , coupling of photovoltaicelectrolyzer system 53 , sensor deployment in wireless sensor networks 54 , and benchmarked functions. 55,56 Hosseini and Al Khaled 57 have presented a state-of-theart literature survey on the application of ICA in different engineering disciplines. In accordance with the reviewed studies, MOICA has been found to be more efficient than the other approaches for solving engineering problems of different domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

Piroozfard¹,

Wong²,

Tiwari³

2018

IJCIS

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New environmental regulations have driven companies to adopt low-carbon manufacturing. This research is aimed at considering carbon dioxide in the operational decision level where limited studies can be found, especially in the scheduling area. In particular, the purpose of this research is to simultaneously minimize carbon emission and total late work criterion as sustainability-based and classical-based objective functions, respectively, in the multiobjective job shop scheduling environment. In order to solve the presented problem more effectively, a new multiobjective imperialist competitive algorithm imitating the behavior of imperialistic competition is proposed to obtain a set of non-dominated schedules. In this work, a three-fold scientific contribution can be observed in the problem and solution method, that are: (1) integrating carbon dioxide into the operational decision level of job shop scheduling, (2) considering total late work criterion in multi-objective job shop scheduling, and (3) proposing a new multi-objective imperialist competitive algorithm for solving the extended multi-objective optimization problem. The elements of the proposed algorithm are elucidated and forty three small and large sized extended benchmarked data sets are solved by the algorithm. Numerical results are compared with two well-known and most representative metaheuristic approaches, which are multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II, in order to evaluate the performance of the proposed algorithm. The obtained results reveal the effectiveness and efficiency of the proposed multi-objective imperialist competitive algorithm in finding high quality non-dominated schedules as compared to the other metaheuristic approaches.

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A survey on the Imperialist Competitive Algorithm metaheuristic: Implementation in engineering domain and directions for future research

Cited by 251 publications

References 50 publications

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

Reduction of carbon emission and total late work criterion in job shop scheduling by applying a multi-objective imperialist competitive algorithm

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