A novel imperialist competitive algorithm for generalized traveling salesman problems

Ardalan, Zaniar; Karimi, Sajad; Poursabzi, Omid; Naderi, Bahman

doi:10.1016/j.asoc.2014.08.033

Cited by 65 publications

(24 citation statements)

References 29 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%

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%

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

Abdi¹,

Lak²,

Seyfari³

2017

Turk J Elec Eng & Comp Sci

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“…Additionally, the TSP has also been used as benchmarking problem for bioinspired techniques proposed in the last decade. Some of these recent developed meta-heuristics are the firefly algorithm ( [Jati et al(2013)Jati, Manurung & Suyanto, Li et al(2015), which is based on the social behavior of fireflies and the phenomenon of bioluminescent communication, the Cuckoo Search ( [Ouaarab et al(2014)Ouaarab, Ahiod & Yang]), which is inspired by the breeding behaviour of cuckoos, or the Imperialist Competitive Algorithm ( [Ardalan et al(2015)Ardalan, Karimi, Poursabzi & Naderi, Yousefikhoshbakht & Sedighpour(2013)]), which is a socio-politically motivated global search strategy, based on the imperialist competition of the countries.…”

Section: Related Workmentioning

confidence: 99%

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

Osaba

Yang

Díaz

et al. 2016

Engineering Applications of Artificial Intelligence

289

115

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Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric traveling salesman problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's ttest, the Holm's test, and the Friedman test. We have also compared the convergence behaviour shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

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“…Empires are formed with imperialist states and their colonies. Imperialistic competition between empires converge to one imperialist state which represent the optimum point of the ICA [37][38][39].…”

Section: Imperialist Competitive Algorithm (Ica)mentioning

confidence: 99%

“…ICA is a sociopolitically inspired optimization strategy that has been proposed to handle tough optimization problems [37]. This approach exhibits good performance in terms of convergence rate and improved global optimum [38,39]. The GWO algorithm is an original swarm intelligence technique stimulated by the leadership hierarchy and hunting structure of grey wolves.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

et al. 2018

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The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.

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A novel imperialist competitive algorithm for generalized traveling salesman problems

Cited by 65 publications

References 29 publications

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

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