Social-Based Algorithm (SBA)

Ramezani, Fatemeh; Lotfi, Shahriar

doi:10.1016/j.asoc.2012.05.018

Cited by 127 publications

(48 citation statements)

References 20 publications

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“…Lin et al [15] presented 2 variants of ICA called ICAAI and ICACI; the former one controls frequency of the competition process by introducing a new parameter, ρ , and the latter one applies uniform crossover between imperialists for exchanging information about the imperialist with other empires. Ramezani et al [13] proposed a new hybrid method called SBA, which combines evolutionary algorithm and ICA features. They presented promising results from their proposed algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Other methods take advantage of the strength of other EAs and ICA together in an algorithm (e.g., [12][13][14]). In this section, we briefly review these methods.…”

Section: Related Workmentioning

confidence: 99%

“…Our implementation was done in a machine built with an Intel Core i3 processor, 2-GB RAM, and the platform used was Windows 7. In computational study, the GICA was compared with the most outstanding variants of the ICA (SBA [13], ICAAI [15], ICACI [15]) and 3 other EAs (ABC [18], PSO [2], SaDE [19]). …”

Section: Computational Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

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%

“…Other methods take advantage of the strength of other EAs and ICA together in an algorithm (e.g., [12][13][14]). In this section, we briefly review these methods.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

Abdi¹,

Lak²,

Seyfari³

2017

Turk J Elec Eng & Comp Sci

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“…Social spider optimization (SSO) algorithm is proposed by Erik Cuevas in 2013 [7], it is a novel meta-heuristic optimization algorithm based on the simulation of socialspider whose members (spider species) maintain a set of complex of cooperative behavior, such as predation, mating, web design, and social interaction [23], [33]. The SSO algorithm is used to multilevel image thresholding, engineer optimization [27].…”

mentioning

confidence: 99%

Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem

Zhou¹,

Zhou²,

Zhao³

2021

Journal of Industrial &Amp; Management Optimization

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The job-shop scheduling problem is one of the well-known hardest combinatorial optimization problems. The problem has captured the interest of a significant number of researchers, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. In this paper, a hybrid social-spider optimization algorithm with differential mutation operator is presented to solve the job-shop scheduling problem. To improve the exploration capabilities of the social spider optimization algorithm (SSO), we incorporate the DM operator (a mutation operator taken from the deferential evolutionary (DE) algorithm) into the framework of the female cooperative operator. The experimental results show that the proposed method effectiveness in solving job-shop scheduling compared to other optimization algorithms in the literature.2010 Mathematics Subject Classification. Primary: 78M50, 90C27.

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“…Simulated Annealing [12] SA Gravitational Search Algorithm [13] GSA Charged System Search [14] CSS Black Hole Algorithm [15] BH Emperor Penguin Optimizer [16] EPO Artificial Chemical Reaction Optimization Algorithm [17] ACROA Ray Optimization Algorithm [18] RO Galaxy-Based Search Algorithm [19] GbSA Ant Colony Optimization [20] ACO Cuckoo Search [21] CS Bat-Inspired Algorithm [22] BA Firefly Algorithm [23] FA Spotted Hyena Optimizer [24] SHO Exchange Market Algorithm [25] EMA Social-Based Algorithm [26] SBA Harmony Search [27] HS Grey Wolf Optimizer [28] GWO Mine Blast Algorithm [29] MBA Every optimization algorithm needs to address the exploration and exploitation of a search space [30] and maintains a good balance between exploration and exploitation. The exploration phase investigates the different promising regions in a search space, whereas exploitation searches the close global optimal solutions around the promising regions [31,32].…”

Section: Algorithms Abbreviationmentioning

confidence: 99%

Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer

Dhiman

Kaur

2018

Designs

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This paper presents the contemporary metaheuristic optimization algorithm named the Spotted Hyena Optimizer (SHO). The proposed technique is based on the law of gravitation and simulates the social behavior of spotted hyenas. The three basic steps of SHO, namely, searching for prey, encircling, and attacking prey, are mathematically modelled and discussed. The main concept of this work is to apply the recently developed SHO algorithm to two real-life design problems, namely optical buffer design and airfoil design. Experimental results reveal the supremacy of the SHO algorithm for solving the engineering design problems as compared to other competitor algorithms.

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Social-Based Algorithm (SBA)

Cited by 127 publications

References 20 publications

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

GICA: Imperialist competitive algorithm with globalization mechanism for optimization problems

Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem

Optimizing the Design of Airfoil and Optical Buffer Problems Using Spotted Hyena Optimizer

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