Election algorithm: A new socio-politically inspired strategy

Emami, Hojjat; Derakhshan, Farnaz

doi:10.3233/aic-140652

Cited by 75 publications

(61 citation statements)

References 23 publications

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“…If an environment is considered as a function, EAs serve as a function optimizer. In this case, each individual in a population is a sample point in the function space [25][26].…”

Section: Election Algorithm (Ea)mentioning

confidence: 99%

“…In every phase, the vector will be updated through the campaign and coalition mechanism, and their eligibility values will be evaluated to ascertain the best candidates or voters' position. The cycle of reorganizing the new weight vector variable with repeat the best individuals vector and continue the searching until an appropriate solution is reached (best solution) [25][26]. The HNN energy dynamics is used as the second eligibility assessment method to pick the most appropriate weight matrix to solve the problem of random-kSAT.…”

Section: Election Algorithm (Ea)mentioning

confidence: 99%

“…The number of variable vector values that are transferred from a candidate toward its supporters is represented in eq. (31) as follows [25].…”

Section: Stage 4 Advertising Campaignmentioning

confidence: 99%

“…All the followers ' supporters become the leader's supporters. In our applications, among the candidates who wish to unite, a candidate is randomly selected as the successor candidate and the remaining candidates are considered as followers [25][26].…”

Section: Coalitionmentioning

confidence: 99%

“…Recently, EA has emerged a new evolutionary metaheuristics algorithm that has been applied in finding an optimal solution to computational optimization and engineering application [25][26]. Unlike many other metaheuristics that are mainly inspired by swam or natural evolutionary process.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Abubakar¹,

Sathasivam²,

Mansor³

et al. 2020

Preprint

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Election Algorithm (EA) is a powerful metaheuristics model motivated by phenomena of the socio-political mechanism of the presidential election conducted in many countries. EA is selected as a topic of discussion due to its capability and robustness to carry out complex problems in the random-2SAT logic program. This paper utilizes a hybridized EA assimilated with the Hopfield neural network (HNN) in carrying out random logic program (HNN-R2SATEA). The efficiency of the proposed method was compared with the existing traditional exhaustive search (HNN-R2SATES) model and the recently introduced HNN-R2SATICA model. From the result obtained, clearly proven that based on our proposed hybrid model outperformed other existing model based on the Global Minima Ratio (ZM), Mean Absolute Error (MAE), Bayesian Information Criterion (BIC) and Execution Time (ET). The expected outcome portrays that the EA algorithm outperformed the other two algorithms in doing random-kSAT logic program. The results proved the robustness, effectiveness, and compatibility of the HNN-R2SATEA model.

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“…If an environment is considered as a function, EAs serve as a function optimizer. In this case, each individual in a population is a sample point in the function space [25][26].…”

Section: Election Algorithm (Ea)mentioning

confidence: 99%

Section: Election Algorithm (Ea)mentioning

confidence: 99%

“…The number of variable vector values that are transferred from a candidate toward its supporters is represented in eq. (31) as follows [25].…”

Section: Stage 4 Advertising Campaignmentioning

confidence: 99%

Section: Coalitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Abubakar¹,

Sathasivam²,

Mansor³

et al. 2020

Preprint

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Ambidextrous Socio-Cultural Algorithms

Lemus-Romani

Crawford

Soto

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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Metaheuristics are a class of algorithms with some intelligence and self-learning capabilities to find solutions to difficult combinatorial problems. Although the promised solutions are not necessarily globally optimal, they are computationally economical. In general, these types of algorithms have been created by imitating intelligent processes and behaviors observed in nature, sociology, psychology and other disciplines. Metaheuristic-based search and optimization is currently widely used for decision making and problem solving in different contexts. The inspiration for metaheuristic algorithms are mainly based on nature’s behaviour or biological behaviour. Designing a good metaheurisitcs is making a proper trade-off between two forces: Exploration and exploitation. It is one of the most basic dilemmas that both individuals and organizations constantly are facing. But there is a little researched branch, which corresponds to the techniques based on the social behavior of people or communities, which are called Social-inspired. In this paper we explain and compare two socio-inspired metaheuristics solving a benchmark combinatorial problem.

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Controller Tuning by Metaheuristics Optimization

Blondin

2021

SpringerBriefs in Optimization

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Election algorithm: A new socio-politically inspired strategy

Cited by 75 publications

References 23 publications

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Ambidextrous Socio-Cultural Algorithms

Controller Tuning by Metaheuristics Optimization

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