Modified election algorithm in hopfield neural network for optimal random<i>k</i>satisfiability representation

Abubakar, Hamza; Sabri, Shamsul Rijal Muhammad; Masanawa, Sagir Abdu; Yusuf, Surajo

doi:10.1051/smdo/2020008

Cited by 11 publications

(18 citation statements)

References 35 publications

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“…We definedS i as the state of the neuron i in HNN and ς is the predefined value. The value of ς = 0.001 has been specified in [6], [3], [20] to certify that the network's energy decreases to zero. The synaptic weight connection in the discrete HNN contains no connection with itself, the synaptic connection from one neuron to other neurons is zero.…”

Section: Random Ksatisfiability In Hopfield Neural Networkmentioning

confidence: 99%

“…Thus, the robustness of Election algorithm manage in improving the training process in Hopfield model. Consequently, the quality of the final neuronal state can be maintained according to Equation (11) as utilized in [6], [4], [3], [20] as follows.…”

Section: Random Ksatisfiability In Hopfield Neural Networkmentioning

confidence: 99%

“…where ξis the pre-determined tolerance value. The value ξ = 0.001 was taken in [6], [4], [3], [20], [19], [2][10-11,18-21]. If the F RANkS AT logical representation embedded in HNN does not satisfy the criteria state in Equation ( 11) then the final state of the neurons have been trapped in the wrong pattern.…”

Section: Random Ksatisfiability In Hopfield Neural Networkmentioning

confidence: 99%

“…The primary goal of EA is to encourage candidates to coverge on a global minimum solution (best solution) to optimzation problem [10]. Optimization process based on standard Hopfield model has a high probability of becoming caught at a suboptimal solution with the number of neurons firing into the network [3], [9], [7].…”

Section: Election Algorithm As Heuristic Search In Hopfield Learning Phasementioning

confidence: 99%

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Optimal representation to High Order Random Boolean kSatisability via Election Algorithm as Heuristic Search Approach in Hopeld Neural Networks

Abubakar

Sagir²,

Yusuf³

et al. 2021

J. Nig. Soc. Phys. Sci.

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This study proposed a hybridization of higher-order Random Boolean kSatisfiability (RANkSAT) with the Hopfield neural network (HNN) as a neuro-dynamical model designed to reflect knowledge efficiently. The learning process of the Hopfield neural network (HNN) has undergone significant changes and improvements according to various types of optimization problems. However, the HNN model is associated with some limitations which include storage capacity and being easily trapped to the local minimum solution. The Election algorithm (EA) is proposed to improve the learning phase of HNN for optimal Random Boolean kSatisfiability (RANkSAT) representation in higher order. The main source of inspiration for the Election Algorithm (EA) is its ability to extend the power and rule of political parties beyond their borders when seeking endorsement. The main purpose is to utilize the optimization capacity of EA to accelerate the learning phase of HNN for optimal random k Satisfiability representation. The global minima ratio (mR) and statistical error accumulations (SEA) during the training process were used to evaluate the proposed model performance. The result of this study revealed that our proposed EA-HNN-RANkSAT outperformed ABC-HNN-RANkSAT and ES-HNN-RANkSAT models in terms of mR and SEA.This study will further be extended to accommodate a novel field of Reverse analysis (RA) which involves data mining techniques to analyse real-life problems.

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Section: Random Ksatisfiability In Hopfield Neural Networkmentioning

confidence: 99%

Section: Random Ksatisfiability In Hopfield Neural Networkmentioning

confidence: 99%

Section: Random Ksatisfiability In Hopfield Neural Networkmentioning

confidence: 99%

Section: Election Algorithm As Heuristic Search In Hopfield Learning Phasementioning

confidence: 99%

See 2 more Smart Citations

Optimal representation to High Order Random Boolean kSatisability via Election Algorithm as Heuristic Search Approach in Hopeld Neural Networks

Abubakar

Sagir²,

Yusuf³

et al. 2021

J. Nig. Soc. Phys. Sci.

Self Cite

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show abstract

“…However, there are a plethora of studies on the application of applications and uses of metaheuristics algorithm (MA) and artificial intelligence (AI) based techniques. These studies include a simulated algorithm (Abbasi et al 2006), genetic algorithm (Alzaeemi & Sathasivam 2020) and election algorithm (EA) (Sathasivam et al, 2020;Abubakar et al, 2020a;Abubakar Danrimi, 2021) and artificial dragonfly algorithm (Abubakar et al, 2020b). One of the purposes of incorporating MA was to maximise the fitness function for optimal representation.…”

Section: Introductionmentioning

confidence: 99%

Simulation Study on Modified Weibull Distribution for Modelling of Investment Return

Abubakar¹,

Sabri²

2021

JST

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The Weibull distribution is one of the most popular statistical models extensively applied to lifetime data analysis such as survival data, reliability data, wind speed, and recently in financial data, due to itsts flexibility to adaptably imitate different families of statistical distributions. This study proposed a modified version of the two-parameter Weibull distribution by incorporating additional parameters in the internal rate of return and insurance claims data. The objective is to examine the behaviour of investment return on the assumption of the proposed model. The proposed and the existing Weibull distribution parameters have been estimated via a simulated annealing algorithm. Experimental simulations have been conducted mimicking the internal rate of return (IRR) data for both short time (small sample) and long-term investment periods (large samples). The performance of the proposed model has been compared with the existing two-parameter Weibull distribution model in terms of their R-square (R2), mean absolute error (MAE), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the Kolmogorov-Smirnov test (KS). The numerical simulation revealed that the proposed model outperformed the existing two-parameter Weibull distribution model in terms of accuracy, robustness, and sensitivity. Therefore, it can be concluded that the proposed model is entirely suitable for the long-term investment period. The study will be extended using the internal rate of return real data set. Furthermore, a comparison of the various Weibull distribution parameter estimators such as metaheuristics or evolutionary algorithms based on the proposed model will be carried out.

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Socio-inspired evolutionary algorithms: a unified framework and survey

2023

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Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin's ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic Algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description of each SIEA enables a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

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Modified election algorithm in hopfield neural network for optimal randomksatisfiability representation

Cited by 11 publications

References 35 publications

Optimal representation to High Order Random Boolean kSatisability via Election Algorithm as Heuristic Search Approach in Hopeld Neural Networks

Optimal representation to High Order Random Boolean kSatisability via Election Algorithm as Heuristic Search Approach in Hopeld Neural Networks

Simulation Study on Modified Weibull Distribution for Modelling of Investment Return

Socio-inspired evolutionary algorithms: a unified framework and survey

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