In this paper, a new hybrid optimization algorithm which combines the standard Butterfly Optimization Algorithm (BOA) with Artificial Bee Colony (ABC) algorithm is proposed. The proposed algorithm used the advantages of both the algorithms in order to balance the trade-off between exploration and exploitation. Experiments have been conducted on the proposed algorithm using ten benchmark problems having a broad range of dimensions and diverse complexities. The simulation results demonstrate that the convergence speed and accuracy of the proposed algorithm in finding optimal solutions is significantly better than BOA and ABC.

/pdf/an-effective-hybrid-butterfly-optimization-algorithm-with-3rcqol1lvi.pdf

An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization

In wireless sensor networks, cluster head selection and routing are two well-known optimization problems associated with high computational complexity. Harmony search algorithm (HSA) is one of the metaheuristics, used to solve a wide range of NP-Hard problems. In this paper, first we propose an HSA-based cluster head (CH) selection algorithm by devising a fitness function with energy, distance and node degree as parameters. Next, we derived a potential function for the assignment of non-CH nodes to the CHs. Finally, an HSA-based routing algorithm is also proposed using the same parameters, i.e., energy, distance and node degree in the derivation of the fitness function. Three test cases have been considered in this study for performance evaluation. The proposed algorithm has been tested with some of the existing related techniques. Simulation results depict that the proposed algorithm (CRHS) shows superior performance over the existing techniques.

CRHS: clustering and routing in wireless sensor networks using harmony search algorithm

After entering the big data era, there is an increasing demand on data analysis. It is natural for the modern actuary to question tech buzzwords like “machine learning” and “data analytics.” In reality, many machine-learning models have a basis in the very concepts, which actuaries have used to assess risk for a long time. We refer to the machine learning techniques that deal most explicitly with probabilities and risks as probability-based learning, and will focus on applying probability-base learning models on a set of car insurance data to create an artificial intelligence to accelerate the underwriting process for property and casualty insurance pricing. Introduction The goal of probability-based learning is given some relevant information and the model is to inform us of a range of probable outcomes. In other words, we would like our model to train on data such that it may accurately predict the value of a target feature in a data set when given the values taken by the set of features, which may influence it. We refer to these features as descriptive features.

/pdf/research-on-probability-based-learning-application-on-car-maorf9s55p.pdf

Research on Probability-based Learning Application on Car Insurance Data

A Bayesian network is a probabilistic graphical model. Many conventional methods have been proposed for its construction. However, these methods often result in an incorrect Bayesian network structure. In this study, to correctly construct a Bayesian network, we extend the concept of propositional logic. We propose a methodology for constructing a Bayesian network with causal relationships that are extracted only if the antecedent states are true. In order to determine the logic to be used in constructing the Bayesian network, we propose the use of association rule mining such as the Apriori algorithm. We evaluate the proposed method by comparing its result with that of traditional method, such as Bayesian Dirichlet equivalent uniform (BDeu) score evaluation with a hill climbing algorithm, that shows that our method generates a network with more necessary arcs than that generated by the traditional method.

Construction of a Bayesian network as an extension of propositional logic

Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud. The efficiency of BinHHO is analyzed with other popular MH algorithms. The BinHHO has achieved the highest accuracy of 99.95% and demonstrates a more significant positive effect on the performance of the proposed model. 

Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning), and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks.

/pdf/a-probability-based-evolutionary-algorithm-with-mutations-to-1aw6q7jv91.pdf

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

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A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks