JayaX: Jaya algorithm with xor operator for binary optimization

ASLAN, Murat; Gündüz, Mesut; Kıran, Mustafa Servet

doi:10.1016/j.asoc.2019.105576

Cited by 83 publications

(35 citation statements)

References 47 publications

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“…(1) Input: the data set after feature extracted (2) Output: best feature subset (3) Get training set and test set after the features have been extracted; (4) Initialize population, such as initial position, velocity and fitness values of particles, as well as local optimal pbest and global optimal gbest of particles; (5) for k � 1 ⟶ maxIterations do (6) for i � 1 ⟶ swarmSize do (7) Update inertia weight using equation 10; (8) for j � 1 ⟶ dimension do (9) Update a and p; (10) Update A and C, respectively, using equations (19) and 20; (11) Update ϖ t 1 and ϖ t 2 , respectively, using equations (13) and 14; (12) Update velocity of particle using equation 11; (13) Update position of particle using equation 18; (14) end for (15) end for (16) for i � 1 ⟶ swarmSize do (17) Update fitness value fitness i ; (18) Update local optimal pbest; (19) end for (20) Update global optimal gbest; (21) end for (22) Getting the optimal feature subset selected by NBPSOSEE; (23) Delete a feature f in the current feature subset using equation 26; (24) Update the optimal feature subset using equation 27; (25) Repeat steps 23 and 24 until the termination condition is met; ALGORITHM 2: NBPSOSEE-SBS feature selection. 8 Mathematical Problems in Engineering e logistic regression model is trained by training set with the optimal feature subset selected by the NBPSOSEE-SBS algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Yang et al [23] propose a novel binary Jaya optimization algorithm, which is integrated with the lambda iteration method to transform the dual objectives of economy and emission commitment into a single objective problem. Aslan et al [24] proposed the JayaX binary optimization algorithm replacing Jaya's solution update rules with XOR operator and compared the results with the latest algorithm, which can produce better quality results in binary optimization problems. e whale optimization algorithm (WOA) [25,26] was proposed, using the wrapperbased method to reach the optimal subset of features and effectively improve the accuracy of classification.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

Suxiang

Shi

et al. 2020

Mathematical Problems in Engineering

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In order to extract more information that affects customer arrears behavior, the feature extraction method is used to extend the low-dimensional features to the high-dimensional features for the warning problem of user arrears risk model of electric charge recovery (ECR). However, there are many irrelevant or redundant features in data, which affect prediction accuracy. In order to reduce the dimension of the feature and improve the prediction result, an improved hybrid feature selection algorithm is proposed, integrating nonlinear inertia weight binary particle swarm optimization with shrinking encircling and exploration mechanism (NBPSOSEE) with sequential backward selection (SBS), namely, NBPSOSEE-SBS, for selecting the optimal feature subset. NBPSOSEE-SBS can not only effectively reduce the redundant or irrelevant features from the feature subset selected by NBPSOSEE but also improve the accuracy of classification. The experimental results show that the proposed NBPSOSEE-SBS can effectively reduce a large number of redundant features and stably improve the prediction results in the case of low execution time, compared with one state-of-the-art optimization algorithm, and seven well-known wrapper-based feature selection approaches for the risk prediction of ECR for power customers.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

Suxiang

Shi

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In this study, shuffled frog leaping algorithm and tree seed algorithm which are continuous optimization methods are proposed. Aslan et al [29] have mentioned in detail discrete optimization method. All algorithms run under the same conditions and the stopping criterion is determined as the number of function evaluations (FEs).…”

Section: Proposed Algorithmsmentioning

confidence: 99%

New Metaheuristic Algorithms for Reactive Power Optimization

2019

Teh. vjesn.

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Optimal reactive power dispatch (ORPD) is significant regarding operating the practice safely and efficiently. The ORPD is beneficial to recover the voltage profile, diminish the losses and increase the voltage stability. The ORPD is a complicated optimization issue in which the total active power loss is reduced by detecting the powersystem control variables, like generator voltages, tap ratios of tap-changer transformers, and requited reactive power, ideally. This study offers new approaches based on Shuffled Frog Leaping Algorithm (SFLA) and Tree Seed Algorithm (TSA) to solve the best ORPD. The results of the approaches are offered set against the current results studied in the literature. The recommended algorithms were tested by IEEE-30 and IEEE-118 bus systems to discover the optimal reactive power control variables. It was observed that the obtained results are more successful than the other algorithms.

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“…Some of optimization-based local search algorithms are Tabu Search (TS) algorithm [34] and Simulated Annealing (SA) algorithm [35]. In recently, optimization-based metaheuristic algorithms are used for many different optimization problems, because of problem free, has a simple structure and easy adaptable to any optimization problems [36]. Some of these metaheuristic algorithms for solving data clustering problems are such as Genetic Algorithm (GA) [37,38], Teacher Learning Based Optimization (TLBO) [39], Ant Colony Optimization (ACO) [40], Artificial Bee Colony (ABC) [3,41], Gravitational Search Algorithm (GSA) [42], Particle Swarm Optimization (PSO) [43], Grey Wolf Optimizer (GWO) [4] and Cuckoo Search (CS) [44,45] algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Approach Based on Tunicate Swarm Algorithm to Solve Partitional Clustering Problem

ASLAN

2021

Balkan Journal of Electrical and Computer Engineering

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The tunicate swarm algorithm (TSA) is a newly proposed population-based swarm optimizer for solving global optimization problems. TSA uses best solution in the population in order improve the intensification and diversification of the tunicates. Thus, the possibility of finding a better position for search agents has increased. The aim of the clustering algorithms is to distributed the data instances into some groups according to similar and dissimilar features of instances. Therefore, with a proper clustering algorithm the dataset will be separated to some groups and it's expected that the similarities of groups will be minimum. In this work, firstly, an approach based on TSA has proposed for solving partitional clustering problem. Then, the TSA is implemented on ten different clustering problems taken from UCI Machine Learning Repository, and the clustering performance of the TSA is compared with the performances of the three well known clustering algorithms such as fuzzy c-means, k-means and k-medoids. The experimental results and comparisons show that the TSA based approach is highly competitive and robust optimizer for solving the partitional clustering problems.

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JayaX: Jaya algorithm with xor operator for binary optimization

Cited by 83 publications

References 47 publications

An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

An Improved Hybrid Feature Selection Algorithm for Electric Charge Recovery Risk

New Metaheuristic Algorithms for Reactive Power Optimization

An Approach Based on Tunicate Swarm Algorithm to Solve Partitional Clustering Problem

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