Binary Differential Evolution Algorithm Research Articles

Feature Subspace Learning-based Binary Differential Evolution Algorithm for Unsupervised Feature Selection

Feature Subspace Learning-based Binary Differential Evolution Algorithm for Unsupervised Feature Selection

Transmission Expansion Planning using Binary Differential Evolution considering forecasted solar Photovoltaic generation

Transmission Expansion Planning (TEP) can be essentially affected by solar Photovoltaic (PV) penetration because its generation is uncertain depending on weather and the environment at a specific time. This uncertainty can impact transmission expansion planning because the uncertain generation of solar PV causes difficulty to expand transmission lines with secure system operations. The aim of the proposed TEP method is to construct transmission lines to handle the increase in solar PV sources and electricity demand while ensuring that the power system constraints are not violated. The goal of the objective function is to minimize the total cost of the system, which comprises both the investment cost and the operating cost of generation. Hourly resolution values of solar PV generation and electricity demand are considered in the planning. Moreover, to obtain accurate values of solar PV generation for planning a system with secure operations, this paper proposes a Long Short-Term Memory (LSTM) for forecasting solar PV generation. The Binary Differential Evolution (BDE) algorithm is applied to solve the TEP problem. The proposed TEP method is tested through an IEEE 24 bus test system. The results from the proposed method show that the obtained expansion plan provides the minimum total system cost without system violations.

HSLS: An efficient local search algorithm for the hardware and software partitioning problem

Hardware and software partitioning (HW/SW) is a crucial step in the co-design of embedded systems, which is a typical combinatorial optimisation problem. In practice, HW/SW instances would be of large scale, so it is of significant importance to design effective heuristic algorithms to obtain a high-quality partitioning scheme. Different from previous heuristics, the HW/SW problem is treated as the minimum weight dominating set (MWDS) problem in this letter, and the mature local search framework for solving the MWDS problem is introduced to solve the HW/SW problem. In addition, two suitable vertex selection rules based on scoring function and tabu are proposed. Combined with the introduced local search framework, an efficient local search algorithm named HSLS is obtained. 96 classic benchmark instances are used to evaluate the performance of the HSLS algorithm, and the four latest evolutionary algorithms designed for the HW/SW problem are used as comparison algorithms, which are genetic algorithm, binary particle swarm optimisation, differential evolution, and group theory-based optimisation algorithm. The experimental results show that the HSLS algorithm is far superior to the four comparison algorithms in terms of the optimal solution and obtains optimal solution on 78 benchmark instances, the four comparison algorithms obtain 43, 29, 42, and 41 optimal solutions respectively.

Memetic binary differential evolution to solve wind–thermal profit based unit commitment problem

Recently, the utilization of renewable energy sources (RESs) has gained popularity all over the world due to its numerous benefits. The RESs are a good choice as a cleaner power producer with economic benefits. Power generation from wind energy is rapidly growing all over the world as compared to other RESs. Literature review reveals that a combinatorial, mixed integers (binary and real) optimization problem also known as profit based unit commitment problem (PBUCP) has been solved for only thermal generating units without using RESs. These days, the PBUCP with the integration of RESs has gained intention. In this paper, the PBUCP is solved with the integration of wind generating units in a single objective framework, wherein objective is profit maximization, as well as multi-objective framework, wherein objectives are profit maximization and emissions minimization. A memetic binary differential evolution (MBDE) algorithm is proposed to solve the wind–thermal​ PBUCP. The proposed MBDE algorithm is a blending of binary differential evolution (BDE) and binary search optimizer (BSO). The proposed hybridization improves the global searching capability of the BDE algorithm while maintaining the effective exploitation capability of the BSO. The proposed BSO makes binary perturbations to ameliorate the performance of the BDE algorithm. The achievement of the proposed algorithms has been tested on two hybrid power systems that comprise wind generating units and thermal generating units. In order to know the impacts of wind power generation on thermal power generation, the results of the wind–thermal PBUCP are compared with the results of the thermal PBUCP in the single and multi-objective framework. The best results of the proposed algorithms are collated with the best results of already applied well known algorithms and are found superior.

Novel binary differential evolution algorithm based on Taper-shaped transfer functions for binary optimization problems

In order to efficiently solve the binary optimization problems by using differential evolution (DE), a class of new transfer functions, Taper-shaped transfer function, is firstly proposed by using power functions. Then, the novel binary differential evolution algorithm based on Taper-shaped transfer functions (T-NBDE) is proposed. T-NBDE transforms a real vector representing the individual encoding into a binary vector by using the Taper-shaped transfer function, which is suitable for solving binary optimization problems. For verifying the practicability of Taper-shaped transfer functions and the excellent performance of T-NBDE, T-NBDE is firstly compared with binary DE based on S-shaped, U-shaped and V-shaped transfer functions, respectively. Subsequently, it is compared with the state-of-the-art algorithms for solving the knapsack problem with a single continuous variable (KPC) and the uncapacitated facility location problem (UFLP). The comparison results show that Taper-shaped transfer functions are competitive than existing transfer functions, and T-NBDE is more effective than existing algorithms for solving KPC problem and UFLP problem.

Cloud e-mail security: An accurate e-mail spam classification based on enhanced binary differential evolution (BDE) algorithm

The evolution of technology has brought new challenges and opportunities for the different dimensions of feature space. The higher dimension of the feature space is one of the most critical issues in e-mail classification problems due to accuracy considerations. The problem of finding the subset features that significantly influence the performance of e-mail spam classification has become one of the important challenges. This paper proposes to overcome such a problem, an intelligent approach to Binary Differential Evolution Support Vector Machine (BDE-SVM). The proposed approach enhances the Binary Differential Evolution (BDE) algorithm based on the correlation coefficient as a fitness function to select the significant subset feature evaluated by an SVM classifier. To our best of knowledge, the correlation coefficient as the fitness function has not been used in the differential evolution algorithm before. The selected subset feature is used to assess the most features that contribute to the reliability of the email spam classification. The finding of the enhanced BDE is to present a powerful accuracy. The tests were conducted using “Spambase” and “SpamAssassin.” Identified benchmark datasets are to assess the feasibility of the proposed solution. The result with full-feature accuracy was 93.55 percent compared to the proposed BDE-SVM approach, which is 93.99 percent. Empirical findings also show that our method is capable of effectively increasing the number of features required to enhance the reliability of the email spam classification.

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain.

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

Near Infrared feature waveband selection for fishmeal quality assessment by frequency adaptive binary differential evolution

Fishmeal is a high-energy feed. Fishmeal quality has an important influence on animal growth. The detection of ash content is essential for evaluating fishmeal quality. Near infrared (NIR) spectroscopy is a rapid detection technique that can be used to determine the ash content in fishmeal. The wavelength related to the target component must be selected in NIR quantitative analysis considering that the NIR spectrum has a strong correlation. In this paper, the adaptive binary differential evolution (ABDE) algorithm was proposed for feature selection. The algorithm was an improvement of the mutation operator of binary differential evolution (BDE) and improved binary differential evolution (IBDE) algorithms. The performance of the BDE, IBDE, and ABDE algorithms was compared in secondary wavelength selection on 11 combined intervals selected by the move window partial least squares (MWPLS) algorithm. The performance of the model established by the ABDE algorithm in feature selection was superior to that constructed by the BDE and IBDE algorithms. Frequency ABDE (FABDE) was designed by introducing frequency into the ABDE algorithm to minimize the influence of random factors. The root mean square error (RMSET) and the correlation coefficient (RT) of the FABDE model for the test set were 1.160 ​% and 0.941, respectively. By comparison, the RMSET and the RT of the ABDE model were 1.212 ​% and 0.935, respectively. The results of model prediction indicated that the FABDE algorithm proposed herein can be feasibly used to improve the model effects for the rapid NIR analysis of ash content in fishmeal.

A New Hybrid Clustering Method of Binary Differential Evolution and Marine Predators Algorithm for Multi-omics Datasets

A New Hybrid Clustering Method of Binary Differential Evolution and Marine Predators Algorithm for Multi-omics Datasets

A general approach to solving hardware and software partitioning problem based on evolutionary algorithms

Hardware/software partitioning (HW/SW) is a significant problem in hardware-software co-design, and it is also an NP-hard problem. In order to solve the HW/SW quickly and effectively by evolutionary algorithms, the HW/SW is firstly regarded as a variant of knapsack problem. Based on a new greedy strategy, a greedy repair and optimization algorithm GROM is proposed to eliminate the infeasible solutions. Subsequently, a general algorithm framework based on discrete evolutionary algorithm for HW/SW problem is proposed. On the basis of the above algorithm framework, genetic algorithm (GA), binary particle swarm optimization (BPSO), binary differential evolution algorithm with hybrid encoding (HBDE) and group theory-based optimization algorithm (GTOA) are used to solve large-scale HW/SW instances. The feasibility and effectiveness of the algorithm framework proposed in the paper are verified by comparing the good and bad of the calculation results of above algorithms, and pointed out that the performance of GTOA and BPSO is better than that of HBDE and GA, they are more suitable for solving large-scale HW/SW problem.

S-shaped and V-shaped gaining-sharing knowledge-based algorithm for feature selection

In machine learning, searching for the optimal feature subset from the original datasets is a very challenging and prominent task. The metaheuristic algorithms are used in finding out the relevant, important features, that enhance the classification accuracy and save the resource time. Most of the algorithms have shown excellent performance in solving feature selection problems. A recently developed metaheuristic algorithm, gaining-sharing knowledge-based optimization algorithm (GSK), is considered for finding out the optimal feature subset. GSK algorithm was proposed over continuous search space; therefore, a total of eight S-shaped and V-shaped transfer functions are employed to solve the problems into binary search space. Additionally, a population reduction scheme is also employed with the transfer functions to enhance the performance of proposed approaches. It explores the search space efficiently and deletes the worst solutions from the search space, due to the updation of population size in every iteration. The proposed approaches are tested over twenty-one benchmark datasets from UCI repository. The obtained results are compared with state-of-the-art metaheuristic algorithms including binary differential evolution algorithm, binary particle swarm optimization, binary bat algorithm, binary grey wolf optimizer, binary ant lion optimizer, binary dragonfly algorithm, binary salp swarm algorithm. Among eight transfer functions, V4 transfer function with population reduction on binary GSK algorithm outperforms other optimizers in terms of accuracy, fitness values and the minimal number of features. To investigate the results statistically, two non-parametric statistical tests are conducted that concludes the superiority of the proposed approach.

A synergy of binary differential evolution and binary local search optimizer to solve multi-objective profit based unit commitment problem

Environmentally constrained profit based unit commitment problem (PBUCP) is a combinatorial multi-objective optimization problem experienced in deregulated power systems, which optimizes ON/OFF status of generating units to maximize economic benefits with less environmental impacts. The PBUCP has gained less attention in the multi-objective framework. The Kyoto Protocol and various incentive policies have forced generation companies to consider emission minimization as an objective function while solving the PBUCP. In most of the recent studies in the literature, emissions are considered as a constraint, not as an objective function. A few studies have considered the emissions as an objective and suggested the compromised solutions of the multi-objective PBUCP. A synergy of the binary differential evolution (BDE) algorithm and a binary local search optimizer (BLSO) is proposed in the present research to solve the multi-objective PBUCP. A novel BLSO is proposed which deals with binary variables while exploiting the local search and is implemented on the commitment part of the multi-objective PBUCP. The proposed BLSO makes perturbations in the unit status based on the priority of units to refine the optimal solution searched by the BDE algorithm. The efficacy of proposed algorithms has been investigated on the small, medium and large power systems. A comparison with previously known best solutions is performed for the validation of obtained results. Computed results of proposed algorithms are dominant than the already published algorithms applied to solve the multi-objective PBUCP.

A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm

In classification problems, normally there exists a large number of features, but not all of them contributing to the improvement of classification performance. These redundant features make the classification problem time consuming and often result in poor performance. Feature selection methods have been proposed to reduce the number of features, minimize computational cost, and maximize classification accuracy. As a wrapper-based approach, evolutionary algorithms have been widely applied in feature subset selection tasks. However, some of them trap into local minima, especially when the number of features increases, while others are not efficient in computational time. This paper proposes a Modified Differential Evolution (DE) approach to Feature Selection (MDEFS) by utilizing two new mutation strategies to create a feasible balance between exploration and exploitation and maintain the classification performance in an acceptable range concerning both the number of features and accuracy. Some modifications are made to the standard DE crossover and its key control parameters to enhance the proposed algorithm’s capabilities further. The proposed method has been compared to several state-of-the-art methods utilizing standard datasets from the UCI repository and results of the experiments demonstrate the superiority of the proposed approach.

A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling

Tool condition monitoring (TCM) in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. TCM based on multiple sensors can provide more information related to tool condition and is a topic of great interest. A novel TCM method for milling processes based on a two-layer angle kernel extreme learning machine (TAKELM) and binary differential evolution (BDE) was proposed. The TAKELM could enhance inherent feature extraction in microarray data and don’t require manual presetting of the kernel function or optimization of its hyperparameter. The BDE algorithm was applied to search the optimal feature parameter combinations in multi-domain alternative feature parameters to achieve the fewest feature parameters satisfying a very small prediction error. Finally, the performance of the proposed TAKELM- BDE method was verified on two milling TCM experiments (one open-access benchmark TCM data set and one TCM experiment) by comparison to the KELM- BDE, PCC- TAKELM, and mRMR- TAKELM methods. The results indicated that the mean absolute error, mean absolute percentage error, and root mean square error of the proposed method are less than 0.005 on two milling TCM experiments, and outperforms the other three methods.

Novel binary differential evolution algorithm for knapsack problems

The capability of the conventional differential evolution algorithm to solve optimization problems in continuous spaces has been well demonstrated and documented in the literature. However, differential evolution has been commonly considered inapplicable for several binary/permutation-based real-world problems because of its arithmetic reproduction operator. Moreover, many limitations of the standard differential evolution algorithm, such as slow convergence and becoming trapped in local optima, have been defined. In this paper, a novel technique which makes a simple differential evolution algorithm suitable and very effective for solving binary-based problems, such as binary knapsack ones, is proposed. It incorporates new components, such as representations of solutions, a mapping method and a diversity technique. Also, a new efficient fitness evaluation approach for calculating and, at the same time, repairing knapsack candidate solutions, is introduced. To assess the performance of this new algorithm, four datasets with a total of 44 instances of binary knapsack problems are considered. Its performance and those of 22 state-of-the-art algorithms are compared, with the experimental results demonstrating its superiority in terms of both the quality of solutions and computational times. It is also capable of finding new solutions which are better than the current best ones for five large knapsack problems.

A Feature Selection-based Approach for the Identification of Critical Components in Complex Technical Infrastructures: Application to the CERN Large Hadron Collider

Complex Technical Infrastructures (CTIs) are large-scale systems made of tens of thousands of interdependent components organized in complex hierarchical architectures. They evolve in time in a way that at one point their functional logic may be more complex than originally designed, and, therefore, traditional reliability/risk importance measures cannot be used for identifying the critical components on which the protection and recovery efforts should be primarily allocated. We propose an approach for identifying the most critical components based on the large amount of operational data collected from the CTI monitoring systems over long time periods and under different operational settings. The underlying idea is to develop binary classifiers to associate different combinations of measured signals to the CTI operating or failed state. The critical CTI components are those whose condition monitoring signals allow optimally classifying the CTI state. To identify the signals and to build the classifier, we consider a feature selection wrapper approach based on the combined use of Support Vector Machine classifiers and the Binary Differential Evolution algorithm for optimization. The approach is successfully applied to a real dataset collected from the CERN (European Centre for Nuclear Research) Large Hadron Collider, a CTI for experiments of physics.

A binary differential evolution algorithm for airline revenue management: a case study

In the current highly competitive airline market, many companies have failed due to their low revenue rates. For this reason, many of them have to develop strategies to increase their revenue. In this study, we develop revenue management (RM) strategy for the Iranian airline industry. More specifically, we present a mathematical model that considers some conditions not studied in previous research in order to provide a more realistic RM modeling of airlines that fits well for the special characteristics of Iranian Airways. A binary differential evolution algorithm is employed to solve the model due to the stochastic nature of data and the NP-hardness of the considered problem. To generate maximum revenue among the six types of airplanes that fly the four capital cities of Iran, the airline under investigation is advised to operate only 21 flights to those cities and cancel the rest of the flights.

Optimal performance of a self‐healing microgrid

This study proposes a multi-objective binary differential evolution algorithm to reconfigure the system and restore the loads within the stand-alone microgrid. The approach aims to attain a restoration path having maximum power flow with the minimum number of interrupted loads and switching operations. To execute the discrete optimisation strategy, the power network has been reframed as a capacitated graph, where the edges are capacitated by the loading capacity of the feeder. The strategy implements the maximum power flow theorem, which has been enhanced using the centrality index. The major operational constraints of the microgrid have been strictly followed to maintain system stability. The proposed restoration algorithm is examined on an islanded 13-bus microgrid system with the two test scenarios of irregular power generation, and the fault instance is isolating a healthy section of a microgrid. Moreover, the efficacy of the proposed approach has been further examined on a 39-bus system, and the results are compared with the other optimisation strategies implemented for restoration.

An Efficient Binary Clonal Selection Algorithm with Optimum Path Forest for Feature Selection

Feature selection is an important step in different applications such as data mining, classification, pattern recognition, and optimization. Until now, finding the most informative set of features among a large dataset is still an open problem. In computer science, a lot of metaphors are imported from nature and biology and proved to be efficient when applying them in an artificial way to solve a lot of problems. Examples include Neural Networks, Human Genetics, Flower Pollination, and Human Immune system. Clonal selection is one of the processes that happens in the human immune system while recognizing new infections. Mimicking this process in an artificial way resulted in a powerful algorithm, which is the Clonal Selection Algorithm. In this paper, we tried to explore the power of the Clonal Selection Algorithm in its binary form for solving the feature selection problem, we used the accuracy of the Optimum-Path Forest classifier, which is much faster than other classifiers, as a fitness function to be optimized. Experiments on three public benchmark datasets are conducted to compare the proposed Binary Clonal Selection Algorithm in conjunction with the Optimum Path Forest classifier with other four powerful algorithms. The four algorithms are Binary Flower Pollination Algorithm, Binary Bat Algorithm, Binary Cuckoo Search, and Binary Differential Evolution Algorithm. In terms of classification accuracy, experiments revealed that the proposed method outperformed the other four algorithms and moreover with a smaller number of features. Also, the proposed method took less average execution time in comparison with the other algorithms, except for Binary Cuckoo Search. The statistical analysis showed that our proposal has a significant difference in accuracy compared with the Binary Bat Algorithm and the Binary Differential Evolution Algorithm.

Public service hot issue discovery with binary differential evolution algorithm based on fuzzy system theory

Social media is becoming more and more closely related to the real life. More and more netizens choose to obtain news and publish notice through social networks. Such huge amount of social media information generated by these users contains a lot of information related to hot topics and events. At the same time, problem of information overload has posed a challenge for people to use the information. It has become an important research issue to discover and track hot events and topics automatically from mass social media data. On the one hand, the short, highly noisy and real-time features of the social media data bring challenges to the discovery and tracking methods of traditional hot issues. On the other hand, the social media data contains abundant information of geography, time, and social relations, which brings great convenience to relevant researches. Based on these features of the social media data, this paper makes a deep study on the discovery, extraction, and tracking of hot issues in the social media based on fuzzy system theory and the word vector semantic clustering.