Search Results (22)

Evolutionary Algorithms (EAs) are those based on the phenomenon of survival of the fittest. Differential Evolution (DE) is a member of this family, and it is well-suited for handling problems with real-valued variables. However, to use DE to solve binary problems, it is necessary to employ some adaptation. The primary objective of the present study is to develop a new binary version of DE. The proposed algorithm is called Binary Differential Evolution with a limited maximum number of dimension changes (NBDE), and it was tested with the OneMax Problem, five variants of the Knapsack Problem (KP), and Feature Selection (FS). The results showed that NBDE is competitive and superior to the other tested algorithms in many instances. For the 0/1 KP and 0/1 Multidimensional KP, NBDE outperforms all the other algorithms for all instances. For the FS problem, the proposed algorithm demonstrates good accuracy as its primary quality. The proposed algorithm exhibits a satisfactory performance, particularly in high-dimensional problems, which appears to be a quality inherited from the method that inspired its creation. This is particularly interesting because it provides empirical evidence that the importation of operators can perpetuate a pattern of behavior in algorithms with different structures. Full article

To produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining the position of the seed covered by the substrate within individual plug-tray holes, a novel method for detecting the growth points of plug seedlings has been proposed. It employs an adaptive grayscale processing algorithm based on the differential evolution extra-green algorithm to extract the contour features of seedlings during the early stages of cotyledon emergence. The pixel overlay curve peak points within the binary image of the plug-tray’s background are utilized to delineate the boundaries of the plug-tray holes. Each plug-tray hole containing a single seedling is identified by analyzing the area and perimeter of the seedling’s contour connectivity domains. The midpoint of the shortest line between these domains is designated as the growth point of the individual seedling. For laboratory-grown plug seedlings of tomato, pepper, and Chinese kale, the highest detection accuracy was achieved on the third-, fourth-, and second-days’ post-cotyledon emergence, respectively. The identification rate of missing seedlings and single seedlings exceeded 97.57% and 99.25%, respectively, with a growth-point detection error of less than 0.98 mm. For tomato and broccoli plug seedlings cultivated in a nursery greenhouse three days after cotyledon emergence, the detection accuracy for missing seedlings and single seedlings was greater than 95.78%, with a growth-point detection error of less than 2.06 mm. These results validated the high detection accuracy and broad applicability of the proposed method for various seedling types at the appropriate growth stages. Full article

Optimization algorithms play a crucial role in solving complex problems across various fields, including global optimization and feature selection (FS). This paper presents the enhanced polar lights optimization with cryptobiosis and differential evolution (CPLODE), a novel improvement upon the original polar lights optimization (PLO) algorithm. CPLODE integrates a cryptobiosis mechanism and differential evolution (DE) operators to enhance PLO’s search capabilities. The original PLO’s particle collision strategy is replaced with DE’s mutation and crossover operators, enabling a more effective global exploration and using a dynamic crossover rate to improve convergence. Furthermore, a cryptobiosis mechanism records and reuses historically successful solutions, thereby improving the greedy selection process. The experimental results on 29 CEC 2017 benchmark functions demonstrate CPLODE’s superior performance compared to eight classical optimization algorithms, with higher average ranks and faster convergence. Moreover, CPLODE achieved competitive results in feature selection on ten real-world datasets, outperforming several well-known binary metaheuristic algorithms in classification accuracy and feature reduction. These results highlight CPLODE’s effectiveness for both global optimization and feature selection. Full article

This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 lluminaHiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to Wilcoxon’s rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers. Full article

Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications, feature selection is one of the most important tasks in remote sensing image classification. In recent years, many metaheuristic algorithms have attempted to explore feature selection, such as the dragonfly algorithm (DA). Dragonfly algorithms have a powerful search capability that achieves good results, but there are still some shortcomings, specifically that the algorithm’s ability to explore will be weakened in the late phase, the diversity of the populations is not sufficient, and the convergence speed is slow. To overcome these shortcomings, we propose an improved dragonfly algorithm combined with a directed differential operator, called BDA-DDO. First, to enhance the exploration capability of DA in the later stages, we present an adaptive step-updating mechanism where the dragonfly step size decreases with iteration. Second, to speed up the convergence of the DA algorithm, we designed a new differential operator. We constructed a directed differential operator that can provide a promising direction for the search, then sped up the convergence. Third, we also designed an adaptive paradigm to update the directed differential operator to improve the diversity of the populations. The proposed method was tested on 14 mainstream public UCI datasets. The experimental results were compared with seven representative feature selection methods, including the DA variant algorithms, and the results show that the proposed algorithm outperformed the other representative and state-of-the-art DA variant algorithms in terms of both convergence speed and solution quality. Full article

Solar PhotoVoltaics (PV) integration into the electricity grids significantly increases the complexity of Transmission Expansion Planning (TEP) because solar PV power generation is uncertain and difficult to predict. Therefore, this paper proposes the optimal planning method for transmission expansion combined with uncertain solar PV generation. The problem of uncertain solar PV generation is solved by using Long Short-Term Memory (LSTM) for forecasting solar radiation with high accuracy. The objective function is to minimize total system cost, including the investment cost of new transmission lines and the operating cost of power generation. The optimal TEP problem is solved by the Binary Differential Evolution (BDE) algorithm. To investigate and demonstrate the performance of the proposed method, the IEEE 24-bus system and solar radiation data in Thailand are selected as a study case for TEP. The MATPOWER program written in MATLAB software is used for solving optimal power flow problems. Simulation results show that the proposed optimal TEP method combined with forecasting solar PV power generation using the LSTM can reduce the total system cost of the transmission expansion by 9.12% compared with the cost obtained by the TEP using solar radiation from statistical data. Full article

Personalized learning path considers matching symmetrical attributes from both learner and learning material. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the matching of the following symmetrical attributes of learner/material: ability level/difficulty level, learning objective/covered concept, learning style/supported learning styles, and expected learning time/required learning time. The prerequisites of material are considered constraints. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show that the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency. Full article

Energy consumption schedulers have been widely adopted for energy management in smart microgrids. Energy management aims to alleviate energy expenses and peak-to-average ratio (PAR) without compromising user comfort. This work proposes an energy consumption scheduler using heuristic optimization algorithms: Binary Particle Swarm Optimization (BPSO), Wind Driven Optimization (WDO), Genetic Algorithm (GA), Differential Evolution (DE), and Enhanced DE (EDE). The energy consumption scheduler based on these algorithms under a price-based demand response program creates a schedule of home appliances. Based on the energy consumption behavior, appliances within the home are classified as interruptible, noninterruptible, and hybrid loads, considered as scenario-I, scenario-II, and scenario-III, respectively. The developed model based on optimization algorithms is the more appropriate solution to achieve the desired objectives. Simulation results show that the expense and PAR of schedule power usage in each scenario are less compared to the without-scheduling case. Full article

A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes. Full article

The paper presents a neuro-evolutionary algorithm called Hill Climb Assembler Encoding (HCAE) which is a light variant of Hill Climb Modular Assembler Encoding (HCMAE). While HCMAE, as the name implies, is dedicated to modular neural networks, the target application of HCAE is to evolve small/mid-scale monolithic neural networks which, in spite of the great success of deep architectures, are still in use, for example, in robotic systems. The paper analyses the influence of different mechanisms incorporated into HCAE on the effectiveness of evolved neural networks and compares it with a number of rival algorithms. In order to verify the ability of HCAE to evolve effective small/mid-scale neural networks, both feed forward and recurrent, it was tested on fourteen identification problems including the two-spiral problem, which is a well-known binary classification benchmark, and on two control problems, i.e., the inverted-pendulum problem, which is a classical control benchmark, and the trajectory-following problem, which is a real problem in underwater robotics. Four other neuro-evolutionary algorithms, four particle swarm optimization methods, differential evolution, and a well-known back-propagation algorithm, were applied as a point of reference for HCAE. The experiments reported in the paper revealed that the evolutionary approach applied in the proposed algorithm makes it a more effective tool for solving the test problems than all the rivals. Full article

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models. Full article

Microwave electromagnetic devices have been used for many applications in tropospheric communication, navigation, radar systems, and measurement. The development of the signal preprocessing units including frequency-selective devices (bandpass filters) determines the reliability and usability of such systems. In wireless sensor network nodes, filters with microstrip resonators are widely used to improve the out-of-band suppression and frequency selectivity. Filters based on multimode microstrip resonators have an order that determines their frequency-selective properties, which is a multiple of the number of resonators. That enables us to reduce the size of systems without deteriorating their selective properties. Various microstrip multimode resonator topologies can be used for both filters and microwave sensors, however, the quality criteria for them may differ. The development of every resonator topology is time consuming. We propose a technique for the automatic generation of the resonator topology with required frequency characteristics based on the use of evolutionary algorithms. The topology is encoded into a set of real valued parameters, which are varied to achieve the desired features. The differential evolution algorithm and the genetic algorithm with simulated binary crossover and polynomial mutation are applied to solve the formulated problem using the dynamic penalties method. The experimental results show that our technique enables us to find microstrip resonator topologies with desired amplitude-frequency characteristics automatically, and manufactured devices demonstrate characteristics very close to the results of the algorithm. The proposed algorithmic approach may be used for automatically exploring the new perspective topologies of resonators used in microwave filters, radar antennas or sensors, in accordance with the defined criteria and constraints. Full article

With the smart grid development, the modern electricity market is reformatted, where residential consumers can actively participate in the demand response (DR) program to balance demand with generation. However, lack of user knowledge is a challenging issue in responding to DR incentive signals. Thus, an Energy Management Controller (EMC) emerged that automatically respond to DR signal and solve energy management problem. On this note, in this work, a hybrid algorithm of Enhanced Differential Evolution (EDE) and Genetic Algorithm (GA) is developed, namely EDGE. The EMC is programmed based with EDGE algorithm to automatically respond to DR signals to solve energy management problems via scheduling three types of household load: interruptible, non-interruptible, and hybrid. The EDGE algorithm has critical features of both algorithms (GA and EDE), enabling the EMC to generate an optimal schedule of household load to reduce energy expense, carbon emission, Peak to Average Ratio (PAR), and user discomfort. To validate the proposed EDGE algorithm, simulations are conducted compared to the existing algorithms like Binary Particle Swarm Optimization (BPSO), GA, Wind Driven Optimization (WDO), and EDE. Results illustrate that the proposed EDGE algorithm outperforms benchmark algorithms in energy expense minimization, carbon emission minimization, PAR alleviation, and user discomfort maximization. Full article

QUasi-Affine TRansformation Evolutionary (QUATRE) algorithm generalized differential evolution (DE) algorithm to matrix form. QUATRE was originally designed for a continuous search space, but many practical applications are binary optimization problems. Therefore, we designed a novel binary version of QUATRE. The proposed binary algorithm is implemented using two different approaches. In the first approach, the new individuals produced by mutation and crossover operation are binarized. In the second approach, binarization is done after mutation, then cross operation with other individuals is performed. Transfer functions are critical to binarization, so four families of transfer functions are introduced for the proposed algorithm. Then, the analysis is performed and an improved transfer function is proposed. Furthermore, in order to balance exploration and exploitation, a new liner increment scale factor is proposed. Experiments on 23 benchmark functions show that the proposed two approaches are superior to state-of-the-art algorithms. Moreover, we applied it for dimensionality reduction of hyperspectral image (HSI) in order to test the ability of the proposed algorithm to solve practical problems. The experimental results on HSI imply that the proposed methods are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Full article

Artificial Bee Colony (ABC) is a Swarm Intelligence optimization algorithm well known for its versatility. The selection of decision variables to update is purely stochastic, incurring several issues to the local search capability of the ABC. To address these issues, a self-adaptive decision variable selection mechanism is proposed with the goal of balancing the degree of exploration and exploitation throughout the execution of the algorithm. This selection, named Adaptive Decision Variable Matrix (A-DVM), represents both stochastic and deterministic parameter selection in a binary matrix and regulates the extent of how much each selection is employed based on the estimation of the sparsity of the solutions in the search space. The influence of the proposed approach to performance and robustness of the original algorithm is validated by experimenting on 15 highly multimodal benchmark optimization problems. Numerical comparison on those problems is made against the ABC and their variants and prominent population-based algorithms (e.g., Particle Swarm Optimization and Differential Evolution). Results show an improvement in the performance of the algorithms with the A-DVM in the most challenging instances. Full article