Niching‐Based Two‐Stage Differential Evolution for Feature Selection

Feature selection plays an essential role in enhancing the efficiency and effectiveness of machine learning models, particularly in the context of large-scale datasets where the dimensionality of features presents significant challenges. This research presents a novel approach to feature selection, a critical aspect of enhancing machine learning model efficiency, particularly in the context of large-scale datasets. By integrating the Flamingo Search Algorithm (FSA), Non-dominated Sorting Genetic Algorithm III (NSGA-III), and Regularised Extreme Learning Machine (RELM), the proposed method addresses limitations in existing feature selection and multi-objective optimisation algorithms. Leveraging FSA’s emulation of flamingo behaviours, the approach achieves a balance between global exploration and local exploitation, mitigating issues like premature convergence and local optima. Integration with NSGA-III enhances multi-objective optimisation capabilities, maintaining a delicate equilibrium between convergence and diversity. FSA-RELM is employed for accurate feature assessment, given its rapid learning and suitability for large datasets with multiple labels. Experimental evaluations demonstrate the proposed method’s superiority in feature selection accuracy, classification performance, and computational efficiency compared to existing approaches. This research contributes to advancing feature selection methodologies, offering a comprehensive solution for high-dimensional datasets in machine learning and data mining applications.

Information utilization based modified Differential evolution algorithm (IUMDE) is an improved version of DE algorithm. In IUMDE, Mutation and Selection operation of basic Differential Evolution (DE) algorithm are modified. In proposed IUMDE initially modified mutation operation provides a selection criterion of base vector before performing the basic DE mutation operation. Further the modified selection operation utilizes the neglected trial vector and hence helps to improve the performance of DE algorithm. In this paper IUMDE algorithm is employed on 6 constrained benchmark optimization problems, selected from the literature. The numerical results expose that the IUMDE algorithm perform more accurately and significantly better than other algorithms with these constrained optimization problems.

A differential evolution algorithm with intersect mutation operator

This paper proposes a novel mutation operator named minimum distance mutation for differential evolution (DE) algorithm. We try to improve the local search ability of the algorithm in the mutation operation. During the mutation operation, the selected base particle will be compared with the nearest particle. The better particle will be selected for the mutation operation in this way the neighborhood information can be applied. A set of famous benchmark functions has been used to test and evaluate the performance of the proposed algorithm. The experimental results show that the proposed algorithm has achieved good improvement.

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

Differential evolution with dynamic combination based mutation operator and two-level parameter adaptation strategy

Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly chosen from the three categories, and three parameter values are also randomly selected from the parameter pool. The three groups of mutation operators and parameter values are employed to produce trial vectors. The proposed algorithm makes good use of different mutation operators. Three recently proposed differential evolution variants and three non-differential evolution algorithms are used to make comparisons on the 29 testing functions from CEC. The experimental results have demonstrated that the proposed algorithm is very competitive. The proposed algorithm is utilized to solve three real applications, and the results are superior.

Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This paper presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency. In the proposed algorithm, two phases, namely, the global exploration and the local exploitation, are performed in each generation. In the global phase, a set of trial vectors is produced for each target individual by employing multiple strategies with strong exploration capability. Afterward, an adaptive underestimation model with a self-adapted slope control parameter is proposed to evaluate these trial vectors, the best of which is selected as the candidate. In the local phase, the better-based strategies guided by individuals that are better than the target individual are designed. For each individual accepted in the global phase, multiple trial vectors are generated by using these strategies and filtered by the underestimation value. The cooperation between the global and local phases includes two aspects. First, both of them concentrate on generating better individuals for the next generation. Second, the global phase aims to locate promising regions quickly while the local phase serves as a local search for enhancing convergence. Moreover, a simple mechanism is designed to determine the parameter of DE adaptively in the searching process. Finally, the proposed approach is applied to predict the protein 3D structure. Experimental studies on classical benchmark functions, CEC test sets, and protein structure prediction problem show that the proposed approach is superior to the competitors.

The Differential Evolution (DE) algorithm, under the family of Evolutionary Algorithms (EAs), is one of the powerful algorithms used for solving continuous parameter optimization challenges. The simplistic nature and robustness of the classical DE algorithm have drawn researchers' attention towards its progressive enhancement. This work reports on an investigation of the behavioral changes of the classical DE algorithm, evoked when its mutation and crossover components are fine tuned for enhancement of DE's performance. The scope of this study covers the implementation of a mutation level enhancement and a crossover level enhancement, followed by their integration. The mutation and the crossover components are augmented by incorporation of Centroid DE and Superior-Superior & Superior-Inferior DE logics, respectively. The algorithms appraised in this inquiry were classical DE, Centroid based DE(cDE), Superior-Superior based DE (ssDE), Superior-Inferior DE (siDE), Centroid Superior-Superior DE (cssDE) and Centroid Superior-Inferior DE (csiDE). These algorithms were evaluated by comparison of the values of their mean objective function (MOV), and their speed, at solving the global optimization problems in a simple benchmarking function suite with 4 functions of different categories. The study concludes that the DE algorithm shows enhancement performance with modified mutation and crossover components. However, with a trend for inconsistency for varying values of its control parameters and benchmarking problems.

In recent years, Differential Evolution (DE) algorithm that is one of the most important delegate of the evolutionary algorithms have been successfully used for solving different optimization problems with its both serial and parallel variants. In this study, the performance of DE algorithm powered with cooperative emigrant creation strategy was investigated over the different types of numerical problems and obtained results were compared with the results of the serial DE and parallel DE algorithm supported with conventional emigrant creation strategy. Experimental studies showed that the convergence speed of the parallel DE algorithm is significantly improved with the cooperative emigrant creation strategy and the proposed parallel DE algorithm produces better results compared to the serial DE algorithm and parallel DE algorithm with the conventional emigrant creation strategy.

Differential evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.

Differential evolution (DE) is a well-known evolutionary algorithm which has been successfully applied in many scientific and engineering fields. In most DE algorithms, the base and difference vectors for mutation are randomly selected from the current population. That is, the useful population information cannot be fully exploited to guide the search of DE through mutation. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance. Therefore, to alleviate this drawback and improve the performance of DE, a novel DE algorithm with a directional mutation based on cellular topology is proposed in this study. This proposed algorithm is named as Cellular Direction Information based DE (DE-CDI). In DE-CDI, the cellular topology is employed first to define a neighborhood for each individual of population and then the direction information based on the neighborhood is incorporated into the mutation operator. In this way, DE-CDI not only utilizes the neighborhood information to exploit the regions of better individuals and accelerate convergence, but also introduces the direction information to guide the search to the promising area. To evaluate the performance of the proposed method, DE-CDI is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of DE-CDI for most DE algorithms studied.

Until now, numerous mutation strategies have been introduced as search operators within the differential evolution (DE) algorithm. These operators are designed mainly to improve fitness value while also maintaining diversity in the population, but they do not directly act to reduce constraint violations of constrained problems. Interestingly, the so-called constraint handling techniques, used with most evolutionary algorithms, are not a part of the actual search process. Instead, the constraint violations are only considered in the ranking and selection of individuals for participation in the search process. This paper introduces a new DE mutation operator that incorporates a mechanism, based on constraint consensus, that can directly help to reduce the constraint violations during the evolutionary search process. The proposed DE algorithm has been tested on a set of well-known constrained benchmark problems. The experimental results show that the proposed algorithm is able to obtain better solutions, compared to the standard DE algorithm, with significantly reduced computational effort. The algorithm also outperforms state-of-the-art algorithms.

Application of a Derivative-Free Method with Projection Skill to Solve an Optimization Problem

A differential evolution-based algorithm with maturity extension for feature selection in intrusion detection system