A dynamic stochastic search algorithm for high-dimensional optimization problems and its application to feature selection

Liu, Qi; Liu, Mengxue; Wang, Fengde; Xiao, Wensheng

doi:10.1016/j.knosys.2022.108517

Cited by 10 publications

(7 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the complexity can be mathematically represented as O ( C QL + C QLESCA − SCNN + C SVM ), where O denotes the worst-case time complexity, and C QL , C QLESCA-SCNN , and C SVM indicate the complexity of Q-Learning implementation while modifying the location of each QLESCA search agent, the QLESCA-SCNN feature selection method, and the execution time of the SVM classifier in the training phase, respectively. Determining the computational complexity of many metaheuristic algorithms typically involves the analyses of three components [ 40 ]:…”

Section: Proposed Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

2023

View full text Add to dashboard Cite

Section: Proposed Frameworkmentioning

confidence: 99%

“… [ 39 ] group teaching optimization algorithm 2022 14. [ 40 ] dynamic stochastic search algorithm 2022 15. [ 41 ] Dynamic Butterfly algorithm 2022 16.…”

Section: Introductionmentioning

confidence: 99%

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

2023

View full text Add to dashboard Cite

“…The suggested model performance assessed using 12 datasets from the UCI repository. The other work, the authors (Liu et al, 2022) suggested a dynamic stochastic search (DSS) for FS problems. It is exclusively designed for high dimensional optimization problems.…”

Section: Related Workmentioning

confidence: 99%

S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

2022

View full text Add to dashboard Cite

The African vulture optimization algorithm (AVOA) is a recently developed metaheuristic algorithm that imitates the eating and movement patterns of authentic African vultures. AVOA is developed to address the continuous optimization problem. However, AVOA is unable to solve the discrete search space, this inspires us to develop the binary AVOA for feature selection problems in classification tasks. The suggested BAVOA incorporates an eight-transfer function (S-shaped and V-shaped) for transforming a continuous variable to a binary one. Using 14 benchmark data sets, the proposed technique is compared against 15 conventional binary metaheuristics algorithms in terms of classification accuracy, fitness function, number of selected features and converging ability. Furthermore, the results are statistically analysed using Wilcoxon test. The comparative findings of S-Shaped and V-shaped transfer functions indicate the superior performance of BAVOA methods, particularly S2-BAVOA, in contrast to other transfer function. Based on results, it turns out that the suggested technique converges to the global minimum in several iterations based on the selection of optimal characteristics, fitness values and higher classification accuracy as compared to the classical binary metaheuristic algorithms.

show abstract

“…They are effective in resolving high-dimensional, NP-hard, non-differentiable, non-convex optimization issues [9]. Among the benefits that have contributed to the success of metaheuristic algorithms are their effectiveness in solving unknown search spaces, nonlinear, discrete, and the simplicity of their principles, and their independence, their ease of implementation from the nature of the issue [10].…”

Section: Introductionmentioning

confidence: 99%

“…As explained in [14,15], the genetic algorithm and the evaluation strategy [16], evolutionary algorithms have been built by modelling biological evolutionary traits such as crossovers, mutations, and selections. Physics-based algorithms are motivated by physical laws, such as the equilibrium algorithm (EA) [10,13] and the Henry gas solubility algorithm [12]. Swarm intelligence algorithms, such as the whale optimization algorithm (WOA) [17], jellyfish search optimization (JFSO) [18], heap-based technique (HT) [19], grasshopper optimization (GO) [20], particle swarm optimization [21], manta rays foraging optimization (MRFO) [22], artificial bee colony [23], and marine predators algorithm (MPA) [24] are a series of algorithms influenced by swarming and animal group behavior.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems

et al. 2023

View full text Add to dashboard Cite

This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA’s core concept is inspired by the dwarf mongoose’s foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy.

show abstract

A dynamic stochastic search algorithm for high-dimensional optimization problems and its application to feature selection

Cited by 10 publications

References 96 publications

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study

S‐shaped and V‐shaped binary African vulture optimization algorithm for feature selection

An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems

Contact Info

Product

Resources

About