A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets

Abstract

The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected.

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems

“…The k-Nearest Neighbor (kNN) is used as the classifier to evaluate the selected feature subsets’ goodness. This classifier was selected due to its popular use in the FS domain and for its suitability in dealing with large dataset dimensions yielding higher classification accuracy than other classifiers [ 16 , 65 ]. This proposed method was assessed using eighteen (18) high-dimensional datasets from the Arizona State University (ASU) feature selection repository.…”

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

“…These inspirational sources can be categorized into swarm-based, evolutionary-based, human, and physics-based, as in [ 16 ], and some further categorized them as system-based and bio-based, as can be found in [ 13 ]. The swarm-based category, also known as Swarm Intelligent (SI), derives their inspirations from the behavior or social interaction of animals, fish, birds and so on.…”

“…The method produced higher validation accuracy on 15 of the 18 datasets utilized. Afterward, the BDMO was hybridized with a local search Algorithm Simulated Annealing (SA) known as BDMSAO [ 13 ] to tackle feature selection challenges of varying dimensional problems. The proposed hybrid method yielded the highest classification accuracy obtainable on 50% of the 18 datasets employed for validation.…”

“…The DMO was hybridized with a Multi-Hop Routing Scheme (DMOSC-MHRS) and applied to solve the clustering problem [12]. The binary variant of DMO, the BDMO, was hybridized with a local search algorithm simulated annealing to tackle feature selection challenges of varying dimensional Predator-Prey Optimization [69] problems (BDMSAO) [13]. Moreover, in [14], the study proposed a Binary Dwarf Mongoose Optimizer (BDMO) and was applied to solve the multiclass high-dimensional feature selection problem.…”

“…The success history-based adaptive differential evolution (SHADE) was hybridized with a modified Whale Optimization Algorithm (WOA) such that the two algorithms work stand-alone and only share information about the best-found solution [15]. These inspirational sources can be categorized into swarm-based, evolutionary-based, human, and physicsbased, as in [16], and some further categorized them as system-based and bio-based, as can be found in [13]. The swarm-based category, also known as Swarm Intelligent (SI), derives their inspirations from the behavior or social interaction of animals, fish, birds and so on.…”