# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 1 Introduction
With two additional operators, the authors effectively dealt with the commensalism and mutualism stages as binary attributes on the foundations of a chaotic function. The proposed system could also utilize S-shaped and V-shaped transfer functions. [PERSON] et al. [34] proposed a binary SOS with an adaptive S-shaped transfer function for solving feature selection problems with high accuracy. All of the above-mentioned algorithms excel in solving certain problems in particular and have their own pros and cons in a given scenario. This paper proposes the application of a newly developed optimization technique called the weighted superposition attraction (WSA) algorithm for efficiently solving feature selection problems. It is a new-generation swarm-intelligence-based metaheuristic algorithm that can deal with continuous optimization challenges, with its particles generating a superposition leading to solution vectors [35]. This algorithm derives its inspiration from the principles of particulate superposition in physics, and the unique proposition has been observed to be quite effective in testing real-valued and dynamic optimization problems. [PERSON] and [PERSON] [36] also proved WSA to be superior to other metaheuristic algorithms since it would not require any S or V-shaped transfer function to convert values to binary, and it also adopts a step sizing function for population diversity. [PERSON] and [PERSON] [36] further developed WSA to solve combinatorial problems by integrating a random walk procedure and opposition-based learning into WSA, creating cWSA. The cWSA has a much more diverse form, with enhanced performance and capabilities over the regular algorithm. The WSA can also be enhanced to solve dynamic binary optimization problems, having the unique capability that is missing in most of the metaheuristic algorithms, since the research focus has heavily been based on real values. [PERSON] and [PERSON] [37] achieved this capability by using the binary properties of WSA, known as bWSA, with excellent results. The WSA has proven itself in a variety of different application problems. [PERSON] et al. [38] observed it to be particularly useful in field pattern recognition while improving fuzzy c-means clustering by integrating a quantum-enhanced version of WSA. The original fuzzy c-means clustering algorithm had been unable to escape the local optima at times, and WSA had overcome the challenge easily. It can also be extended to macro-level structures, with [PERSON] and [PERSON] [39] performing 2D and 3D truss design optimizations using WSA. The adopted algorithm could deal with five truss design problems with 200 elements and generate a sheet for optimal weight while including statistical measures, such as standard deviations and analysis counts in comparison to other considered algorithms. This paper extends those studies by applying WSA to feature selection problems as the pinnacle of future advancements in the field. To the best of the authors' knowledge, no work on feature selection using WSA has been carried out so far. In this paper, the performance of WSA is compared with that of seven other metaheuristics, i.e., DE, GA, PSO, FPA, SOS, MPA and MRFO. Traditional metaheuristics, such as DE, GA and PSO,have already been applied to solve almost all classes of optimization problems. Thus, the comparison of WSA against them would provide a useful benchmark for traditional metaheuristics. On the other hand, FPA, SOS, MPA and MRFO are powerful metaheuristics that have been developed within the last five years. Comparison with these algorithms would therefore establish WSA's standing among its recent peers. Thus, the results derived from this paper would provide valuable insights into the effectiveness of WSA for feature selection and its potential for future applications in the field. A variety of datasets have been considered to evaluate the performance of the algorithms under the consideration of classification accuracy, fitness, average features selected, convergence and computational time.