Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

Abstract

In modulation identification issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature characteristics. Feature weighting boosts the performance of machine learning algorithms, particularly the class of instance-based learning algorithms such as the Minimum Distance (MD) classifier, in which the distance measure is highly sensitive to the magnitude of features. In this paper, we propose an improved version of the Salp Swarm optimization Algorithm (SSA), called ISSA, that will be applied to optimize feature weights for an MD classifier. The aim is to improve the performance of a blind digital modulation detection approach in the context of multiple-antenna systems. The improvements introduced to SSA mainly rely on the opposition-based learning technique. Computer simulations show that the ISSA outperforms the SSA as well as the algorithms that derive from it. The ISSA also exhibits the best performance once it is applied for feature weighting in the above context.

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection

“…SSA has also been used to address the feature selection problem. Some of the efficiently improved cases of the basic SSA include the solution of feature weighting with the minimum distance problem [41], the problem of feature selection solving through hybridization with the opposition based learning heuristics [42], and the improvement of accuracy, reliability and the convergence time for the problem of feature selection with the introduction of the inertia weight control parameter [43]. SSA has also been successfully modified and applied in other application domains recently, such as green home health care routing problem [44], health care supply chain [45], crop disease detection [46] and power systems unit commitment task [47], to name the few.…”

“…Finally, to show the performances of the proposed bSSARM-SCA algorithm and compare it to other SOTA SSA versions, the authors have implemented binary versions of three novel SSA modifications. The accuracy of the bSSARM-SCA over 21 datasets was compared to opposition based learning and inertia weight ISSA (bISSA1), proposed by [41], opposition based learning and local search ISSA (bISSA2) proposed in [42], and inertia weight ISSA (bISSA3) given in [43]. Again, it is worth noting that the authors have independently implemented all three mentioned binary ISSA variants and executed the experiments with 21 observed datasets.…”

“…The simulation findings clearly show the superiority of the proposed bSSARM-SCA method, that obtained the best results on 15 out of 21 observed datasets. The second best method was bISSA2 [42], which obtained the best results on four datasets, while the bISSA1 method [41] achieved the best accuracy on two datasets.…”

Aplicación del algoritmo de optimización por enjambre de salpas para la estimación de parámetros en transformadores monofásicos empleando medidas de tensión y corriente

“…Para el movimiento de las salpas, la población se divide en dos componentes, los cuales son movimiento en relación al líder y movimientos entre seguidores. El líder es la salpa que obtuvo la mejor solución en la iteración actual y es aquella que se ubica al principio de la cadena de salpas; esta es responsable de guiar el movimiento de las demás salpas de la población en búsqueda de la fuente de alimento [29]. La actualización del líder de las salpas se da en cada iteración dependiendo del movimiento realizado y el resultado de la función objetivo al evaluar cada uno de los individuos de la población.…”

A promising approach using Fibonacci sequence-based optimization algorithms and advanced computing

“…The proposed method, namely FSPSOSSA or HSSAPSO (Hybrid SSAPSO), is employed to deal with inverse problems of a real large-scale truss bridge. To compare with FSPSOSSA, other algorithms, namely PSO, Genetic Algorithm (GA), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), SSA, Biogeography-based Optimization (BBO) 22 , Moth-Flame Optimization (MFO) 23 , other improved SSA (ISSA) 24 are employed.…”