An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems

Faris, Hossam; Mafarja, Majdi; Heidari, Ali Asghar; Aljarah, Ibrahim; Al-Zoubi, Ala’ M.; Mirjalili, Seyedali; Fujita, Hamido

doi:10.1016/j.knosys.2018.05.009

Cited by 563 publications

(222 citation statements)

References 69 publications

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“…Then, the goal of the second set of experiments is to inspect our MOCMVO algorithm by using a wide range of multiobjective testing problems. The results of CMVO algorithm are compared with those of MVO that is the original version of CMVO, PSO, GA, grasshopper optimization algorithm (GOA), locust search algorithm LSO, Salp swarm algorithm (SSA), and whale optimization algorithm (WOA), we compared them to determine the performance of the proposed algorithm. The results of MOCMVO algorithm are compared with those of MOPSO, MOMVO, MOALO, and MOGWO algorithms for demonstrating the efficiency of our proposed method.…”

Section: Computational Resultsmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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Section: Computational Resultsmentioning

confidence: 99%

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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“…This is performed by using the V-shaped transfer function that is applied on the step vector value of each search agent. Binary Salp Swarm Algorithm (SSA) [32] is a recent binary metaheuristic algorithm, that uses S-shaped and Vshaped transfer functions to modify the algorithm in order to solve feature selection problems. Lately, Whale Optimization Algorithm [33] has been employed as a feature selection algorithm for disease detection [34].…”

Section: Related Workmentioning

confidence: 99%

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

Taghian¹,

Nadimi-Shahraki²

2019

ACIJ

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A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.

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“…In the machine learn ing context, the FS method reduces the dimensio nality of data without losing any in formation. This technique is used in the processing phase of the methodology to try to select the best subset of features and remove unneeded or irrelevant features (Faris, A la'M, & Aljarah, 2017;Faris et al, 2018), th us improving the results. Next, as a second step, we analy zed the data with the highest results using an auto feature selection method to select the best feature subset.…”

Section: Feature Selection (Fs)mentioning

confidence: 99%

Explaining Individuals’ Usage of Social Commerce: A Data Mining Approach

Alshamaileh¹,

Aljarah²,

Al-Zoubi³

2018

MAS

Self Cite

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With the use of Web 2.0 technology, e-commerce is undergoing a radical change that enriches consumer involvement and enables a better understanding of economic value. This emerging phenomenon is known as social commerce. Social commerce (s-commerce) presents a new alternative for consumers to search for and find information about products they are seeking to buy. In spite of its universality, the adoption of this burgeoning technology is affected by several factors. This research project is an initial attempt to explore individuals’ intention of s-commerce usage through the data mining approach. The data was collected via a web-based questionnaire survey of 360 social network site (SNS) users in Jordan. Data mining techniques were then used to analyze the collected data in order to figure out what group of features is best for predicting s-commerce adoption among SNS users. The results showed that data characteristics related to gender, monthly income, civil status, number of connections, and prior online shopping experience are key factors in the classification process. The findings may assist researchers in investigating social commerce issues and aid practitioners in developing new s-commerce strategies.

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An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems

Abstract: Two wrapper feature selection approaches using salp swarm algorithm are proposed.

Cited by 563 publications

References 69 publications

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Binary Sine Cosine Algorithms for Feature Selection from Medical Data

Explaining Individuals’ Usage of Social Commerce: A Data Mining Approach

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