A dynamic locality multi-objective salp swarm algorithm for feature selection
•A novel multi-objective SSA algorithm is proposed.•Two essential components; dynamic time-varying strategy and the local fittest solutions are integrated with SSA.•The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets.•The MODSSA-lbest achieved significant promising results versus...
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| Published in: | Computers & industrial engineering Vol. 147; p. 106628 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.09.2020
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| ISSN: | 0360-8352, 1879-0550 |
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| Abstract | •A novel multi-objective SSA algorithm is proposed.•Two essential components; dynamic time-varying strategy and the local fittest solutions are integrated with SSA.•The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets.•The MODSSA-lbest achieved significant promising results versus its counterpart algorithms.
Developing intelligent analytical tools requires pre-processing data and finding relevant features that best reinforce the performance of the predictive algorithms. Feature selection plays a significant role in maximizing the accuracy of machine learning algorithms since the presence of redundant and irrelevant attributes deteriorates the performance of the learning process and increases its complexity. Feature selection is a combinatorial optimization problem that can be formulated as a multi-objective optimization problem with the purpose of maximizing the classification performance and minimizing the number of irrelevant features. It is considered an NP hard optimization problem since having a number of (n) features produces a large search space of size (2n) of different permutations of features. An eminent type of optimizer for tackling such an exhausting search process is evolutionary, which mimic evolutionary processes in nature to solve problems in computers. Salp Swarm Algorithm (SSA) is a well-established metaheuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating global optima for optimization problems. The objective of this article is to promote and boost the performance of the multi-objective SSA for feature selection. Therefore, it proposes an enhanced multi-objective SSA algorithm (MODSSA-lbest) that adopts two essential components: the dynamic time-varying strategy and local fittest solutions. These components assist the SSA algorithm in balancing exploration and exploitation. Thus, it converges faster while avoiding locally optimal solutions. The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets and compared with the well-regarded Multi-Objective Evolutionary Algorithms (MOEAs). The results show that the MODSSA-lbest achieves significantly promising results versus its counterpart algorithms. |
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| AbstractList | •A novel multi-objective SSA algorithm is proposed.•Two essential components; dynamic time-varying strategy and the local fittest solutions are integrated with SSA.•The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets.•The MODSSA-lbest achieved significant promising results versus its counterpart algorithms.
Developing intelligent analytical tools requires pre-processing data and finding relevant features that best reinforce the performance of the predictive algorithms. Feature selection plays a significant role in maximizing the accuracy of machine learning algorithms since the presence of redundant and irrelevant attributes deteriorates the performance of the learning process and increases its complexity. Feature selection is a combinatorial optimization problem that can be formulated as a multi-objective optimization problem with the purpose of maximizing the classification performance and minimizing the number of irrelevant features. It is considered an NP hard optimization problem since having a number of (n) features produces a large search space of size (2n) of different permutations of features. An eminent type of optimizer for tackling such an exhausting search process is evolutionary, which mimic evolutionary processes in nature to solve problems in computers. Salp Swarm Algorithm (SSA) is a well-established metaheuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating global optima for optimization problems. The objective of this article is to promote and boost the performance of the multi-objective SSA for feature selection. Therefore, it proposes an enhanced multi-objective SSA algorithm (MODSSA-lbest) that adopts two essential components: the dynamic time-varying strategy and local fittest solutions. These components assist the SSA algorithm in balancing exploration and exploitation. Thus, it converges faster while avoiding locally optimal solutions. The proposed approach (MODSSA-lbest) is tested on 13 benchmark datasets and compared with the well-regarded Multi-Objective Evolutionary Algorithms (MOEAs). The results show that the MODSSA-lbest achieves significantly promising results versus its counterpart algorithms. |
| ArticleNumber | 106628 |
| Author | Heidari, Ali Asghar Mafarja, Majdi Aljarah, Ibrahim Habib, Maria Al-Madi, Nailah Faris, Hossam Elaziz, Mohamed Abd Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Ibrahim orcidid: 0000-0002-9265-9819 surname: Aljarah fullname: Aljarah, Ibrahim email: i.aljarah@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 2 givenname: Maria surname: Habib fullname: Habib, Maria organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 3 givenname: Hossam orcidid: 0000-0003-4261-8127 surname: Faris fullname: Faris, Hossam email: hossam.faris@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 4 givenname: Nailah surname: Al-Madi fullname: Al-Madi, Nailah email: n.madi@psut.edu.jo organization: Department of Computer Science, Princess Sumaya University for Technology, Amman, Jordan – sequence: 5 givenname: Ali Asghar surname: Heidari fullname: Heidari, Ali Asghar email: as_heidari@ut.ac.ir, aliasghar68@gmail.com, aliasgha@comp.nus.edu.sg, t0917038@u.nus.edu organization: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran – sequence: 6 givenname: Majdi surname: Mafarja fullname: Mafarja, Majdi email: mmafarja@birzeit.edu organization: Department of Computer Science, Birzeit University, Birzeit, Palestine – sequence: 7 givenname: Mohamed Abd orcidid: 0000-0002-7682-6269 surname: Elaziz fullname: Elaziz, Mohamed Abd email: meahmed@zu.edu.eg organization: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt – sequence: 8 givenname: Seyedali orcidid: 0000-0002-1443-9458 surname: Mirjalili fullname: Mirjalili, Seyedali email: ali.mirjalili@gmail.com organization: Centre for Artificial Intelligence Research and Optimisation Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia |
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