Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

Aljarah, Ibrahim; Al-Zoubi, Ala’ M.; Faris, Hossam; Hassonah, Mohammad A.; Mirjalili, Seyedali; Saadeh, Heba

doi:10.1007/s12559-017-9542-9

Cited by 213 publications

(83 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Computational Resultsmentioning

confidence: 99%

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

Benmessahel

Xie

Chellal

2020

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…ese characteristics make GOA get some good applications; paper [42] presents a parameter adaptive VMD method based on GOA to analyze vibration signals of rotating machinery and proves that this method is effective for fault diagnosis of mechanical vibration signals. In [43], a hybrid method based on GOA is proposed to optimize the parameters of support vector machine (SVM) model and find the best feature subset, which is verified that this method is superior to other methods in classification accuracy, while minimizing the number of selected features. GOA has been applied in many fields since it was put forward, but it has not involved electromagnetic field and antenna optimization so far.…”

Section: Introductionmentioning

confidence: 91%

Optimal Pattern Synthesis of Linear Array and Broadband Design of Whip Antenna Using Grasshopper Optimization Algorithm

Wang

Liu

et al. 2020

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

Antenna arrays with high directivity, low side-lobe level, and null control in desired direction and whip antenna with wider bandwidth both need to be optimized to meet different needs of communication systems. A new natural heuristic algorithm simulating social behavior of grasshoppers, grasshopper optimization algorithm (GOA), is applied to electromagnetic field as a new effective technology to solve the antenna optimization problem for the first time. Its algorithm is simple and has no gradient mechanism, can effectively avoid falling into local optimum, and is suitable for single-objective and multiobjective optimization problems. GOA is used to optimize the side lobe suppression, null depth, and notch control of arbitrary linear array and then used to optimize the loading and matching network of 10-meter HF broadband whip antenna compared with other algorithms. The results show that GOA has more advantages in side-lobe suppression, null depth, and notch control of linear array than other algorithms and has better broadband optimization performance for HF whip antenna. The pattern synthesis and antenna broadband optimization based on GOA provide a new and effective method for antenna performance optimization.

show abstract

“…Aljarah et al [17] explores the grasshopper optimization algorithm. A bio-inspired optimization technique is introduced to optimize the performance of Support Vector Machine (SVM) classifier, a powerful supervised machine learning technique.…”

Section: A Feature Selection In Supervised Machine Learningmentioning

confidence: 99%

Feature Selection for Learning-to-Rank using Simulated Annealing

Allvi¹,

Hasan²,

Rayan³

et al. 2020

IJACSA

View full text Add to dashboard Cite

Machine learning is being applied to almost all corners of our society today. The inherent power of large amount of empirical data coupled with smart statistical techniques makes it a perfect choice for almost all prediction tasks of human life. Information retrieval is a discipline that deals with fetching useful information from a large number of documents. Given that today millions, even billions, of digital documents are available, it is no surprise that machine learning can be tailored to this task. The task of learning-to-rank has thus emerged as a wellstudied domain where the system retrieves the relevant documents from a document corpus with respect to a given query. To be successful in this retrieving task, machine learning models need a highly useful set of features. To this end, meta-heuristic optimization algorithms may be utilized. The aim of this work is to investigate the applicability of a notable meta-heuristic algorithm called simulated annealing to select an effective subset of features from the feature pool. To be precise, we apply simulated annealing algorithm on the well-known learning-torank datasets to methodically select the best subset of features. Our empirical results show that the proposed framework achieve gain in accuracy while using a smaller subset of features, thereby reducing training time and increasing effectiveness of learningto-rank algorithms.

show abstract

Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

Cited by 213 publications

References 51 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

Optimal Pattern Synthesis of Linear Array and Broadband Design of Whip Antenna Using Grasshopper Optimization Algorithm

Feature Selection for Learning-to-Rank using Simulated Annealing

Contact Info

Product

Resources

About