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Feature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction
(2025-01-01)
Teerasarn, Chalanwich
;
Kimpan, Warangkhana
This research focuses on developing an efficient feature selection process using a hybrid technique combining Cuckoo Search algorithm and Firefly Algorithm. The Wisconsin Diagnostic Breast Cancer dataset is utilized to evaluate the capability of selecting significant features and eliminating irrelevant ones. The experimental results demonstrate that using the hybrid technique significantly improves the accuracy of machine learning compared to using the Cuckoo Search and Firefly Algorithm individually. Additionally, an analysis was conducted on the impact of splitting the dataset for training and testing, with splits of 70/30, 80/20, and 90/10. The experiments revealed a relationship between the number of selected features and the model accuracy. The findings from this study can serve as a guideline for developing appropriate feature selection process for complex data analysis problems.
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Primate swarm algorithm for continuous optimization problems
(2017-08-29)
Ritthipakdee, Amarita
;
Thammano, Arit
In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the primate. The communication among them is studied and is also a key part in their food finding strategy. Our proposed algorithm developed upon the behavior is tested with twelve standard benchmark functions and most of which converged to the optimal value.
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A modified particle swarm optimization with dynamic mutation period
(2014-01-01)
Ratanavilisagul, Chiabwoot
;
Kruatrachue, Boontee
The particle swarm optimization (PSO) is an algorithm that attempts to search for better solution in the solution space by attracting particles to converge toward a particle with the best fitness. PSO is typically troubled with the problems of trapping in local optimum and premature convergence. In order to overcome both problems, we propose an improved PSO algorithm that is applied mutation operator dynamically when particles are in local optimum. Moreover, the mutation period can be adjusted to solve the problem appropriately. The proposed technique is tested on benchmark functions and gives more satisfied search results in comparison with PSOs for the benchmark functions. © 2014 IEEE.
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A new selection operator to improve the performance of genetic algorithm for optimization problems
(2013-11-25)
Ritthipakdee, Amarita
;
Thammano, Arit
;
Premasathian, Nol
;
Uyyanonvara, Bunyarit
Nature-inspired algorithms, such as Particle swarm optimization (PSO), Ant colony optimization (ACO), and Firefly algorithm, are well known for solving NP-hard optimization problems. They are capable of obtaining optimal solutions in a reasonable time. The algorithm presented in this paper is a combination of a firefly mating concept and genetic algorithm. Genetic algorithm is used as the core of the algorithm while a firefly mating concept is used to compose a new selection operator. The proposed algorithm is tested on four standard benchmark functions. Experimental results have confirmed that the proposed algorithm is not only computationally more efficient than both the original firefly algorithm and the genetic algorithm but also almost always ensure the optimal solutions. © 2013 IEEE.

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