A novel multi-objective wrapper-based feature selection method using quantum-inspired and swarm intelligence techniques

Zouache, Djaafar; Got, Adel; Alarabiat, Deemah; Abualigah, Laith; Talbi, El‐Ghazali

doi:10.1007/s11042-023-16411-9

Cited by 5 publications

(6 citation statements)

References 48 publications

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“…Step 2. Roulette fitness-distance balancing strategy: during the exploration phase, FDB scores are calculated using Equation (7) and the roulette strategy is used to select an agent to replace the original random agent.…”

Section: Implementation Of Imscsomentioning

confidence: 99%

“…Such algorithms do not depend on the specific form of the problem but rather guide the search process by simulating phenomena in nature, behaviors of organisms, physical principles, and social laws, etc., thus demonstrating excellent adaptability and efficiency in numerous application fields. With the continuous development of metaheuristic algorithms, these algorithms play a crucial role in a variety of fields, such as path planning [2,3], image segmentation [4,5], feature selection [6,7], neural network hyperparameter optimization [8,9], task allocation [10,11], supply chain management [12,13], waste collection [14], wireless sensor optimization problems [15,16], and antenna array synthesis issues [17,18]. And, they show great potential in promoting the development of engineering technology, improving productivity, and solving multi-objective optimization problems [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

Zhang,

He,

Wang

et al. 2024

Biomimetics

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The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm’s efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm’s escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm’s exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance.

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Section: Implementation Of Imscsomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

Zhang,

He,

Wang

et al. 2024

Biomimetics

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show abstract

“…The behavior of crayfish enters different stages with change in temperature. The temperature is calculated as shown in Equation (2). In COA, when the temperature is higher than 30 • C, crayfish will choose heat avoidance behavior.…”

Section: Initialization Phasementioning

confidence: 99%

“…These algorithms have proven their reliability and effectiveness in discovering optimal solutions to complex real-world problems. In addition to classical numerical optimization, metaheuristic algorithms have demonstrated their efficacy in a wide range of optimization tasks, including, but not limited to, feature selection [ 1 , 2 ], the traveling salesman problem [ 3 , 4 ], image segmentation problems [ 5 , 6 , 7 ], wireless sensor coverage problems [ 8 , 9 ], parameter estimation for solar photovoltaic models [ 10 , 11 ], and path planning [ 12 , 13 ]. In the past decades, new metaheuristic algorithms have been continuously proposed that are based on evolutionary theory, physical laws, biological population behavior, and human behavior.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

Wang,

Zhang,

Zou

2024

Biomimetics

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The crayfish optimization algorithm (COA), proposed in 2023, is a metaheuristic optimization algorithm that is based on crayfish’s summer escape behavior, competitive behavior, and foraging behavior. COA has a good optimization performance, but it still suffers from the problems of slow convergence speed and sensitivity to the local optimum. To solve these problems, an improved multi-strategy crayfish optimization algorithm for solving numerical optimization problems, called IMCOA, is proposed to address the shortcomings of the original crayfish optimization algorithm for each behavioral strategy. Aiming at the imbalance between local exploitation and global exploration in the summer heat avoidance and competition phases, this paper proposes a cave candidacy strategy and a fitness–distance balanced competition strategy, respectively, so that these two behaviors can better coordinate the global and local optimization capabilities and escape from falling into the local optimum prematurely. The directly foraging formula is modified during the foraging phase. The food covariance learning strategy is utilized to enhance the population diversity and improve the convergence accuracy and convergence speed. Finally, the introduction of an optimal non-monopoly search strategy to perturb the optimal solution for updates improves the algorithm’s ability to obtain a global best solution. We evaluated the effectiveness of IMCOA using the CEC2017 and CEC2022 test suites and compared it with eight algorithms. Experiments were conducted using different dimensions of CEC2017 and CEC2022 by performing numerical analyses, convergence analyses, stability analyses, Wilcoxon rank–sum tests and Friedman tests. Experiments on the CEC2017 and CEC2022 test suites show that IMCOA can strike a good balance between exploration and exploitation and outperforms the traditional COA and other optimization algorithms in terms of its convergence speed, optimization accuracy, and ability to avoid premature convergence. Statistical analysis shows that there is a significant difference between the performance of the IMCOA algorithm and other algorithms. Additionally, three engineering design optimization problems confirm the practicality of IMCOA and its potential to solve real-world problems.

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“…They evaluate individual features based on statistical measures of relevance, such as correlation or mutual information, independent of any machine learning algorithm. This makes them distinct from wrapper methods, which evaluate subsets of features based on the performance of a specific machine learning model [9], and embedded methods, which perform feature selection as part of the model training process.…”

Section: Introductionmentioning

confidence: 99%

PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization

Bouchene

2024

Preprint

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Univariate Feature Selection (UFS) traditionally involves a labor-intensive process of trial-and-error, necessitating the selection of scoring functions and the determination of feature numbers. These choices can inadvertently affect both the performance and interpretability of the model. To address this challenge, we introduce Particle Swarm Optimization for Univariate Feature Selection (PSO-UFS), an innovative method that automates these crucial decisions. PSO-UFS leverages the power of Particle Swarm Optimization (PSO) to autonomously identify the optimal scoring function and feature subset that maximize a machine learning algorithm's performance metric. Our empirical evaluations across multiple datasets demonstrate that PSO-UFS significantly outperforms traditional UFS in various performance metrics, including accuracy, precision, recall, and F1-score. Importantly, PSO-UFS generates more interpretable feature subsets, thereby enhancing the model's comprehensibility. This advancement paves the way for broader applications in real-world scenarios where feature reduction and interpretability are paramount.

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A novel multi-objective wrapper-based feature selection method using quantum-inspired and swarm intelligence techniques

Cited by 5 publications

References 48 publications

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

Improved Multi-Strategy Sand Cat Swarm Optimization for Solving Global Optimization

An Improved Multi-Strategy Crayfish Optimization Algorithm for Solving Numerical Optimization Problems

PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization

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