Feature selection is a process used during machine learning and data analysis, aimed at selecting the best features to increase model efficiency, decrease complexity, and increase readability. Metaheuristic algorithms are suited to provide solutions to feature selection problems because these problems are combinatorial and require an effective and efficient search through large solution spaces. Over the last decade (2014–2024), numerous approaches have been explored, each with its own optimization strengths and constraints. Swarm intelligence and evolutionary algorithms—including genetic algorithms, particle swarm optimization, and the zebra optimization algorithm—have operated effectively in this area. This study seeks to discuss the metaheuristic algorithms for feature selection and responds to questions in the process. In this study, a case study is provided using datasets from the University of California, Irvine repository, where various metaheuristic algorithms are applied to identify optimal feature subsets.
The vehicle routing problem with time windows is an NP-hard optimization problem vital to logistics and supply chain management. It involves optimizing vehicle routes to serve customers within time windows and capacity limits. This study proposes a hybrid genetic algorithm combining a nearest neighbor-based initialization with advanced mutation operators. The nearest neighbor method ensures high-quality initial solutions by prioritizing proximity and constraints, while multiple mutation operators enhance exploration and exploitation. Tested on the Solomon 100-customer dataset, NN-IHGA outperformed benchmarks, especially on random and mixed datasets, reducing travel costs and vehicle counts. Results highlight NN-IHGA's robustness and adaptability, offering a practical solution for real-world logistics optimization.
The vehicle routing problem with time windows is an NP-hard optimization problem vital to logistics and supply chain management. It involves optimizing vehicle routes to serve customers within time windows and capacity limits. This study proposes a hybrid genetic algorithm combining a nearest neighbor-based initialization with advanced mutation operators. The nearest neighbor method ensures high-quality initial solutions by prioritizing proximity and constraints, while multiple mutation operators enhance exploration and exploitation. Tested on the Solomon 100-customer dataset, NN-IHGA outperformed benchmarks, especially on random and mixed datasets, reducing travel costs and vehicle counts. Results highlight NN-IHGA’s robustness and adaptability, offering a practical solution for real-world logistics optimization.
Algorithms are fundamental to solving complex problems in science and engineering. However, conventional methods often struggle with nonlinear, high-dimensional landscapes. The biologically inspired dung beetle optimization algorithm has offered promising solutions but is still prone to premature convergence and falling into local optima. This article presents a multi-strategy enhanced dung beetle optimization algorithm that integrated tent chaotic mapping for population initialization, a golden sine strategy for position updating, Lévy flights to escape local minima, and dynamic weighting coefficients for adaptive search balancing. These strategies collectively enhanced population diversity and improved the balance between exploration and exploitation. Benchmarking on the CEC2017 test suite and real-world engineering problems demonstrated that the multi-strategy enhanced dung beetle optimization algorithm achieved superior convergence speed, solution accuracy, and robustness when compared with the standard dung beetle optimization algorithm and other state-of-the-art metaheuristics.
This study examines the performance of genetic algorithms, the particle swarm optimization (PSO) algorithm, and the artificial bee colony (ABC) algorithm in procedural content generation for game map layouts. A series of experiments evaluated each algorithm's efficiency based on convergence speed, content quality, and overall map structure. The results showed that the genetic algorithms with tournament selection outperformed the PSO and the ABC algorithms in generating high-quality maps, though the PSO and the ABC algorithms demonstrated competitive performance in specific scenarios. This research highlights the importance of task-specific optimization, suggesting that hybrid approaches could improve game content generation by combining the strengths of different algorithms.
