In this paper, a distinction is drawn between research which assesses the suitability of the Hopfield network for solving the travelling salesman problem (TSP) and research which attempts to determine the effectiveness of the Hopfield network as an optimization technique. It is argued that the TSP is generally misused as a benchmark for the latter goal, with the existence of an alternative linear formulation giving rise to unreasonable comparisons.
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Sensors (Basel)
January 2025
Department of Product & Systems Design Engineering, University of the Aegean, 84100 Syros, Greece.
This paper addresses the complex problem of multi-goal robot navigation, framed as an NP-hard traveling salesman problem (TSP), in environments with both static and dynamic obstacles. The proposed approach integrates a novel path planning algorithm based on the Bump-Surface concept to optimize the shortest collision-free path among static obstacles, while a Genetic Algorithm (GA) is employed to determine the optimal sequence of goal points. To manage static or dynamic obstacles, two fuzzy controllers are developed: one for real-time path tracking and another for dynamic obstacle avoidance.
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January 2025
Faculty of Medicine, Department of Medical Education and Informatics, Karamanoğlu Mehmetbey University, Karaman, 70200, Türkiye.
With the importance of time and cost in today's world, it is essential to solve problems in the best way possible. Optimization is a process used to achieve this goal and is applied in several areas, one of which is route planning. Route optimization minimizes the use of resources such as fuel, distance, and time.
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January 2025
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China.
Multi-objective and multi-stage decision-making problems require balancing multiple objectives at each stage and making optimal decision in multi-dimensional control variables, where the commonly used intelligent optimization algorithms suffer from low solving efficiency. To this end, this paper proposes an efficient algorithm named non-dominated sorting dynamic programming (NSDP), which incorporates non-dominated sorting into the traditional dynamic programming method. To improve the solving efficiency and solution diversity, two fast non-dominated sorting methods and a dynamic-crowding-distance based elitism strategy are integrated into the NSDP algorithm.
View Article and Find Full Text PDFHeliyon
November 2024
Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi- 6204, Bangladesh.
To balance the convergence speed and solution diversity and enhance optimization performance when addressing large-scale optimization problems, this research study presents an improved ant colony optimization (ICMPACO) technique. Its foundations include the co-evolution mechanism, the multi-population strategy, the pheromone diffusion mechanism, and the pheromone updating method. The suggested ICMPACO approach separates the ant population into elite and common categories and breaks the optimization problem into several sub-problems to boost the convergence rate and prevent slipping into the local optimum value.
View Article and Find Full Text PDFMolecules
October 2024
School of Medicine, and Warshel Institute for Computational Biology, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
CRISPR-Cas12e is a recently identified gene-editing tool mainly known because its relatively small size benefits cell delivery. Drastically different from Cas9, it creates a blunt-end double-strand breakage of the DNA via two cleavage sites; Cas12e produces a sticky-end double-strand breakage of the DNA through only one cleavage site in its RuvC domain, meaning two consecutive cleavage events first on the non-target strand (ntsDNA) and then the target strand (tsDNA). Though crucial for Cas12e's cleavage efficiency, the mechanism by which Cas12e loads tsDNA for the second cleavage remains elusive.
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