Combining ideas inspired by ant colonies and flocks of birds may hold the key to unlocking more effective artificial ...
Neural-guided Ant Colony Optimization (ACO) currently suffers from a fundamental training-inference misalignment: policies are typically trained to generate static priors (e.g., heatmaps) from ...
Python provides an integrated analytical ecosystem for solving core supply chain problems such as demand forecasting, inventory planning, transportation routing, and operational simulation.
Abstract: Set meal design (SMD) for online-to-offline (O2O) restaurant services presents a complex optimization problem, requiring the simultaneous satisfaction of diverse customer preferences, ...
Initially, several strategies transform the multi-label learning challenge into one or more single-label problems to utilize single-label learning techniques 8,9,10. Secondly, other methods involve ...
In the relentless pursuit of enhancing customer engagement, the retail sector is turning towards innovative solutions. One such solution, inspired by nature, is the Ant Colony Optimization (ACO) ...
Our Computer Science Ph.D. students are innovators, problem-solvers, and future leaders in the field. From pioneering research in AI and cybersecurity to breakthroughs in data science and HCI, they’re ...
Customers are the most valuable resource as they present the main reason for any industry's success. On the other hand, a churner is a customer who abandons his current company to join another ...
In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed.
Abstract: Ant Colony Optimization algorithms have been successfully applied to solve the Traveling Salesman Problem (TSP). However, they still have a tendency to fall into local optima, mainly ...
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