Abstract: Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the ...

This paper introduces a new human-based metaheuristic algorithm called Sewing Training-Based Optimization (STBO), which has applications in handling optimization tasks. The fundamental inspiration of ...

In the realm of science, problems that have multiple feasible solutions are referred to as optimization problems. Therefore, finding the best feasible solution among all the available solutions for a ...

Abstract: The complex and heterogeneous ecosystem of the Internet of Things (IoT) makes it difficult to achieve energy-efficient routing because of the power, memory, and processing constraints of ...

Metaheuristic algorithms are problem-solving approaches that draw inspiration from the processes observed in nature, such as evolution, swarm behavior, and physical phenomena. Unlike traditional ...

This is equivalent to MetaheuristicAlgorithms written in Ruby (https://github.com/tadatoshi/metaheuristic_algorithms). The reason why I wrote it in Python is that I ...

ABSTRACT: This paper presents a multi-objective production planning model for a factory operating under a multi-product, and multi-period environment using the lexicographic (pre-emptive) procedure.

If you are working on a complex optimization problem, such as finding the best route for a delivery truck, you might need to use an algorithm that can find a good solution in a reasonable time. But ...

Genetic Algorithms (GA) are a powerful class of optimization algorithms inspired by the principles of natural selection and genetics. Developed by John Holland in the 1960s and 1970s, GAs are used to ...