Continuous metaheuristics for binary optimization problems: An updated systematic literature review
For years, extensive research has been in the binarization of continuous metaheuristics for
solving binary-domain combinatorial problems. This paper is a continuation of a previous …
solving binary-domain combinatorial problems. This paper is a continuation of a previous …
Binarization of Metaheuristics: Is the Transfer Function Really Important?
In this work, an approach is proposed to solve binary combinatorial problems using
continuous metaheuristics. It focuses on the importance of binarization in the optimization …
continuous metaheuristics. It focuses on the importance of binarization in the optimization …
Q-learnheuristics: Towards data-driven balanced metaheuristics
One of the central issues that must be resolved for a metaheuristic optimization process to
work well is the dilemma of the balance between exploration and exploitation. The …
work well is the dilemma of the balance between exploration and exploitation. The …
A novel learning-based binarization scheme selector for swarm algorithms solving combinatorial problems
Currently, industry is undergoing an exponential increase in binary-based combinatorial
problems. In this regard, metaheuristics have been a common trend in the field in order to …
problems. In this regard, metaheuristics have been a common trend in the field in order to …
Embedded learning approaches in the whale optimizer to solve coverage combinatorial problems
When we face real problems using computational resources, we understand that it is
common to find combinatorial problems in binary domains. Moreover, we have to take into …
common to find combinatorial problems in binary domains. Moreover, we have to take into …
Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector
In recent years, continuous metaheuristics have been a trend in solving binary-based
combinatorial problems due to their good results. However, to use this type of …
combinatorial problems due to their good results. However, to use this type of …
Chaotic binarization schemes for solving combinatorial optimization problems using continuous metaheuristics
Chaotic maps are sources of randomness formed by a set of rules and chaotic variables.
They have been incorporated into metaheuristics because they improve the balance of …
They have been incorporated into metaheuristics because they improve the balance of …
A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm
D Leiva, B Ramos-Tapia, B Crawford, R Soto… - Biomimetics, 2024 - mdpi.com
The set-covering problem aims to find the smallest possible set of subsets that cover all the
elements of a larger set. The difficulty of solving the set-covering problem increases as the …
elements of a larger set. The difficulty of solving the set-covering problem increases as the …
Reinforcement learning based whale optimizer
This work proposes a Reinforcement Learning based optimizer integrating SARSA and
Whale Optimization Algorithm. SARSA determines the binarization operator required during …
Whale Optimization Algorithm. SARSA determines the binarization operator required during …
A comparison of Learnheuristics using different Reward Functions to solve the Set Covering Problem
The high computational capacity that we have thanks to the new technologies allows us to
communicate two great worlds such as optimization methods and machine learning. The …
communicate two great worlds such as optimization methods and machine learning. The …