Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey
This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over
the last 20 years, highlighting its success in solving complex optimization problems. We …
the last 20 years, highlighting its success in solving complex optimization problems. We …
Bridging evolutionary algorithms and reinforcement learning: A comprehensive survey on hybrid algorithms
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
Differential evolution using improved crowding distance for multimodal multiobjective optimization
In multiobjective optimization, the relationship between decision space and objective space
is generally assumed to be a one-to-one map**, but it is not always the case. In some …
is generally assumed to be a one-to-one map**, but it is not always the case. In some …
Deep reinforcement learning assisted co-evolutionary differential evolution for constrained optimization
Solving constrained optimization problems (COPs) with evolutionary algorithms (EAs) is a
popular research direction due to its potential and diverse applications. One of the key …
popular research direction due to its potential and diverse applications. One of the key …
A clustering-based differential evolution algorithm for solving multimodal multi-objective optimization problems
Abstract Multimodal Multi-objective Optimization Problems (MMOPs) refer to the problems
that have multiple Pareto-optimal solution sets in decision space corresponding to the same …
that have multiple Pareto-optimal solution sets in decision space corresponding to the same …
[HTML][HTML] Reinforcement learning-based differential evolution for parameters extraction of photovoltaic models
In photovoltaic (PV) model, it is an urgent problem to control and optimize the accurate
parameters. Hence, many algorithms have been proposed for parameter extraction of …
parameters. Hence, many algorithms have been proposed for parameter extraction of …
Q-Learning-based parameter control in differential evolution for structural optimization
The operations of metaheuristic optimization algorithms depend heavily on the setting of
control parameters. Therefore the addition of adaptive control parameter has been widely …
control parameters. Therefore the addition of adaptive control parameter has been widely …
Grid search based multi-population particle swarm optimization algorithm for multimodal multi-objective optimization
In the multimodal multi-objective optimization problems (MMOPs), there may exist two or
multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of …
multiple equivalent Pareto optimal sets (PS) with the same Pareto Front (PF). The difficulty of …
RL-GA: A reinforcement learning-based genetic algorithm for electromagnetic detection satellite scheduling problem
The study of electromagnetic detection satellite scheduling problem (EDSSP) has attracted
attention due to the detection requirements for a large number of targets. This paper …
attention due to the detection requirements for a large number of targets. This paper …
A survey on learnable evolutionary algorithms for scalable multiobjective optimization
Recent decades have witnessed great advancements in multiobjective evolutionary
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …
algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these …