A review on probabilistic graphical models in evolutionary computation
Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains …
candidates for machine learning and decision making tasks especially in uncertain domains …
Adapting reference vectors and scalarizing functions by growing neural gas to handle irregular Pareto fronts
The performance of decomposition-based multiobjective evolutionary algorithms (MOEAs)
often deteriorates clearly when solving multiobjective optimization problems with irregular …
often deteriorates clearly when solving multiobjective optimization problems with irregular …
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 …
Bat algorithm and cuckoo search: a tutorial
XS Yang - … , evolutionary computing and metaheuristics: in the …, 2013 - Springer
Nature-inspired metaheuristic algorithms have attracted much attention in the last decade,
and new algorithms have emerged almost every year with a vast, ever-expanding literature …
and new algorithms have emerged almost every year with a vast, ever-expanding literature …
A novel particle swarm optimization algorithm with Lévy flight and orthogonal learning
Z Wang, Y Chen, S Ding, D Liang, H He - Swarm and Evolutionary …, 2022 - Elsevier
Taguchi method This paper presents a novel particle swarm optimization algorithm (PSO)
variant to tackle single-objective numerical optimization, named “Lévy flight orthogonal …
variant to tackle single-objective numerical optimization, named “Lévy flight orthogonal …
Enhancing the performance of cuckoo search algorithm using orthogonal learning method
The cuckoo search algorithm is a simple and effective global optimization algorithm. It has
been successfully applied to solve a wide range of real-world optimization problem. In this …
been successfully applied to solve a wide range of real-world optimization problem. In this …
Growing Neural Gas Network-based surrogate-assisted Pareto set learning for multimodal multi-objective optimization
The key issue in handling multimodal multi-objective optimization problems (MMOPs) is to
find multiple Pareto sets (PSs) corresponding to one Pareto front (PF). Therefore, learning …
find multiple Pareto sets (PSs) corresponding to one Pareto front (PF). Therefore, learning …
Multiobjective estimation of distribution algorithm based on joint modeling of objectives and variables
This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based
on joint probabilistic modeling of objectives and variables. This EDA uses the …
on joint probabilistic modeling of objectives and variables. This EDA uses the …
Self-adaptive constrained artificial bee colony for constrained numerical optimization
The artificial bee colony is a simple and effective global optimization algorithm. It has been
successfully applied to solve a wide range of real-world optimization problem, and later, it …
successfully applied to solve a wide range of real-world optimization problem, and later, it …
A stopping criterion for multi-objective optimization evolutionary algorithms
This paper puts forward a comprehensive study of the design of global stopping criteria for
multi-objective optimization. In this study we propose a global stopping criterion, which is …
multi-objective optimization. In this study we propose a global stopping criterion, which is …