Multimodal multi-objective optimization: Comparative study of the state-of-the-art
Multimodal multi-objective problems (MMOPs) commonly arise in the real world where
distant solutions in decision space correspond to very similar objective values. To obtain …
distant solutions in decision space correspond to very similar objective values. To obtain …
A survey on evolutionary constrained multiobjective optimization
Handling constrained multiobjective optimization problems (CMOPs) is extremely
challenging, since multiple conflicting objectives subject to various constraints require to be …
challenging, since multiple conflicting objectives subject to various constraints require to be …
Process knowledge-guided autonomous evolutionary optimization for constrained multiobjective problems
Various real-world problems can be attributed to constrained multiobjective optimization
problems (CMOPs). Although there are various solution methods, it is still very challenging …
problems (CMOPs). Although there are various solution methods, it is still very challenging …
Improved differential evolution using two-stage mutation strategy for multimodal multi-objective optimization
Y Wang, Z Liu, GG Wang - Swarm and Evolutionary Computation, 2023 - Elsevier
Recently, multimodal multi-objective problem (MMOP) has become a popular research field
in multi-objective optimization problems. Multimodal multi-objective optimization problem …
in multi-objective optimization problems. Multimodal multi-objective optimization problem …
Function value ranking aware differential evolution for global numerical optimization
Differential evolution (DE) has been experimentally demonstrated to be effective in solving
optimization problems. However, the effectiveness of DE encounters rapid deterioration in …
optimization problems. However, the effectiveness of DE encounters rapid deterioration in …
Development of the multi-objective adaptive guided differential evolution and optimization of the MO-ACOPF for wind/PV/tidal energy sources
Currently, one of the most popular research topics is the development of a new meta-
heuristic algorithm for solving multi-objective optimization problems. However, few of the …
heuristic algorithm for solving multi-objective optimization problems. However, few of the …
Unified space approach-based Dynamic Switched Crowding (DSC): a new method for designing Pareto-based multi/many-objective algorithms
This study proposes a robust method to improve the search performance of multi-objective
evolutionary algorithms (MOEAs) using a Pareto-based archiving mechanism and a …
evolutionary algorithms (MOEAs) using a Pareto-based archiving mechanism and a …
A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems
S Yin, Z **ang - Expert Systems with Applications, 2024 - Elsevier
This paper proposes a hyper-heuristic evolutionary algorithm via proximal policy
optimization, named HHEA-PPO, for solving multi-objective truss optimization problems …
optimization, named HHEA-PPO, for solving multi-objective truss optimization problems …
Balancing convergence and diversity in objective and decision spaces for multimodal multi-objective optimization
Solving multimodal multi-objective optimization problems (MMOPs) via evolutionary
algorithms receives increasing attention recently. Maintaining good diversity in both decision …
algorithms receives increasing attention recently. Maintaining good diversity in both decision …
An evolutionary algorithm based on independently evolving sub-problems for multimodal multi-objective optimization
Multimodal multi-objective problems (MMOPs) arise frequently in the real world, in which
multiple Pareto optimal solution (PS) sets correspond to the same objective set. Traditional …
multiple Pareto optimal solution (PS) sets correspond to the same objective set. Traditional …