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 …
Differential evolution-based feature selection: A niching-based multiobjective approach
Feature selection is to reduce both the dimensionality of data and the classification error rate
(ie, increase the classification accuracy) of a learning algorithm. The two objectives are often …
(ie, increase the classification accuracy) of a learning algorithm. The two objectives are often …
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 …
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 …
Multi-modal multi-objective particle swarm optimization with self-adjusting strategy
H Han, Y Liu, Y Hou, J Qiao - Information Sciences, 2023 - Elsevier
Since the exploration of multiple solution sets will lead to the deterioration of convergence in
multi-objective particle swarm optimization, the motion of the particles is severely disturbed …
multi-objective particle swarm optimization, the motion of the particles is severely disturbed …
Multiobjective differential evolution with speciation for constrained multimodal multiobjective optimization
This article proposes a novel differential evolution algorithm for solving constrained
multimodal multiobjective optimization problems (CMMOPs), which may have multiple …
multimodal multiobjective optimization problems (CMMOPs), which may have multiple …
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 …