[HTML][HTML] A Review of Constrained Multi-Objective Evolutionary Algorithm-Based Unmanned Aerial Vehicle Mission Planning: Key Techniques and Challenges

G Huang, M Hu, X Yang, X Wang, Y Wang, F Huang - Drones, 2024 - mdpi.com
UAV mission planning is one of the core problems in the field of UAV applications. Currently,
mission planning needs to simultaneously optimize multiple conflicting objectives and take …

Design and analysis of helper-problem-assisted evolutionary algorithm for constrained multiobjective optimization

Y Zhang, Y Tian, H Jiang, X Zhang, Y Jin - Information Sciences, 2023 - Elsevier
In recent years, solving constrained multiobjective optimization problems (CMOPs) by
introducing simple helper problems has become a popular concept. To date, no systematic …

Learning-aided evolutionary search and selection for scaling-up constrained multiobjective optimization

S Liu, Z Wang, Q Lin, J Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The existing constrained multiobjective evolutionary algorithms (CMOEAs) still have great
room for improvement in balancing populations convergence, diversity and feasibility on …

Constrained multi-objective optimization problems: Methodologies, algorithms and applications

Y Hao, C Zhao, Y Zhang, Y Cao, Z Li - Knowledge-Based Systems, 2024 - Elsevier
Constrained multi-objective optimization problems (CMOPs) are widespread in practical
applications such as engineering design, resource allocation, and scheduling optimization …

A dual-population Constrained Many-Objective Evolutionary Algorithm based on reference point and angle easing strategy

C Ji, L Wu, T Zhao, X Cai - PeerJ Computer Science, 2024 - peerj.com
Constrained many-objective optimization problems (CMaOPs) have gradually emerged in
various areas and are significant for this field. These problems often involve intricate Pareto …

A framework for constrained large-scale multi-objective white-box problems based on two-scale optimization through decision transfer

Q Wang, T Li, F Meng, B Li - Information Sciences, 2024 - Elsevier
Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so
efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To …

Kriging Surrogate Model-Based Constraint Multiobjective Particle Swarm Optimization Algorithm

H Wang, T Cai, W Pedrycz - IEEE Transactions on Cybernetics, 2025 - ieeexplore.ieee.org
The main challenge when solving constrained multiobjective optimization problems
(CMOPs) with intricate constraints and high dimensionality is how to overcome a problem of …

Joint Power Control and Resource Allocation With Task Offloading for Collaborative Device‐Edge‐Cloud Computing Systems

S Xie, K Li, W Wang, H Wang… - International Journal of …, 2024 - Wiley Online Library
Collaborative edge and cloud computing is a promising computing paradigm for reducing
the task response delay and energy consumption of devices. In this paper, we aim to jointly …

Fitness function with two rankings embedded in a push and pull search framework for constrained multi-objective optimisation problems

K Li, J Xu, S Xie, H Wang - International Journal of Bio …, 2024 - inderscienceonline.com
Optimising objectives and satisfying constraints present significant challenges in solving
constrained multi-objective optimisation problems. In this paper, we propose an algorithm …

A Coevolutionary Algorithm based on Constraints Decomposition for Constrained Multi-Objective Optimization Problems

G Li, L Li, G Cai - 2024 - researchsquare.com
Constrained multiobjective optimization problems (CMOPs) are challenging for evolutionary
algorithms (EAs). Due to the interaction of multiple constraints, the constrained Pareto fronts …