A survey on evolutionary computation for complex continuous optimization
Complex continuous optimization problems widely exist nowadays due to the fast
development of the economy and society. Moreover, the technologies like Internet of things …
development of the economy and society. Moreover, the technologies like Internet of things …
Handling constrained multiobjective optimization problems with constraints in both the decision and objective spaces
Constrained multiobjective optimization problems (CMOPs) are frequently encountered in
real-world applications, which usually involve constraints in both the decision and objective …
real-world applications, which usually involve constraints in both the decision and objective …
Handling constrained multiobjective optimization problems via bidirectional coevolution
Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective
functions and various constraints. Due to the presence of constraints, CMOPs' Pareto …
functions and various constraints. Due to the presence of constraints, CMOPs' Pareto …
Dynamic selection preference-assisted constrained multiobjective differential evolution
Solving constrained multiobjective optimization problems brings great challenges to an
evolutionary algorithm, since it simultaneously requires the optimization among several …
evolutionary algorithm, since it simultaneously requires the optimization among several …
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 …
Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization
Proposing new mutation strategies to improve the optimization performance of differential
evolution (DE) is an important research study. Therefore, the main contribution of this paper …
evolution (DE) is an important research study. Therefore, the main contribution of this paper …
Shift-based penalty for evolutionary constrained multiobjective optimization and its application
Z Ma, Y Wang - IEEE Transactions on Cybernetics, 2021 - ieeexplore.ieee.org
This article presents a new constraint-handling technique (CHT), called shift-based penalty
(ShiP), for solving constrained multiobjective optimization problems. In ShiP, infeasible …
(ShiP), for solving constrained multiobjective optimization problems. In ShiP, infeasible …
Composite differential evolution for constrained evolutionary optimization
When solving constrained optimization problems (COPs) by evolutionary algorithms, the
search algorithm plays a crucial role. In general, we expect that the search algorithm has the …
search algorithm plays a crucial role. In general, we expect that the search algorithm has the …
A recovery planning model for online business operations under the COVID-19 outbreak
This study analytically develops a new recovery planning optimisation model for managing
the impacts of the recent COVID-19 outbreak for online business operations. Firstly, a …
the impacts of the recent COVID-19 outbreak for online business operations. Firstly, a …
Self-adaptive resources allocation-based differential evolution for constrained evolutionary optimization
When using evolutionary algorithms to address constrained optimization problems, it is
important to balance not only the diversity and convergence but also the constraints and …
important to balance not only the diversity and convergence but also the constraints and …