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A review on constraint handling techniques for population-based algorithms: from single-objective to multi-objective optimization
Most real-world problems involve some type of optimization problems that are often
constrained. Numerous researchers have investigated several techniques to deal with …
constrained. Numerous researchers have investigated several techniques to deal with …
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 …
Evolutionary constrained multiobjective optimization: Test suite construction and performance comparisons
For solving constrained multiobjective optimization problems (CMOPs), many algorithms
have been proposed in the evolutionary computation research community for the past two …
have been proposed in the evolutionary computation research community for the past two …
Push and pull search for solving constrained multi-objective optimization problems
This paper proposes a push and pull search (PPS) framework for solving constrained multi-
objective optimization problems (CMOPs). To be more specific, the proposed PPS divides …
objective optimization problems (CMOPs). To be more specific, the proposed PPS divides …
A constrained multiobjective evolutionary algorithm with detect-and-escape strategy
Overall constraint violation functions are commonly used in multiobjective evolutionary
algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck …
algorithms (MOEAs) for handling constraints. Constraints could cause these algorithms stuck …
Shift-based penalty for evolutionary constrained multiobjective optimization and its application
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 …
An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions
This paper proposes an improved epsilon constraint-handling mechanism and combines it
with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve …
with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve …
Handling constrained many-objective optimization problems via problem transformation
Objectives optimization and constraints satisfaction are two equally important goals to solve
constrained many-objective optimization problems (CMaOPs). However, most existing …
constrained many-objective optimization problems (CMaOPs). However, most existing …
A new fitness function with two rankings for evolutionary constrained multiobjective optimization
Among the constraint-handling techniques (CHTs) in constrained multiobjective
optimization, constrained dominance principle (CDP) is simple, flexible, nonparametric, and …
optimization, constrained dominance principle (CDP) is simple, flexible, nonparametric, and …
A data-driven digital transformation approach for reverse logistics optimization in a medical waste management system
COVID-19's aftereffects have had a significant impact on our daily lives. The recent
pandemic caused by the new coronavirus epidemic has increased the production of …
pandemic caused by the new coronavirus epidemic has increased the production of …