Many-objective evolutionary algorithms: A survey
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …
applications. However, most MOEAs based on Pareto-dominance handle many-objective …
An overview of population-based algorithms for multi-objective optimisation
In this work we present an overview of the most prominent population-based algorithms and
the methodologies used to extend them to multiple objective problems. Although not exact in …
the methodologies used to extend them to multiple objective problems. Although not exact in …
A strengthened dominance relation considering convergence and diversity for evolutionary many-objective optimization
Both convergence and diversity are crucial to evolutionary many-objective optimization,
whereas most existing dominance relations show poor performance in balancing them, thus …
whereas most existing dominance relations show poor performance in balancing them, thus …
A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization
While Pareto-based multiobjective optimization algorithms continue to show effectiveness
for a wide range of practical problems that involve mostly two or three objectives, their …
for a wide range of practical problems that involve mostly two or three objectives, their …
Methods for multi-objective optimization: An analysis
Decomposition-based methods are often cited as the solution to multi-objective nonconvex
optimization problems with an increased number of objectives. These methods employ a …
optimization problems with an increased number of objectives. These methods employ a …
Effectiveness and efficiency of non-dominated sorting for evolutionary multi-and many-objective optimization
Since non-dominated sorting was first adopted in NSGA in 1995, most evolutionary
algorithms have employed non-dominated sorting as one of the major criteria in their …
algorithms have employed non-dominated sorting as one of the major criteria in their …
Constrained subproblems in a decomposition-based multiobjective evolutionary algorithm
A decomposition approach decomposes a multiobjective optimization problem into a
number of scalar objective optimization subproblems. It plays a key role in decomposition …
number of scalar objective optimization subproblems. It plays a key role in decomposition …
A new many-objective evolutionary algorithm based on generalized Pareto dominance
In the past several years, it has become apparent that the effectiveness of Pareto-dominance-
based multiobjective evolutionary algorithms deteriorates progressively as the number of …
based multiobjective evolutionary algorithms deteriorates progressively as the number of …
Controller tuning using evolutionary multi-objective optimisation: current trends and applications
Control engineering problems are generally multi-objective problems; meaning that there
are several specifications and requirements that must be fulfilled. A traditional approach for …
are several specifications and requirements that must be fulfilled. A traditional approach for …
An angle dominance criterion for evolutionary many-objective optimization
It is known that Pareto dominance encounters difficulties in many-objective optimization.
This strict criterion could make most individuals of a population incomparable in a high …
This strict criterion could make most individuals of a population incomparable in a high …