In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence pressure of Pareto dominance with the increase in the number of objectives, numerous modified dominance relationships were proposed. Recently, the strengthened dominance relation (SDR) has been proposed, where the dominance area of a solution is determined by convergence degree and niche size (θ¯). Later, in controlled SDR (CSDR), θ¯ and an additional parameter (k) associated with the convergence degree are dynamically adjusted depending on the iteration count. Depending on the problem characteristics and the distribution of the current population, different situations require different values of k, rendering the linear reduction of k based on the...
Finding the overall Pareto optimal front while addressing the effect of an increasing number of obje...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide ...
In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence press...
Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most...
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been...
Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in sol...
Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas mos...
Many multi-objective evolutionary algorithms (MOEAs) have been proposed over the years. Main part of...
In spite of the recent quick growth of the Evolutionary Multi-objective Optimization (EMO) research ...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
The Multi-Level Selection Genetic Algorithm (MLSGA) is shown to increase the performance of a simple...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
Finding the overall Pareto optimal front while addressing the effect of an increasing number of obje...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide ...
In multi/many-objective evolutionary algorithms (MOEAs), to alleviate the degraded convergence press...
Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most...
Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been...
Multiobjective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in sol...
Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas mos...
Many multi-objective evolutionary algorithms (MOEAs) have been proposed over the years. Main part of...
In spite of the recent quick growth of the Evolutionary Multi-objective Optimization (EMO) research ...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
The Multi-Level Selection Genetic Algorithm (MLSGA) is shown to increase the performance of a simple...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
Finding the overall Pareto optimal front while addressing the effect of an increasing number of obje...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
While Pareto-based multiobjective optimization algorithms continue to show effectiveness for a wide ...