In this paper, we propose a calculation method of local dominance and enhance multiobjective evolutionary algorithms by performing a distributed search based on local dominance. We divide the population into several sub-populations by using declination angles of polar coordinate vectors in the objective space. We calculate local dominance for individuals belonging to each sub-population based on the local search direction, and apply genetic operators to individuals within each sub-population. We verify the effectiveness of the proposed method by comparing the search performance between NSGA-II, SPEA2 and their enhanced versions. 1
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its ...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
Abstract — This work studies and compares the effects on performance of local dominance and local re...
Since practical problems often are very complex with a large number of objectives it can be difficul...
Using multiple local evolutionary searches, instead of single and overall search, has been an effect...
Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has ...
Hybridization of local search based algorithms with evolutionary algorithms is still an under-explo...
Hybridization of local search based algorithms with evolutionary algorithms is still an under-explo...
Division of the evolutionary search among multiple multi-objective evolutionary algorithms (MOEAs) i...
Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most...
A local search method is often introduced in an evolutionary optimization technique to enhance its s...
A local search method is often introduced in an evolutionary optimization technique to enhance its s...
This article proposes a simple yet effective multiobjective evolutionary algorithm (EA) for dealing ...
Multiobjective evolutionary algorithms (MOEAs) are useful tools capable of searching problems that c...
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its ...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
Abstract — This work studies and compares the effects on performance of local dominance and local re...
Since practical problems often are very complex with a large number of objectives it can be difficul...
Using multiple local evolutionary searches, instead of single and overall search, has been an effect...
Over the past decade, subdividing evolutionary search into multiple local evolutionary searches has ...
Hybridization of local search based algorithms with evolutionary algorithms is still an under-explo...
Hybridization of local search based algorithms with evolutionary algorithms is still an under-explo...
Division of the evolutionary search among multiple multi-objective evolutionary algorithms (MOEAs) i...
Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most...
A local search method is often introduced in an evolutionary optimization technique to enhance its s...
A local search method is often introduced in an evolutionary optimization technique to enhance its s...
This article proposes a simple yet effective multiobjective evolutionary algorithm (EA) for dealing ...
Multiobjective evolutionary algorithms (MOEAs) are useful tools capable of searching problems that c...
A local search method is often introduced in an evolutionary optimization algorithm, to enhance its ...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
In this paper, Hamming distance is used to control individual difference in the process of creating ...
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