Scalability of multiobjective genetic local search to many-objective problems: Knapsack problem case studies
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Summary
This paper examines the scalability of multiobjective genetic local search (MOGLS) to many-objective problems using a hybrid algorithm of NSGA-lI and local search and shows by experimental results that the performance of NS GA-li is improved by the hybridization with local search independent of the number of objectives.
- Type
- article
- Published
- 2008-06-01
- Cited by
- 7
- References
- 32
- OpenAlex
- https://openalex.org/W2052770178
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:9425340
Keywords
Knapsack problem, Scalability, Multi-objective optimization, Mathematical optimization, Local search (optimization)
References
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- Performance evaluation of simple multiobjective genetic local search algorithms on multiobjective 0/1 knapsack problems
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- Evolutionary many-objective optimisation: many once or one many?
- Genetic local search for multi-objective combinatorial optimization
- On the computational efficiency of multiple objective metaheuristics. The knapsack problem case study
- SMS-EMOA: Multiobjective selection based on dominated hypervolume
- MSOPS-II: A general-purpose Many-Objective optimiser
- Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
- A multi-objective genetic local search algorithm and its application to flowshop scheduling
- Reference point based multi-objective optimization using evolutionary algorithms
- Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling
- A fast and elitist multiobjective genetic algorithm: NSGA-II
- MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
- Ranking-Dominance and Many-Objective Optimization
- On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment
- Comparison between Single-Objective and Multi-Objective Genetic Algorithms: Performance Comparison and Performance Measures
- Performance Scaling of Multi-objective Evolutionary Algorithms
- Indicator-Based Selection in Multiobjective Search
Cited by
- A survey on multi-objective evolutionary algorithms for many-objective problems
- HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm
- Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem
- Probabilistic Analysis of Pareto Front Approximation for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm
- Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm
- Solving binary multi-objective knapsack problems with novel greedy strategy
- The Pyrenees International Workshop and Summer School on Statistics, Probability and Operations Research : SPO 2009 : Jaca, Spain, September 15-18, 2009
- Many-Objective Test Problems to Visually Examine the Behavior of Multiobjective Evolution in a Decision Space
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