Title :
Hybridizing the Pareto Multi-Objective Optimization Evolutionary Algorithms by Means of Multi-Objective Local Search
Author :
Haidine, Abdelfatteh ; Lehnert, Ralf
Author_Institution :
Tech. Univ. Dresden, Dresden
Abstract :
Hybridizing of evolutionary algorithms (EA) by means of local search has shown considerable performance improvement in single-objective optimization (SOO) field. The fine search in the neighborhood of the EA individuals (solutions) allows a fine exploration of the solution space. This paper investigates the application and the evaluation of the hybridizing mechanism of the EAs in the multi-objective optimization (MOO) domain. For this hybridizing, two types of multi-objective local search (MOLS) are used; namely large- and narrow-MOLS. Among numerous possible multi-objective optimization EAs (MOEAs), the Pareto-based variants have been considered. The performance of hybrid MOO variants are evaluated by solving the multi-objective knapsack problem.
Keywords :
Pareto optimisation; evolutionary computation; knapsack problems; search problems; Pareto multiobjective optimization; evolutionary algorithm; knapsack problem; multiobjective local search; Approximation algorithms; Constraint optimization; Convergence; Evolutionary computation; Genetic algorithms; Hybrid intelligent systems; Hybrid power systems; Pareto optimization; Sorting; Wheels; evolutionary algorithm; hybrid algorithm; multi-objective algorithm; multi-objective local search;
Conference_Titel :
Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-0-7695-3326-1
Electronic_ISBN :
978-0-7695-3326-1
DOI :
10.1109/HIS.2008.104
