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Population Decomposition-Based Greedy Approach Algorithm for the Multi-Objective Knapsack Problems.
- Source :
-
International Journal of Pattern Recognition & Artificial Intelligence . Apr2017, Vol. 31 Issue 4, p-1. 17p. - Publication Year :
- 2017
-
Abstract
- Despite the effectiveness of the decomposition-based multi-objective evolutional algorithm (MOEA/D-M2M) in solving continuous multi-objective optimization problems (MOPs), its performance in addressing 0/1 multi-objective knapsack problems (MOKPs) has not been fully explored. In this paper, we use MOEA/D-M2M with an improved greedy repair strategy to solve MOKPs. It first decomposes an MOKP into a number of simple optimization subproblems and solves them in a collaborative way. Each subproblem has its own subpopulation, and then an improved greedy strategy is introduced to improve the performance of the proposed algorithm on MOKPs. Therein, a weight vector chosen randomly from a corresponding subpopulation is utilized to repair infeasible individuals or improve feasible individuals to have a better fitness, which improves the convergence of the population. Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA/D in terms of finding better approximations to the Pareto front. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 31
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 121081899
- Full Text :
- https://doi.org/10.1142/S0218001417590066