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Hybridisation of decomposition and GRASP for combinatorial multiobjective optimisation

Alhindi, A and Zhang, Q and Tsang, E (2014) Hybridisation of decomposition and GRASP for combinatorial multiobjective optimisation. In: UNSPECIFIED, ? - ?.

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This paper proposes an idea of using heuristic local search procedures specific for single-objective optimisation in multiobjectie evolutionary algorithms (MOEAs). In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) hybridised with a multi-start single-objective metaheuristic called greedy randomised adaptive search procedure (GRASP). In our method a multiobjetive optimisation problem (MOP) is decomposed into a number of single-objecive subproblems and optimised in parallel by using neighbourhood information. The proposed GRASP alternates between subproblems to help them escape local Pareto optimal solutions. Experimental results have demonstrated that MOEA/D with GRASP outperforms the classical MOEA/D algorithm on the multiobjective 0-1 knapsack problem that is commonly used in the literature. It has also demonstrated that the use of greedy genetic crossover can significantly improve the algorithm performance.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 19 Jul 2015 16:38
Last Modified: 07 Apr 2021 10:16

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