Paper Info
Title
Hybridisation of decomposition and GRASP for combinatorial multiobjective optimisation
Abstract
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.
Year
DOI
Venue
2014
10.1109/UKCI.2014.6930173
UKCI
Keywords
Field
DocType
greedy randomised adaptive search procedure,greedy genetic crossover,grasp,decomposition hybridisation,multiobjective 0-1 knapsack problem,multiobjective evolutionary algorithms,neighbourhood information,combinatorial mathematics,search problems,pareto optimisation,moea/d,greedy algorithms,multistart single-objective metaheuristic,single-objective optimisation,genetic algorithms,combinatorial multiobjective optimisation problem,knapsack problems,heuristic local search procedures,local pareto optimal solutions,single-objecive subproblems
Mathematical optimization,Heuristic,Crossover,GRASP,Evolutionary algorithm,Algorithm,Knapsack problem,Local search (optimization),Greedy randomized adaptive search procedure,Mathematics,Metaheuristic
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
3
Name
Order
Citations
PageRank
Ahmad Alhindi101.01
Qingfu Zhang27634255.05
Edward P. K. Tsang389987.77