NDLI: Local search-based heuristics for the multiobjective multidimensional knapsack problem

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Local search-based heuristics for the multiobjective multidimensional knapsack problem

Content Provider	Semantic Scholar
Author	Viannaa, Dalessandro Soares Viannab, Marcilene De Fátima Dianin
Copyright Year	2012
Abstract	In real optimization problems it is generally desirable to optimize more than one performance criterion (or objective) at the same time. The goal of the multiobjective combinatorial optimization (MOCO) is to optimize simultaneously r > 1 objectives. As in the single-objective case, the use of heuristic/metaheuristic techniques seems to be the most promising approach to MOCO problems because of their efficiency, generality and relative simplicity of implementation. In this work, we develop algorithms based on Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Local Search (ILS) metaheuristics for the multiobjective knapsack problem. Computational experiments on benchmark instances show that the proposed algorithms are very robust and outperform other heuristics in terms of solution quality and running times.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.scielo.br/pdf/prod/v23n3/aop_t6_0006_0424.pdf
Alternate Webpage(s)	http://www.scielo.br/pdf/prod/2012nahead/aop_t6_0006_0424.pdf
Language	English
Access Restriction	Open
Subject Keyword	Benchmark (computing) Combinatorial optimization Computation Experiment GRASP Greedy algorithm Greedy randomized adaptive search procedure Heuristics Integrated Learning System Iterated function Iterated local search Knapsack problem Local search (constraint satisfaction) Local search (optimization) Mathematical optimization Metaheuristic Optimization problem Randomized algorithm
Content Type	Text
Resource Type	Article

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