Particle Swarm Optimization for the Multidimensional Knapsack Problem

Hembecker, Fernanda; Lopes, Heitor Silvério; Godoy, Walter

doi:10.1007/978-3-540-71618-1_40

Cited by 52 publications

(40 citation statements)

References 8 publications

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“…The rest of the GA settings discovered to be the best are maintained from the parameter tuning experiments. Most of the previous approaches were tested over a small subset of SAC-94 [6,7,22,25] or over some instances that have not been used as a benchmark anymore due their small size.…”

Section: Resultsmentioning

confidence: 99%

“…The best approach turned out to be the extended approach based on (full) attributed grammars (AG) that disallows duplicate configurations in a population. Hembecker et al [22] applied particle swarm optimization (PSO) for solving MKPs. Since, different parameter settings are utilized in these studies, only an indirect comparison can be made using their results.…”

Section: Comparison Of Ma0-8 and Mma-8 To The Previous Approachesmentioning

confidence: 99%

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A case study of memetic algorithms for constraint optimization

Özcan

Başaran

2008

Soft Comput

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There is a variety of knapsack problems in the literature. Multidimensional 0-1 Knapsack Problem (MKP) is an NP-hard combinatorial optimization problem having many application areas. Many approaches have been proposed for solving this problem. In this paper, an empirical investigation of memetic algorithms (MAs) that hybridize genetic algorithms (GAs) with hill climbing for solving MKPs is provided. Two distinct sets of experiments are performed.During the initial experiments, MA parameters are tuned. GA and four MAs each using a different hill climbing method based on the same configuration are evaluated. In the second set of experiments, a self-adaptive (co-evolving) multimeme memetic algorithm (MMA) is compared to the best MA from the parameter tuning experiments. MMA utilizes the evolutionary process as a learning mechanism for choosing the appropriate hill climbing method to improve a candidate solution at a given time. Two well-known MKP benchmarks are used during the experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison Of Ma0-8 and Mma-8 To The Previous Approachesmentioning

confidence: 99%

A case study of memetic algorithms for constraint optimization

Özcan

Başaran

2008

Soft Comput

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“…This work was improved by Volgenant and Zoon [28]. More recently, a variety of exact and meta-heuristic methods have also been proposed in the literature to solve the multidimensional 0-1 knapsack problem including simulated annealing [29], [30], neural networks [31], genetic algorithms [32], memetic algorithms [33], [34], selection hyper-heuristics [35] and particle swarm optimisation [36] and core-based and tree search algorithms [37], [38], [39]. Fréville [40] provides a more complete survey of the multidimensional 0-1 knapsack problem literature.…”

Section: B Genetic Programming As a Hyper-heuristicmentioning

confidence: 99%

A genetic programming hyper-heuristic for the multidimensional knapsack problem

et al. 2014

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Abstract-Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. Early hyperheuristics focussed on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. This work investigates the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results.

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“…Population based optimization algorithms have also been applied to the MKP including genetic algorithms (GA) (Chu and Beasley, 1998;Khuri et al, 1994), ant colony optimization (Kong et al, 2008), as well as PSO. Kong and Tian (2006) used the binary PSO which includes a heuristic repair operator to avoid infeasible solutions, while Hembecker et al (2007) used penalty functions to steer the search towards solutions that satisfy the MKP's constraints. Labed et al (2011) proposed a hybrid GA binary PSO algorithm that includes a crossover operator and a separate repair operator that modifies positions to represent feasible solutions to the MKP.…”

Section: Multidimensional Knapsack Problemmentioning

confidence: 99%

Set-based particle swarm optimization applied to the multidimensional knapsack problem

Langeveld

Engelbrecht

2012

Swarm Intell

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Particle swarm optimization algorithms have been successfully applied to discrete-valued optimization problems. However, in many cases the algorithms have been tailored specifically for the problem at hand. This paper proposes a generic set-based particle swarm optimization algorithm for use on discrete-valued optimization problems that can be formulated as set-based problems. A detailed sensitivity analysis of the parameters of the algorithm is conducted. The performance of the proposed algorithm is then compared against three other discrete particle swarm optimization algorithms from literature using the multidimensional knapsack problem, and is shown to statistically outperform the existing algorithms.

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Particle Swarm Optimization for the Multidimensional Knapsack Problem

Cited by 52 publications

References 8 publications

A case study of memetic algorithms for constraint optimization

A case study of memetic algorithms for constraint optimization

A genetic programming hyper-heuristic for the multidimensional knapsack problem

Set-based particle swarm optimization applied to the multidimensional knapsack problem

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