Bin-Packing Using Genetic Algorithms

Ponce-Perez, A.; Perez-Garcia, A.; Ayala-Ramírez, Víctor

doi:10.1109/coniel.2005.25

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…There are many heuristics for this problem [22][23][24][25] because there does not exist an exact algorithm to solve it for its complexity. This is the reason to try to apply the metaheuristics [26][27][28], because it looks for a better result than the one obtained by heuristics.…”

Section: Introductionmentioning

confidence: 97%

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Sotelo-Figueroa

Soberanes

Carpio

et al. 2016

Nature-Inspired Design of Hybrid Intelligent Systems

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In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the poor diversity. In this paper we propose using BSO as part of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP). A comparison between BSO, PSO, and BPP heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is to propose a way to implement different algorithms as search strategy in GE. In this paper, it is proposed that the BSO obtains better results than the ones obtained by PSO, also there is a grammar proposed to generate online and offline heuristics to improve the heuristics generated by other grammars and humans.

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Section: Introductionmentioning

confidence: 97%

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Sotelo-Figueroa

Soberanes

Carpio

et al. 2016

Nature-Inspired Design of Hybrid Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…This is an NP-Hard Problem [1] and due to its complexity many heuristics have been developed attempting to give an approximation [15][16][17][18][19]. Some metaheuristics have also been applied to try to obtain better results than those obtained by heuristics [20][21][22]. Some exact algorithms have been developed [23][24][25]; however, given the nature of the problem the time reported by these algorithms grows up and depending on the instance the time may grow up exponentially.…”

Section: Introductionmentioning

confidence: 99%

Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

Sotelo-Figueroa

Soberanes

Carpio

et al. 2014

Mathematical Problems in Engineering

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In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

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Fabricating an articulate robot for automated packing system by utilizing IOT system

Nagappan

Dommeti

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Bin-Packing Using Genetic Algorithms

Cited by 8 publications

References 7 publications

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

Fabricating an articulate robot for automated packing system by utilizing IOT system

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