Solving 0/1 Knapsack Problem Using Hybrid TLBO-GA Algorithm

Umbarkar, A. J.; Sheth, P. D.; Babar, S. V.

doi:10.1007/978-81-322-2217-0_1

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…The mechanism adopted in this paper for solving TSP with GA was self-adapting cross and self-adapting mutation operator [7][8] , and the strategy of retaining optimal values after crosses and mutations of every generation was adopted [9] . With the improved mechanism, impacts of different selection modes on GA could be better reflected.…”

Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Improved Fitness Proportionate Selection-Based Genetic Algorithm

Yu¹,

Fu²,

Li³

et al. 2016

Proceedings of the 2016 3rd International Conference on Mechatronics and Information Technology

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Genetic algorithms are typical swarm intelligence techniques based on the mechanics of natural selection and natural genetics , which combines artificial survival of the fittest concept with genetic operations abstracted from nature. Since the genetic algorithm has good global search capability, as well as the parallel nature of other advantages, it has been widely used in combinatorial optimization, machine learning, signal processing field, adaptive control and artificial life and so on. It is one of the key technologies related to modern intelligent calculation. Fitness proportionate selection, as a common selection method for GA, is usually implemented with method of roulette wheel selection. In this paper，An improved selection method based on fitness proportionate selection was presented. Computational results show that the method which proposed in this paper improved the result precision and better astringency by solving TSP problem.

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Section: Implementation and Experimental Resultsmentioning

confidence: 99%

Improved Fitness Proportionate Selection-Based Genetic Algorithm

Yu¹,

Fu²,

Li³

et al. 2016

Proceedings of the 2016 3rd International Conference on Mechatronics and Information Technology

View full text Add to dashboard Cite

show abstract

“…Harmony search considerations and pitch adjustment rules are utilized to construct BHS. At the same time, some hybrid algorithms are proposed to optimize KP01, such as hybrid cuckoo search algorithm with global harmony search [31], hybrid BPSO-GA algorithm [32], hybrid TLBO-GA algorithm [33], hybrid BPSOGSA [34], etc.…”

Section: Introductionmentioning

confidence: 99%

A Binary Multi-Scale Quantum Harmonic Oscillator Algorithm for 0–1 Knapsack Problem With Genetic Operator

Huang

Wang

et al. 2019

IEEE Access

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The 0-1 knapsack problem is a typical discrete combinatorial optimization problem with numerous applications. In this paper, a binary multi-scale quantum harmonic oscillator algorithm (BMQHOA) with genetic operator is proposed for solving 0-1 knapsack problem. The framework of BMQHOA is consisted of three nested phases including energy level stablization, energy level decline and scale adjustment. In BMQHOA, the number of different bits between solutions is defined as the distance between solutions to map the continuous search space into the discrete search space. Repair operator with greedy strategy is adopted in BMQHOA to guarantee the knapsack capacity constraint. The current best solution is used to perform a random mutation with the origin solutions, making solutions evolve towards the current optimal solution. The performance of BMQHOA is evaluated on two low-dimensional and three high-dimensional KP01 data sets, and computational results are compared with several state-of-art 0-1 knapsack algorithms. Experimental results demonstrate that the proposed BMQHOA can get the best solutions of most knapsack data sets, and performs well on convergence and stability.INDEX TERMS Evolutionary computation, knapsack problem, binary multi-scale quantum harmonic oscillator algorithm, genetic operator.

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