An Innovative Genetic Algorithm for a Multi-Objective Optimization of Two-Dimensional Cutting-Stock Problem

Mellouli, Ahmed; Mellouli, Racem; Masmoudi, Faouzi

doi:10.1080/08839514.2019.1583857

Cited by 7 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mnif et al [7] introduced a new approach called multiobjective firework algorithm (MFWA). Mellouli et al [8], in order to solve the two-dimensional cutting stock problem, combined genetic algorithm with linear programming model to estimate the best Pareto frontier for these two goals. e Pareto front provided by this algorithm is very close to the optimal front.…”

Section: Introductionmentioning

confidence: 99%

“…ComplexityFigures[2][3][4][5][6][7][8][9][10][11][12][13] show the corresponding relationship between the optimal solutions obtained by each algorithm and the true PF. In Figures2-6, we can intuitively draw the following conclusions: In the test functions, ZDT, except that the convergence of IMOPSO in ZDT3 is worse than that of NSGA-III, IMOPSO is better than the other compared algorithms in terms of convergence and distribution.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation

Gao

Wang

2020

Complexity

View full text Add to dashboard Cite

In order to solve the shortcomings of particle swarm optimization (PSO) in solving multiobjective optimization problems, an improved multiobjective particle swarm optimization (IMOPSO) algorithm is proposed. In this study, the competitive strategy was introduced into the construction process of Pareto external archives to speed up the search process of nondominated solutions, thereby increasing the speed of the establishment of Pareto external archives. In addition, the descending order of crowding distance method is used to limit the size of external archives and dynamically adjust particle parameters; in order to solve the problem of insufficient population diversity in the later stage of algorithm iteration, time-varying Gaussian mutation strategy is used to mutate the particles in external archives to improve diversity. The simulation experiment results show that the improved algorithm has better convergence and stability than the other compared algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%