Akademik Veri Yönetim Sistemi
SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES, cilt.41, sa.3, ss.545-564, 2023 (ESCI)
The
bin packing problem (BPP) is one of the most elaborated combinatorial
optimization problems, yet there is
still a need and room for improvement. An improved flower pollination algorithm (FPA) is proposed for the solution
of one-dimensional BPP (1DBPP). To increase efficiency, global and local
pollination procedures are modified and hybridized with the genetic algorithm
(GA). An elimination strategy that increases the quality of the solution set in
each iteration is also included and the proposed algorithm is tested on the
Scholl dataset. It is compared with the adaptive fitness-dependent optimizer
(AFDO), the improved Lévy-based whale optimization (ILWOA), and the modified
squirrel search BPP (MSBPP) algorithms. The comparison is made in terms of
metrics including the container number, minimum and average fitness values, and
minimum and average percentage performances. In terms of the container number,
the proposed algorithm yielded results equal to or better than competing
algorithms. In terms of minimum fitness value, the proposed algorithm achieved
88% more successful results than its competitors. It achieved 92% more
successful results in terms of average fitness value. Regarding minimum
percentage performance, the proposed algorithm is more successful in 93.3% of
the samples compared to AFDO. Compared to MSBPP, the proposed algorithm is
84.6% more successful. In terms of average percentage performance metric, the
proposed algorithm has better results in 90% of the samples than AFDO; and
compared to MSBPP, it is more successful in 96.1% of the samples. These results
show the effectiveness of the proposed algorithm in solving the 1DBPP problem.