الفهرس 
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Abstract
recent years, the one-dimensional bin packing problem (1D-BPP) has become one of
the most famous combinatorial optimization problems. The 1D-BPP is a robust NPhard
problem that can be solved through optimization algorithms. This paper proposes
an adaptive procedure using a recently optimized swarm algorithm and fitnessdependent
optimizer (FDO), named the AFDO, to solve the BPP. The proposed
algorithm is based on the generation of a feasible initial population through a modified
well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the
most critical parameters of the algorithm are adapted for the problem. To the best of our
knowledge, this is the first study to apply the FDO algorithm in a discrete optimization
problem, especially for solving the BPP. The adaptive algorithm was tested on 30
instances obtained from benchmark datasets. The performance and evaluation results of
this algorithm were compared with those of other popular algorithms, such as the
particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya
algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed
the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the
AFDO shows superiority in terms of execution time with improvements over the
execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%,
respectively. The experimental results illustrate the effectiveness of the proposed
adaptive algorithm for solving the 1D-BPP.