Abstract
In 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 NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent 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.
Highlights
The bin packing problem (BPP) is a commonly studied combinatorial optimization problem; it can be defined as a finite collection of items with varying specifications to be packed into several bins or containers [1] without exceeding the capacity of each bin
The improvement in the AFDO in terms of the average fitness value for different test instances ranged from 5% to 7% when compared with the particle swarm optimization (PSO) and crow search algorithm (CSA) results and reached up to 10% when compared with the value for the Jaya algorithm
The proposed AFDO algorithm is based on a feasible random initial population through a modified first fit (FF) heuristic and improves the BPP solution by adjusting the parameters to search for the final optimal solution
Summary
The bin packing problem (BPP) is a commonly studied combinatorial optimization problem; it can be defined as a finite collection of items with varying specifications to be packed into several bins or containers [1] without exceeding the capacity of each bin. The 1D-BPP allows items to be packed according to a fixed dimension This problem seems simple to define, The associate editor coordinating the review of this manuscript and approving it for publication was Manuel Rosa-Zurera. Coffman Jr. et al [10] provided an overview of the approximation algorithms used for solving the 1D-BPP Another new and popular solution of this problem involves the use of metaheuristic approaches naturally inspired by biological, physical, or sociological phenomena; these include the genetic algorithm (GA) [11], particle swarm optimization (PSO) [12], and the tabu search (TS) method [13]. Solving NP-hard problems is still an open challenge; in the current study, the 1D-BPP was approached practically and effectively by proposing an adaptive version of the fitness-dependent optimizer (FDO) algorithm called the AFDO.
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