The task of optimization is no easy task and from a computational point of view, it often involves scanning a large search space to find the best solution that adheres to all the constraints and desired specifications. Designing a customized algorithm to solve several optimization problems is also a challenging task, therefore scientists and engineers utilize metaheuristic algorithms that can provide an optimal solution within a reasonable time. This optimal solution may or may not be the best solution in the search space, but it is usually good enough to satisfy the requirements without spending a lot of computational resources or time. The 0–1 knapsack problem is an constraint-based optimization problem in which a number of items have to be packed into a container by maximizing the value of the items in the container while also adhering to the weight limit of the container. In this paper, sine-cosine algorithm (SCA) is adopted to solve 0–1 knapsack problems. The proposed algorithm is called binary sine-cosine algorithm (BSCA). Due to the binary nature of 0–1 knapsack problem, the SCA is manipulated using a mapping function. The performance of the proposed BSCA is evaluated using 15 well-known datasets. Furthermore, the performance of the proposed BSCA is compared with other comparative algorithms (i.e., GA, PSO, and BFPA) from the literature using the same datasests. It can be observed from the results that the performance of the proposed BSCA is similar to other algorithms by obtaining the optimal results on 10 datasets. While the results of the proposed BSCA are convergent with others for the remaining five datasets.
The task of optimization is no easy task and from a computational point of view, it often involves scanning a large search space to find the best solution that adheres to all the constraints and desired specifications. Designing a customized algorithm to solve several optimization problems is also a challenging task, therefore scientists and engineers utilize metaheuristic algorithms that can provide an optimal solution within a reasonable time. This optimal solution may or may not be the best solution in the search space, but it is usually good enough to satisfy the requirements without spending a lot of computational resources or time. The 0–1 knapsack problem is an constraint-based optimization problem in which a number of items have to be packed into a container by maximizing the value of the items in the container while also adhering to the weight limit of the container. In this paper, sine-cosine algorithm (SCA) is adopted to solve 0–1 knapsack problems. The proposed algorithm is called binary sine-cosine algorithm (BSCA). Due to the binary nature of 0–1 knapsack problem, the SCA is manipulated using a mapping function. The performance of the proposed BSCA is evaluated using 15 well-known datasets. Furthermore, the performance of the proposed BSCA is compared with other comparative algorithms (i.e., GA, PSO, and BFPA) from the literature using the same datasests. It can be observed from the results that the performance of the proposed BSCA is similar to other algorithms by obtaining the optimal results on 10 datasets. While the results of the proposed BSCA are convergent with others for the remaining five datasets.