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| Methods/authors/years | Contributions |
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| MD-HACO (Imen Ben Mansour [21], 2023) | The suggested MD-HACO approach combines an ACO algorithm with a multiobjective local search process to create a multidirectional framework. The proposed method is evaluated and compared to well-known state-of-the-art algorithms on extensively utilized multiobjective multidimensional knapsack problem (MOMKP) |
| MBO and PSO (Silalahi et al. [22], 2022) | The 0-1 knapsack problem is addressed in this work, along with its solution using the migrating birds optimization and particle swarm optimization methods |
| BPSO-SA (Zhang et al. [23], 2022) | The study proposes a new approach for optimization that combines the advantages of the simulated annealing algorithm (SA) with the binary particle swarm optimization algorithm (BPSO). For the fusion optimization technique, they develop a knapsack model of logistics |
| TSTS (Miranda-Burgos and Rojas-Morales [24], 2022) | The work presented here suggests opposition-inspired techniques as a way to increase the diversity of tabu search (TS) algorithms that have been suggested for solving KPs |
| MFEA (Du et al. [25], 2022) | A novel mixed-factor evolutionary algorithm (MFEA) is suggested, implemented, and evaluated on the multiobjective knapsack problem and then compared to five state-of-the-art algorithms |
| Recurrent neural networks (Hertrich and Skutella [26], 2021) | The authors demonstrated that neural networks are able to accurately foretell linear KP solutions. They show that the size of the neural network used to forecast the solution to a KP instance is proportional to the size of the instance |
| Genetic-based PSO (gbPSO) (Ozsoydan and Gölcük [27], 2023) | The particle swarm optimization (PSO) algorithm, the genetic algorithm, and a combination of these two algorithms called genetic-based PSO (gbPSO) are used as optimizers in the proposed Q-learning method. On the basis of a set-union knapsack problem, the efficacy of every method that was used is evaluated |
| Cost and renewable energy-aware dynamic PUE (CRA-DP) (Abbasi-Khazaei and Rezvani [20], 2022) | The study solve the joint cost and scheduling optimization problem using two metaheuristic methods of genetic algorithm (GA) and memetic algorithm (MA) |
| Evolutionary game theory with replicator dynamics (Khoobkar et al. [28], 2022) | This paper proposes a partial offloading method based on replicator dynamics of evolutionary game theory |
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