# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 2 Related Work
### Meta-Heuristic Algorithms
#### 2.3.4 Other Evolutionary Meta-Heuristic Algorithms  
The authors of [5] propose a bio-inspired solution based on the Bees Life algorithm to solve the scheduling problem in the FC environment. This solution is based on the distribution of a set of tasks between all FDs. Analysis of the execution time of the CPU and allocated memory by FDs after simulation showed that this method performs better than PSO and GA.  
In [34], the authors introduce a swarm-based meta-heuristics optimization method called Krill Herd (KH) and then accelerate its global convergence speed by a chaotic theory called CKH. The objective function in the KH is based on the minimum distance between the food position and the location of the krill [35]. To evaluate the results of the proposed method [34], various problems have been used. The results show that the performance of CKH with an appropriate chaotic map is better than that of other state-of-the-art meta-heuristic methods.  
The researchers in [36] propose a quantum-inspired binary grey wolf optimizer (QIBGWO) method for the unit commitment (UC) problem. The proposed method integrates the concepts of quantum computing with the BGWO to improve the hunting process of the wolf pack. The results show the effectiveness and improvement of the proposed method based on binary and quantum computations for the UC problem.  
In [37], the cuckoo search and flower pollination algorithm as two meta-heuristic methods are used to solve different optimization problems. The results show that all platforms have similar performance on average. Additionally, if the algorithm is based on a balanced search and combines exploration with exploitation, it guarantees high-quality solutions.  
In [38], the authors proposed an innovative algorithm for the group decision-making (GDM) problem with triangular neutrosophic additive reciprocal matrices. To evaluate GDM, they used triangular fuzzy numbers because of incompatibility with preferential relationships. By analyzing previous studies, they concluded that fuzzy preference relations have some drawbacks owing.  
The improved whale optimization algorithm (IWOA) is presented in [39] for solving the 0-1 knapsack problems on a variety of scales. In this method, a penalty function is added to evaluate the performance of the solutions. The authors gained the trade-off between diversification and intensification through using two strategies: local strategy strategies (LSS)and Levy flight walks. The results show better performance for the proposed algorithm than other state-of-art algorithms.  
In [40], an evolutionary multi-objective optimization algorithm is presented for high-performance computing in cyber-physical social systems (CPSS). To examine the feasibility of their proposed model, a floor planning case study is used. The B*-tree algorithm and the multi-step simulated annealing (MSA) algorithm are used to solve this problem. The performance of the proposed method was 74.44%. The researchers in [41] also present a multi-objective method based on a hybrid hitchcock bird algorithm and fuzzy signature (MOHFHB) for task scheduling in cloud computing. They improved makespan and resource utilization compared to both the Moth Search Algorithm with Enhanced Multi-Verse Optimizer and the Fuzzy Modified Particle Swarm Optimization. The authors of [42] also used Opposition-based learning to optimize PSO (OPSO) for task scheduling in a cloud computing environment. They improved the convergence of standard PSO, energy consumption, and makespan.