A parallel hybrid local search algorithm for the container loading problem

In this contribution, a parallel hybrid local search algorithm for the three‐dimensional container loading problem (CLP) is proposed. First a simulated annealing method for the CLP is developed, which is then combined with an existing tabu search algorithm to form a hybrid metaheuristic. Finally, parallel versions are introduced for these algorithms. The emphasis is on CLP instances with a weakly heterogeneous load. Numerical tests based on the well‐known 700 test instances from Bischoff and Ratcliff are performed, and the outcome is compared with methods from other authors. The results show a high solution quality obtained with reasonable computing time.     

The importance of nature-inspired metaheuristic algorithms in the data routing and path finding problem in the internet of things

Over the last decade, the Internet of Things (IoT) has become ever more popular, as is evident from its role in changing the human lifestyle and conferring remarkable privileges for them. It has a significant presence in various crucial areas, including smart cities, smart factories, manufacturing, transportation, and healthcare. Massive amounts of data generated by IoT devices have the potential to endanger the lifetime of nodes in IoT-based networks due to increased communication power consumption. It has become crucial to propose solutions for network-based issues, such as quality of service, security, network heterogeneity, congestion avoidance, reliable routing, and energy conservation. To address the mentioned problems, routing protocols play a critical role in data transmission among heterogeneous items. In such environments, routing refers to constructing routes between mobile nodes. Since identifying optimal routes among IoT nodes and establishing an effective routing protocol in an IoT network are an NP-hard issue, employing metaheuristic algorithms may be a viable solution to overcome this problem. Various IoT routing protocols based on metaheuristic algorithms have been presented in recent years, but there is still a lack of systematic study for reviewing the existing works. The current study emphasizes the impact of metaheuristic algorithms in the IoT routing problem, discusses the optimization models, presents a comprehensive comparison of protocols based on critical parameters, and eventually suggests some hints for future studies.     

Grid quorum‐based spatial coverage in mobile wireless sensor networks using nature‐inspired firefly algorithm

Optimization and design of mobile wireless sensor networks (MWSNs) must assure adequate spatial coverage of the site. The spatial coverage optimization aims to enrich discoverability of MWSN by specifying mobile sensors geographical locations in order to maximize their coverage. In this paper, an enhanced metaheuristic algorithm called “firefly algorithm with crossover and detection phases” is introduced for optimizing the area coverage percentage of MWSN. The proposed algorithm is tested on many datasets with different criterions and compared with other algorithms including differential evolution, whale optimization algorithm, and flower pollination algorithm. The experimental results are analysed with one‐way ANOVA test. In addition, the proposed algorithm is compared with particle swarm optimization, and the results are analysed with Wilcoxon signed‐rank test. The overall analysis results prove the prosperity and efficient exploration of the proposed algorithm.     

Fuzzy-Based Secure Clustering with Routing Technique for VANETs

Due to the advanced developments in communication technologies, Internet of vehicles and vehicular adhoc networks (VANET) offers numerous opportunities for effectively managing transportation problems. On the other, the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way. To resolve this issue, clustering and routing techniques can be designed using computational intelligence approaches. With this motivation, this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing (T2FSC-MOR) technique for secure communication in VANET. The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET. The proposed model involves T2FSC technique for the selection of CHs and construction of clusters. The T2FSC technique uses different parameters namely traveling speed (TS), link quality (LQ), trust factor (TF), inter-vehicle distance (IVD), and neighboring node count (NCC). The inclusion of trust factor helps to select the proper cluster heads (CHs) for secure data dissemination process. Moreover, trust aware seagull optimization based routing (TASGOR) approach was derived for the optimal selection of routes in VANET. In order to validate the enhanced performance of proposed technique, the set of simulations take place and the outcomes are examined interms of different measures. The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%.     

Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm

Prediction of solar power involves the knowledge of the sun , atmosphere and other parameters, and the scattering processes and the specifications of a solar energy plant that employs the sun's energy to generate solar power . This prediction result is essential for an efficient use of the solar power plant, the management of the electricity grid, and solar energy trading. However, because of nonlinear and nonstationary behavior of solar power time series, an efficient forecasting model is needed to predict it. Accordingly, in this paper, we propose a new forecast approach based on combination of a neural network with a metaheuristic algorithm as the hybrid forecasting engine. The metaheuristic algorithm optimizes the free parameters of the neural network. This approach also includes a 2‐stage feature selection filter based on the information‐theoretic criteria of mutual information and interaction gain, which filters out the ineffective input features. To demonstrate the effectiveness of the proposed forecast approach, it is implemented on a real‐world engineering test case. Obtained results illustrate the superiority of the proposed approach in comparison with other prediction methods.     

混合花粉算法求解全局优化问题

元启发式算法可以用作寻找近似最优解的有效工具,因此,对元启发式算法进行改进,提高算法性能是有必要的。本文介绍花粉算法(Flower Pollination Algorithm, FPA)的增强变体,将花粉算法与极值优化算法(Extremal Optimization, EO)混合形成FPA-EO算法。FPA-EO算法综合利用了FPA的全局搜索能力和EO的局部搜索能力,并将其应用于11个基准测试函数来测试新算法。同时将该算法与其他4种著名优化算法（标准花粉算法(FPA)、蝙蝠算法(BAT)、萤火虫算法(FA)、模拟退火算法(SA)）进行比较。综合结果表明,本文算法能够找到比其他4种算法更精确的解。