Grasshopper Optimization Algorithm

Wireless sensor networks have become extensively applied in various fields with their advance. They may be formed freely and simply in many areas with no infrastructure. Also, they gather information about environmental phenomena for decent efficiency and event analysis and send it to base stations. The absence of infrastructure in such networks, on the other hand, limits the sources; therefore, the nodes are powered by a battery with inadequate energy. As a result, preserving energy in such networks is a critical task. Clustering the nodes and picking the cluster head based on the available transmission factors is an intriguing way for reducing energy consumption in these networks, as the average energy consumption of the nodes is lowered and the network lifespan is increased. By combining the multi-objective grasshopper optimization algorithm and the harmony search, this study provides a novel optimization strategy for wireless sensor network clustering. The cluster head is chosen using the multi-objective grasshopper optimization algorithm, and information is communicated between the cluster head's nodes and the sink node using nearly optimum routing based on the harmony search. The simulation outcomes indicate that when the functionality of the multi-objective grasshopper optimization algorithm and the harmony search are taken into account, the suggested technique outperforms the previous methods in terms of data delivery rate, energy consumption, efficiency, and information packet transmission.

With the accelerated development of Internet finance, electronic funds transfer, and the rapid growth of credit card activity, credit cards play a very important role in every area of ​​life today. There are some risks in this regard that are considered serious threats to both issuers and cardholders. The increasing number of fraudulent credit card transactions forged credit cards and fraudulent use of expired credit cards have led to increased losses. Therefore, finding fraud detection techniques accurately and quickly has become an important topic in current investigations. In this study, after normalizing and reducing the dimensionality of the data using the PCA algorithm, we used the modified perceptron neural network and the grasshopper algorithm to classify the data. In this study, we use the grasshopper algorithm to adjust the weights and biases of neural networks. In the end, we were able to achieve 99.20% accuracy.

In this paper, multilayer perceptron neural network (MLP-NN) training is used by the grasshopper optimization algorithm with the tuning of control parameters using a fuzzy system for the big data sonar classification problem. With proper tuning of these parameters, the two stages of exploration and exploitation are balanced, and the boundary between them is determined correctly. Therefore, the algorithm does not get stuck in the local optimization, and the degree of convergence increases. So the main aim is to get a set of real sonar data and then classify real sonar targets from unrealistic targets, including noise, clutter, and reverberation, using GOA-trained MLP-NN developed by the fuzzy system. To have accurate comparisons and prove the GOA performance developed with fuzzy logic (called FGOA), nine benchmark algorithms GOA, GA, PSO, GSA, GWO, BBO, PBIL, ES, ACO, and the standard backpropagation (BP) algorithm were used. The measured criteria are concurrency speed, ability to avoid local optimization, and accuracy. The results show that FGOA has the best performance for training datasets and generalized datasets with 96.43% and 92.03% accuracy, respectively.

Most humanitarian relief items' investigations try to satisfy demands in disaster areas in an appropriate time and reduce the rate of causality. Time is an essential element in humanitarian relief items; the quietest response time, the more rescued people. Reducing response time with high reliability is the main objective of this research. In our investigation, monitoring the route’s situation after occurrence disaster with drones and motorcycles is planned for collecting information about routes and demand points in the first stage. The collected information is analyzed by the disaster management to determine the probability of each scenario. By evaluating collected data, the route repair groups are sent to increase the route’s reliability. In the final step, the relief items operation allocates the relief items to demand points. All in all, this research tries to present a practical model and real situation to survive more people after occurrence disaster. An exact solver solves the evolutionary model in small and medium scales; the developed model in big scale is solved by Grasshopper Optimization Algorithm (GOA), and then results are evaluated. The evaluation results indicate the positive effect of valid initial information on the humanitarian supply chain’s performance.