Drone logistics can play an important role in logistics at the end of the supply chain and special environmental logistics. At present, drone logistics is in the initial development stage, and the location of drone logistics hubs is an important issue in the optimization of logistics systems. This paper implements a compact cuckoo search algorithm with mixed uniform sampling technology, and, for the problem of weak search ability of the algorithm, this paper combines the method of recording the key positions of the search process and increasing the number of generated solutions to achieve further improvements, as well as implements the improved compact cuckoo search algorithm. Then, this paper uses 28 test functions to verify the algorithm. Aiming at the problem of the location of drone logistics hubs in remote areas or rural areas, this paper establishes a simple model that considers the traffic around the village, the size of the village, and other factors. It is suitable for selecting the location of the logistics hub in advance, reducing the cost of drone logistics, and accelerating the large-scale application of drone logistics. This paper uses the proposed algorithm for testing, and the test results indicate that the proposed algorithm has strong competitiveness in the proposed model.

This work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

Cloud computing seems to be the result of advancements in distributed computing, parallel computing, and network computing. The management and allocation of cloud resources have emerged as a central research direction. An intelligent resource allocation system can significantly minimize the costs and wasting of resources. In this paper, we present a task scheduling technique based on the advanced Phasmatodea Population Evolution (APPE) algorithm in a heterogeneous cloud environment. The algorithm accelerates up the time taken for finding solutions by improving the convergent evolution of the nearest optimal solutions. It then adds a restart strategy to prevent the algorithm from entering local optimization and balance its exploration and development capabilities. Furthermore, the evaluation function is meant to find the best solutions by considering the makespan, resource cost, and load balancing degree. The results of the APPE algorithm being tested on 30 benchmark functions show that it outperforms similar algorithms. Simultaneously, the algorithm solves the task scheduling problem in the cloud computing environment. This method has a faster convergence time and greater resource usage when compared to other algorithms.

Resource-constrained devices in open environments face diverse optimization problems, so general-purpose optimization capabilities become important but are currently lacking. Our algorithm framework aims to fill this gap and better understand the issues when implementing general optimization in hardware, especially using field-programmable gate array. Therefore, the challenge is to design an algorithm framework that can handle different optimization problems with fewer hardware resources while maximizing solution performance. Based on the Zynq XC7Z020-2CLG400I device and the compact differential evolution (cDE) algorithm, this article describes the unified software and hardware architecture, the cDE algorithm that incorporates ensemble mutation and crossover strategy as well as uniform mutation operation (cDE-emc-um), providing an efficient and low-resource algorithm framework while maintaining generality. This article also theoretically analyzes the global convergence and low computational complexity of the cDE-emc-um and tests the general optimization capabilities of our algorithm framework through two optimization problems.

The construction and governance of smart cities require the collaboration of different systems and different regions. How to realize the monitoring of abnormal hot spots through the collaboration of subsystems with limited resources is related to the stability and efficiency of the city. This work constructs a hot data processing framework for drones and 5G edge computing infrastructure, as well as an Ensemble Multi-Objective Cooperative Learning method to process three different types of hot data. The data collection phase combines set operations with the 0-1 multi-knapsack model, and the cooperative learning phase realizes the degree of cooperation control while retaining the ability of independent optimization of the subsystem. Finally, the advantages of the framework are verified by hot data coverage and collaborative processing efficiency, resource use cost, and balance.