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In this paper, we optimized the performance of a 2.4 GHz variable gain low-noise amplifier for WLAN applications which provides high dynamic range with relatively low power consumption. First, the differential evolution algorithm was used to optimize the width of input transistors, then the tunable on-chip switching stage method was applied to control the amplifier gain when the input signal increases. The optimization was performed in terms of gain, noise figure (NF), IIP3 and power dissipation. The LNA has achieved a variable gain from 16.55 to 20.45 dB with excellent NF between 1.63 and 1.74 dB. Furthermore, the proposed circuit achieves a third order input intercept point of 6.6 dBm. It consumes only 10 mW from a 1.5 V supply.

In data acquisition scenario of edge computing, the optimization of UAV (Unmanned Aerial Vehicle) deployment is of great significance for making use of resources of UAV. We establish an optimization model of UAV cluster deployment in the edge data acquisition system. The model takes the height of UAV as the solving variable, which is more in line with the realistic characteristics. DEVIPSK-SA-FWA is proposed according to the characteristics of this model. The algorithm uses a novel coding mechanism, and uses K-Means to accelerate the convergence process of the algorithm. A variety of differential evolution mutation operators are used to form a self-adaptive strategy pool mechanism to carry out variable scale variation of population, which complete the global search well. Then fireworks algorithm searches the population locally after each round of global search. In our algorithm, global search and local search are well balanced and local optimal is effectively escaped. Finally, experimental results indicate that DEVIPSK-SA-FWA is capable of solving the model with good results, and the superiority of DEVIPSK-SA-FWA is verified through the Wilcoxon rank sum test method. In the best case, the proposed algorithm reduces energy consumption of edge data acquisition system by 32.87$\%$.

Recently, an effective method called return maps is proposed for the parameter estimation of chaotic systems. However, high time-consumption limits practical applications. In this paper, we focus on this problem, and an improved return maps method is proposed. It combines the differential evolution algorithm with the return maps method, and simplifies the calculation process of Euclidean distance. Numerical simulations are carried out on two fractional-order chaotic systems, and the other five methods are used as the comparison. Results show that the improved method can accurately estimate the parameters of chaotic systems, and it saves much time than does the classical return maps method. Furthermore, the proposed method also exhibits good anti-noise performance.

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

This study is aimed to develop a parameter estimation procedure for a stochastic model of gastric emptying of pellets under fasting conditions of healthy subjects that represent the irregular decrements of gastric contents after administration of pellets. An algorithm for the identification of experimental subjects into subgroups based on their gastric emptying rates has been proposed. The parameter estimation procedure was performed with observational data of gastric emptying profiles of 19 subjects from the existing literature. The identification algorithm was validated through comparison against the original results, showing that seven subjects indicated slow gastric emptying and 12 subjects indicated fast emptying. After excluding one subject with gastric stasis, the optimal results obtained from this study classified subjects into three subgroups: two subjects with slow, five subjects with intermediate and 11 subjects with fast gastric emptying. The proposed parameter estimation procedure of the stochastic model is relatively easy to implement and can be used to analyze real patients’ data. The algorithm for identification of the gastric emptying dynamics in experimental subjects could provide useful information for further investigations of long-term energy balance forecasting.

Promotion plays an important role in determining fate of a product. It voices product qualities and persuades potential customers to make purchases. In todays diversified markets every customer has individual needs and preferences. This calls for market segmentation which facilitates companies to adopt customer driven marketing. Product is also promoted using mass promotion to influence larger markets and segments with a spectrum effect. Existing literature in promotion resource allocation optimization evades distinction in two types of promotion strategies while allocating resources and primarily focuses on either of them statically. In this paper, we formulate an optimization model which dynamically allocates differentiated and mass promotional resources in segments to maximize sales under budgetary and minimum market share aspiration constraint on each segment as well as on total market share including repeat purchase behavior. Planning horizon is divided into multi-periods, adoption pattern is studied in each period and then resources are allocated attuned to current market behavior. Dynamic allocation provides a way to track the market potential expansion due to promotion and plan the allocation for the uncaptured potential. The proposed optimization model is NP hard and very challenging to solve. Solution methodology is presented with practical application based on real-time data using differential evolution algorithm.

With the continuous expansion of the city scale and the increasing population, people have put forward higher requirements for the speed, reliability and economy of logistics distribution. Although there have been many achievements in the research on the vehicle optimal scheduling decision model and its solution algorithm, there are some problems such as low solution efficiency, long time consumption, and easy to fall into the local optimal solution. The purpose of this paper is to study the model of vehicle scheduling problem in distribution center based on improved differential evolution algorithm. This paper firstly makes a simple analysis of the vehicle distribution route optimization problem, and points out the main factors that constitute the route optimization problem; then analyzes the principle of the differential evolution algorithm, and on this basis, the differential evolution algorithm is improved. In the experimental part, this paper takes a distribution center as the research object, and proposes the implementation steps of the algorithm of the vehicle scheduling problem based on the improved differential evolution algorithm. And test the running cost of the improved differential evolution algorithm and differential evolution algorithm. Experimental results show that applying the improved differential evolution algorithm to vehicle scheduling problems has certain practicality. When the number of iterations reaches 60, the running cost of the improved differential evolution algorithm is 1,200 yuan, and the running cost of the differential evolution algorithm is 1,300 yuan.

We have presented an approach to automatic document summarization. In the proposed approach, text summarization is modeled as a quadratic integer-programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. To solve the optimization problem we have created a novel differential evolution algorithm. Experimental results on DUC2005 and DUC2007 data sets showed that the proposed approach outperforms the other methods.

In the environment of scientific and technological innovation, the human and material resources that can reach the project threshold are often limited. For enterprise projects that require high R&D and production level to produce rapid emergency resources, they often face the problem of difficult effective allocation of emergency resources, resulting in project delay. Therefore, in the process of high-tech projects, it is necessary to deal with the lack of emergency scientific and technological resources. Therefore, based on the particle swarm optimization algorithm which is easy to be realized by scientific and technological innovation enterprises, this study improves its two shortcomings of easy local optimization and low performance, constructs the CD-PSO project scheduling model, verifies the performance of the improvement points, and tests the performance of the model in the actual project case of enterprise rapid emergency resource scheduling. The results show that the optimal solution of CD-PSO model in the three sample sets is higher than that of D-PSO model in the same group, and the average scheduling duration is the shortest, which is 350 days, and the proportion of reaching the optimal duration is 100%. The operation effect is obviously better than other scheduling models. The CD-PSO project scheduling model designed in this study is easier to implement and can calculate the optimal solution of the first project more efficiently. It not only has excellent performance, but also has a strong popularization foundation.

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on differential evolution (DE) search is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to falling into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with DE algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of DE algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Kapur entropy and Type 2 fuzzy entropy are employed to evaluate the capability of the introduced algorithm. Nine different metaheuristic algorithms with Darwinian modes are also implemented and compared with our method. Experimental results manifest that the proposed method is a powerful tool for multilevel thresholding and the obtained results outperform the DE algorithm and other methods.

How to detect the topological degree (TD) of a function is of vital importance in investigating the existence and the number of zero values in the function, which is a topic of major significance in the theory of nonlinear scientific fields. Usually a sufficient refinement of the boundary of the polyhedron decided by Boult and Sikorski algorithm (BS) is needed as prerequisite when the well known method of Stenger and Kearfott is chosen for computing TD. However two linchpins are indispensable to BS, the parameter δ on the boundary of the polyhedron and an estimation of the Lipschitz constant K of the function, whose computations are analytically difficult. In this paper, through an appropriate scheme that transforms the problems of computing δ and K into searching optimums of two non-differentiable functions, a novel differential evolution algorithm (DE) combined with established techniques is proposed as an alternative method to computing δ and K. Firstly it uses uniform design method to generate the initial population in feasible field so as to have the property of large scale convergence, without better approximation of the unknown parameter as iterative initial point. Secondly, it restrains the normal DE's local convergence limitation virtually through deflection and stretching of objective function. The main advantages of the put algorithm are its simplicity and its ability to work by using function values solely. Finally, details of applying the proposed method into computing δ and K are given, and experimental results on two benchmark problems in contrast to the results reported have demonstrated the promising performance of the proposed algorithm in different scenarios.

Now, the dynamic solution is put forward for the QoS of network service problems, the solution is to integrate the mixture of differential evolutionary algorithm and ant colony algorithm into the QoS routing, and the solution adjusts the local pheromone updating strategy dynamically. The optimal global pheromone improves the global convergence of the algorithm. The simulation results show that DE-ACO algorithm is effective to deal with the problem of routing. At the same time, DE-ACO algorithm compares with the traditional QoS multicast routing algorithm in convergence and convergence speed. DE-ACO algorithm has been greatly improved.

In response to characteristics of differential evolutionary algorithms in solving combinatory problems, this paper investigated different solution encoding and decoding methods and designed feasible evolution operation, designed special crossover and mutation operator to fit in with the specific problem, and initial value is obtained by stochastic topological sorting for permutation-based method. In order to improve the solution quality and the global search capability, local and SA search strategy are integrated to accelerate the convergence of the algorithms. The experimental results are perfect.