Chengcheng Chen

Bio: Chengcheng Chen is an academic researcher from Jilin University. The author has contributed to research in topics: Engineering optimization & Swarm intelligence. The author has an hindex of 4, co-authored 7 publications receiving 93 citations. Previous affiliations of Chengcheng Chen include Chinese Ministry of Education.

Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

Abstract: The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning

Abstract: Accurate diagnosis of corn crop diseases is a complex challenge faced by farmers during the growth and production stages of corn. In order to address this problem, this paper proposes a method based on K-means clustering and an improved deep learning model for accurately diagnosing three common diseases of corn leaves: gray spot, leaf spot, and rust. First, to diagnose three diseases, use the K-means algorithm to cluster sample images and then feed them into the improved deep learning model. This paper investigates the impact of various k values (2, 4, 8, 16, 32, and 64) and models (VGG-16, ResNet18, Inception v3, VGG-19, and the improved deep learning model) on corn disease diagnosis. The experiment results indicate that the method has the most significant identification effect on 32-means samples, and the diagnostic recall of leaf spot, rust, and gray spot disease is 89.24 %, 100 %, and 90.95 %, respectively. Similarly, VGG-16 and ResNet18 also achieve the best diagnostic results on 32-means samples, and their average diagnostic accuracy is 84.42% and 83.75%. In addition, Inception v3 (83.05%) and VGG-19 (82.63%) perform best on the 64-means samples. For the three corn diseases, the approach cited in this paper has an average diagnostic accuracy of 93%. It has a more significant diagnostic effect than the other four approaches and can be applied to the agricultural field to protect crops.

An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme

Abstract: In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems in engineering and data science. When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. However, when the single modal function is processed, the convergence speed of the algorithm is too slow to converge quickly to the optimum during optimization. In this paper, the elite-based dominance scheme of another well-established method, grey wolf optimizer (GWO), is introduced into the CLPSO, and the grey wolf local enhanced comprehensive learning PSO algorithm (GCLPSO) is proposed. Thanks to the exploitative trends of the GWO, the algorithm improves the local search capacity of the CLPSO. The new variant is compared with 15 representative and advanced algorithms on IEEE CEC2017 benchmarks. Experimental outcomes have shown that the improved algorithm outperforms other comparison competitors when coping with four different kinds of functions. Moreover, the algorithm is favorably utilized in feature selection and three constrained engineering construction problems. Simulations have shown that the GCLPSO is capable of effectively dealing with constrained problems and solves the problems encountered in actual production.

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

Abstract: Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.

Protein nanoparticles in drug delivery: animal protein, plant proteins and protein cages, albumin nanoparticles

Abstract: In this article, we will describe the properties of albumin and its biological functions, types of sources that can be used to produce albumin nanoparticles, methods of producing albumin nanoparticles, its therapeutic applications and the importance of albumin nanoparticles in the production of pharmaceutical formulations. In view of the increasing use of Abraxane and its approval for use in the treatment of several types of cancer and during the final stages of clinical trials for other cancers, to evaluate it and compare its effectiveness with conventional non formulations of chemotherapy Paclitaxel is paid. In this article, we will examine the role and importance of animal proteins in Nano medicine and the various benefits of these biomolecules for the preparation of drug delivery carriers and the characteristics of plant protein Nano carriers and protein Nano cages and their potentials in diagnosis and treatment. Finally, the advantages and disadvantages of protein nanoparticles are mentioned, as well as the methods of production of albumin nanoparticles, its therapeutic applications and the importance of albumin nanoparticles in the production of pharmaceutical formulations.

Magnetic Nanoparticles in Targeted Drug Delivery: a Review

Abstract: Magnetic nanoparticles are one of the most important and widely used types of nanomaterials, whose unique properties make them special compared to other nanostructures. These particles can be used in various fields. But their role in biomedicine, especially in the field of drug delivery, is significant because their inherent magnetism facilitates many tasks, including targeting, which is very important and necessary in drug delivery. In the present article, an attempt has been made to give general information about magnetic nanoparticles and the properties of particles in biomedical applications. In the following, special attention has been paid to the properties of these particles in drug delivery and their various applications have been studied. The importance of coating magnetic nanoparticles has also been mentioned as a basic requirement for medical applications. In the following, the method of drug loading in magnetic nanoparticles, the entry of particles into the body, targeting, and drug release are discussed, and finally, a brief discussion is presented regarding the pharmacokinetics of drugs and their toxicity in the body.

Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem

Abstract: In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to solve complex optimization problems. However, these algorithms suffer from the shortcoming that multiple hyperparameters need to be set carefully. Therefore, to solve the problem, the kernel search optimization (KSO) algorithm inspired by the kernel method has been proposed. KSO can simplify the optimization process by transforming the optimization process of nonlinear function into the linear optimization process. Despite its advantage, the original KSO requires a large amount of computation, and has no powerful exploitation search, resulting in its inability to obtain more accurate results. In the present study, a local search of the hill-climbing algorithm is adopted, and the calculation of the kernel parameter is simplified to improve the original KSO. In an experiment using 50 benchmark functions, the new algorithm outperformed KSO and some well-known algorithms in accuracy and running time. Moreover, when applied in the real-world economic emission dispatch problem, the improved algorithm achieved a better performance than other algorithms compared. An online repository will support this research at https://aliasgharheidari.com .