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Meta-heuristic algorithms have the advantages of resilience, global optimization capacity, and coding flexibility, making them helpful in tackling difficult optimization issues. The enhanced wind-driven optimization (CHTWDO) that was proposed in this paper coupled the chaotic map approach and the hyperbolic tangent with the T-distribution mutation method. The initial air particles are evenly distributed in the system space through a tent mapping strategy. Meanwhile, the variation probability of the hyperbolic tangent model and the T-distribution variation method are used to improve the comprehensive performance of the algorithm. In this way, the global search accuracy and the ability of avoiding the extreme value of the algorithm can be taken into account. Combining the three strategies, CHTWDO had higher global search accuracy and a stronger ability to jump out of local extremum. Comparing with the eight meta-heuristic algorithms (including WDO) and the single strategy improved WDO on 24 test functions, the experimental results show that CHTWDO with two improved strategies has better convergence precision and faster convergence speed. Statistical tests such as Friedman's and Wilcoxon's rank-sum tests are used to determine significant differences between these comparison algorithms. Finally, CHTWDO also obtains the best results on four classical optimization problems in engineering applications, which verifies its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

To improve soldiers' combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to RRT ∗ , RRT ∗ smart, and information RRT ∗ , the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm's motion costs. [ABSTRACT FROM AUTHOR]

Functions in nonlinear programing optimization are sometimes not easily differentiable. When such kinds of functions are present in the optimization problem, then the process of obtaining the solution can sometimes become quite lengthy. Traditional and classical methods cannot be used to solve such optimization problems because such methods assume the functions to be differentiable. Smoothing of nondifferentiable functions is thus required to tackle such difficulties. In this study, we introduce the concept of constructing a technique to smooth such nondifferentiable functions. We begin with the smoothing of the penalty function. On the basis of it, we come up with an algorithm to find the best way to solve an optimization problem with inequality constraints. We also talk about how to figure out the error for a certain smooth penalty function. We also provide numerical examples to demonstrate that the suggested approach is practical and usable. [ABSTRACT FROM AUTHOR]

An automated flower thinning system, when combined with machine vision, has the potential to reduce the labor force, improve efficiency, and lower costs. This combination represents the future of agricultural machinery development. The primary objective of automatic flower thinning is to determine the flowering density of fruit trees under natural light conditions. In this study, we introduce a flower recognition algorithm that uses pixel values as an independent variable to recognize flower categories by constructing a nonlinear regression model. Initially, the RGB pixel values of elements in the training set are extracted. Similar pixel values are clustered together to reduce the amount of computation, and representative elements are selected to construct a nonlinear classification function, known as the regression function. The coefficients in the classifier are determined by transforming the problem into an unconstrained optimization problem using the least square method. The optimal solution is then found as the coefficient value in the classifier. The classification function calculates the function value of the RGB pixel value for each input entity to determine whether it belongs to the flower entity. Finally, the developed algorithm is used to classify the flower graphic elements of the measured pictures, and the efficiency of the algorithm is verified. [ABSTRACT FROM AUTHOR]

The concept of a "Smart City" emphasizes the need to employ information and communication technologies to strengthen the quality, connectivity, and efficiency of various municipal services. Cloud computing and the Internet of Things are shaping future tech. Both ideas greatly impact smart city application and solution development. Cloud computing is amazing at managing and storing remote service access. Several companies have switched to cloud leasing to reduce local resource burden. Due to the intricacy and flexibility of cloud-maintained services, selecting jobs that best suit client needs should be optimized. Quality of service criteria for each cloud service are the best tools for choosing and optimizing cloud carriers. Genetic algorithms (GAs) and ant colony optimization (ACO) are combined to make cloud computing. It is discovered that the recommended ACO + GA obtains an accuracy of 82% when compared to existing methods of energy- and reliability-aware multiobjective optimization method and the hybrid cuckoo particles swarm, artificial bee colony optimization (CPS + ABCO) where accuracy is 68% and 75%, respectively. [ABSTRACT FROM AUTHOR]