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Camera calibration is a core task in computer vision. Traditional calibration methods usually achieve approximate solutions due to the complexity of solving nonlinear equations, resulting in reprojection errors. This article proposes a camera calibration optimization algorithm based on the Starling-Inspired Strategy optimization algorithm, which improves calibration accuracy and stability by combining chaotic mapping and sine cosine optimization strategies. First, we constructed a real camera calibration image dataset that includes various calibration scenarios and calculated the initial values of camera calibration parameters based on Zhang’s calibration method. Then, we established a reprojection error model to evaluate the accuracy of the calibration parameters. Finally, we reduced the reprojection error through a hybrid optimization method based on the Steller Jay optimization algorithm. Experimental results show that our algorithm significantly reduces the reprojection error and improves the camera calibration accuracy.

Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achieve differently on medical data, and most of them cannot find those effective features that may fulfill the required accuracy in diagnosing important diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus, which are targeted datasets in this study. To tackle this drawback, an algorithm is needed that can strike a balance between local and global search strategies in selecting effective features from medical datasets. In this paper, a new binary optimizer algorithm named BSMO is proposed. It is based on the newly proposed starling murmuration optimizer (SMO) that has a high ability to solve different complex and engineering problems, and it is expected that BSMO can also effectively find an optimal subset of features. Two distinct approaches are utilized by the BSMO algorithm when searching medical datasets to find effective features. Each dimension in a continuous solution generated by SMO is simply mapped to 0 or 1 using a variable threshold in the second approach, whereas in the first, binary versions of BSMO are developed using several S-shaped and V-shaped transfer functions. The performance of the proposed BSMO was evaluated using four targeted medical datasets, and results were compared with well-known binary metaheuristic algorithms in terms of different metrics, including fitness, accuracy, sensitivity, specificity, precision, and error. Finally, the superiority of the proposed BSMO algorithm was statistically analyzed using Friedman non-parametric test. The statistical and experimental tests proved that the proposed BSMO attains better performance in comparison to the competitive algorithms such as ACO, BBA, bGWO, and BWOA for selecting effective features from the medical datasets targeted in this study.

In this article, a novel population selection method, fitness distance balance (FDB), and predictive candidate (PC) solution generation hybridization with starling murmuration optimizer (SMO), FDBPC-SMO are proposed. In FDBPC-SMO algorithm, FDB selects subpopulations instead of the separating search strategy (SSS) in the original SMO. The separating size determined in SMO is given as input to the FDB, and the FDB generates the subpopulation based on the distances among the populations. The least squares strategy is applied to the population obtained at the end of the SMO, and the estimated population candidates are found and replaced with the worst solution candidates from the original population. By adding qualitative analysis, the effectiveness of the FDBPC-SMO has been examined based on the dimension and iteration. The success of FDBPC-SMO is the selection of more efficient candidate solutions from the previous population at each iteration, thus minimizing the possibility of getting stuck in the local optimum. The performance of FDBPC-SMO has been investigated on CEC2017 and CEC2019 test sets and seven engineering application problems. In addition, Wilcoxon and Friedman statistical tests confirm the convergence and fitness results of the proposed method. Accordingly, comparing to conventional and improved methods, it is clear that the convergence ability of FDBPC-SMO is superior.

This paper introduces a novel murmuration-flight-based dispersive optimization algorithm (MDO) inspired by the natural phenomena of starlings’ murmuration, flight patterns of migrating birds, and dispersive migration. To the best of our knowledge, the proposed algorithm is the first of its kind to utilize Lévy flights to initialize the first population of solutions, thereby ensuring better exploration of the search space from the starting point of the optimization process. Additionally, the starling murmuration leads to better local and global search ability. Captain selection and dispersive migration give the proposed algorithm greater exploitation power. It has few tunable parameters and can be easily applied to various problem domains. Extensive tests and experiments show that the MDO delivers promising and competitive results over other algorithms, and its applicability is also checked statistically by performing a significance test. One of the most complex problems in health IoTs is how to preserve sensitive and personal patient data while addressing the main concerns of data integrity and security in modern health information and telemedicine systems. Hence, the MDO is applied to solve the optimum key-based image encryption problem to showcase its usefulness in real-world applications. Simplicity, efficiency, and better adaptability make the proposed method a strong contender for solving complex optimization problems.

Switched Reluctance Motor (SRM) is an electrical motor which operates by a reluctance torque that has stator and rotor salient poles. It also has a simple configuration and reasonable power electronics necessity for both AC and DC machines within adjustable-speed drives. The problem working on it is, SRM with doubly salient poles introduce a torque ripple in its output which leads to deteriorated performance on speed control. In this proposed work, torque ripple reduction and speed control of SRM is implemented, utilizing an asymmetrical converter with PI-PWM controller with the aid of Bio-inspired algorithms. By utilizing the best value of flux, torque ripple and integral square error of speed and settling times, controller design is performed. The proposed research work compares three optimization algorithms namely Crow Search Algorithm, an Improved Ant Lion Optimization Algorithm and Modified Chaotic Starling Particle Swarm technique to control the speed and torque reduction of SRM in aerospace applications. To obtain the optimal parameter values, optimization techniques are introduced and the optimized controller results are compared with other conventional controller results. In normal operation, the engine operates with two phases simultaneously, but it is modelled to meet the load specification, which is single and double phase faulty. Detection of fault gives a great outcome in control of an electrical drive system. Reduction of faulty operation time leads to better motor lifetime that is obtained by early detection of a fault. This paper presents how the five-phase switched reluctance motor is designed to meet the needs of flap actuators in mid-sized aircraft. Electrical systems are modelled to provide the right functionality at the right time in advanced aircraft. However, requirement of power for the landing gear and secondary flight control only a short duration. The system is powered on and off as required, thus preserving power. Control performance and fault analysis were verified by the results of the MATLAB simulation. Experiments have been carried out more effectively to compare the results obtained with those of simulations.

For the sake of accomplishing the rapidity, safety and consistency of obstacle avoidance for a large-scale unmanned aerial vehicle (UAV) swarm in a dynamic and unknown 3D environment, this paper proposes a flocking control algorithm that mimics the behavior of starlings. By analyzing the orderly and rapid obstacle avoidance behavior of a starling flock, a motion model inspired by a flock of starlings is built, which contains three kinds of motion patterns, including the collective pattern, evasion pattern and local-following pattern. Then, the behavior patterns of the flock of starlings are mapped on a fixed-wing UAV swarm to improve the ability of obstacle avoidance. The key contribution of this paper is collective and collision-free motion planning for UAV swarms in unknown 3D environments with dynamic obstacles. Numerous simulations are conducted in different scenarios and the results demonstrate that the proposed algorithm improves the speed, order and safety of the UAV swarm when avoiding obstacles.

Installing and developing a sophisticated control system to optimize left ventricular assist device (LVAD) pump speed to meet changes in metabolic demand is essential for advancing LVAD technology. This paper aims to design and implement a physiological control method for LVAD pumps to provide optimal cardiac output. The method is designed to adjust the pump speed by regulating the pump flow based on a predefined set point (operating point). The Frank–Starling mechanism technique was adopted to control the set point within a safe operating zone (green square), and it mimics the physiological demand of the patient. This zone is predefined by preload control lines, which are known as preload lines. A proportional–integral (PI) controller was utilized to control the operating point within safe limits to prevent suction or overperfusion. In addition, a PI type 1 fuzzy logic controller was designed and implemented to drive the LVAD pump. To evaluate the design method, rest, moderate, and exercise scenarios of heart failure (HF) were simulated by varying the hemodynamic parameters in one cardiac cycle. This evaluation was conducted using a lumped parameter model of the cardiovascular system (CVS). The results demonstrated that the proposed control method efficiently drives an LVAD pump under accepted clinical conditions. In both scenarios, the left ventricle pressure recorded 112 mmHg for rest and 55 mmHg for exercise, and the systematic flow recorded 5.5 L/min for rest and 1.75 L/min for exercise.

Purpose - The purpose of this paper is to apply a hybrid algorithm based on the combination of two heuristics inspired by artificial life to the solution of optimization problems.Design methodology approach - The flock-of-starlings optimization (FSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the BCA has been used to refine the FSO-found solutions, thanks to its better performances in local search.Findings - A good solution of the 8-th parameters version of the TEAM problem 22 is obtained by using a maximum 200 FSO steps combined with 20 BCA steps. Tests on an analytical function are presented in order to compare FSO, PSO and FSO+BCA algorithms.Practical implications - The development of an efficient method for the solution of optimization problems, exploiting the different characteristic of the two heuristic approaches.Originality value - The paper shows the combination and the interaction of stochastic methods having different exploration properties, which allows new algorithms able to produce effective solutions of multimodal optimization problems, with an acceptable computational cost, to be defined.

Purpose – This paper aims the application of a novel hybrid algorithm, called MeTEO, based on the combination of three heuristics inspired by artificial life to the optimization of electrodes voltages of multistage depressed collector. Design/methodology/approach – The flock-of-starlings optimization (FSO), the particle swarm optimization (PSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the PSO+BCA has been used to refine the FSO-found solutions, exploiting their better performances in local search. Findings – The optimization of the voltage of the electrodes of multistage depressed collector are efficiently handled with a moderate computational effort. Practical implication – The development of an efficient method for the solution of a complicated electromagnetic optimization problem, exploiting the different characteristic of different approaches based on evolutionary computation algorithm. Originality/value – The paper shows that the combination of stochastic methods having different exploration properties with appositely developed FE electromagnetic simulator allows us to produce effective solutions of multimodal electromagnetic optimization problems, with an acceptable computational cost.