Flight Controller Optimization of Unmanned Aerial Vehicles using a Particle Swarm Algorithm

A novel approach to multiobjective particle swarm optimization (MOPSO) technique for solving optimal power flow (OPF) problem is proposed in this paper. The new MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. A clustering algorithm to manage the size of the Pareto-optimal set is imposed. The proposed MOPSO technique has been implemented to solve the OPF problem with competing and non-commensurable cost and voltage stability enhancement objectives. The optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed MOPSO technique to generate a set of well-distributed Pareto-optimal solutions in one single run.

As the world is progressing towards faster and more efficient computing techniques, cloud computing has emerged as an efficient and cheaper solution to such increasing and demanding requirements. Cloud computing is a computing model which facilitates not only the end-users but also organizational and other enterprise users with high availability of resources on demand basis. This involves the use of scientific workflows that require large amount of data processing, which can be costly and time-consuming if not properly scheduled in cloud environment. Various scheduling strategies have been developed, which include swarm-based optimization approaches as well. Due to the presence of multiple and conflicting requirements of users, multi-objective optimization techniques have become popular for workflow scheduling. This paper deals with cat swarm-based multi-objective optimization approach to schedule workflows in a cloud computing environment. The objectives considered are minimization of cost, makespan and CPU idle time. Proposed technique gives improved performance, compared with multi-objective particle swarm optimization (MOPSO) technique.

In this brief, we deal with the generation of the Pareto front for multi-objective analog circuit sizing optimization. The main idea of the proposed work consists of using metamodels of the considered performances to generate the set of nondominated solutions. These models serve as evaluators. They offer several advantages, mainly their high precision for reproducing the real behavior of the considered performances, and their very rapid evaluation, when compared to their counterpart, i.e. the in-loop-based sizing technique. Multiobjective particle swarm optimization (MOPSO) technique is used as the optimization metaheuristic. The proposed approach is first applied to some mathematical test functions, then, we consider the optimal sizing of MOS current conveyors, where two conflicting objectives are considered, namely maximizing the current transfer cutoff frequency (fci) and minimizing the parasitic X-port resistance (Rx). Obtained results are compared to those reached using the conventional in-loop optimization technique. Hspice simulations, using AMS 0.35μm CMOS technology, are given to highlight these reached results.

Reliability-based performance simulation for optimized pavement maintenance

As far as continuous supply and reliable power delivery to the distribution system customers are concerned, finding a practical and cost-effective method for locating faults after their occurrence is of high importance. In this study, a method is proposed to determine the optimal number and location of power quality monitors (PQMs) to make the distribution network fault-observable, that means, to be able to locate faults as precisely as possible. Moreover, as placement of PQMs is highly dependent on the network topology, the proposed method considers the most probable configurations of the network for optimization. The error of measuring equipment and its effect on number of PQMs is also taken into consideration. The defined objective functions of this study aim to minimize the cost of installing PQMs while minimizing the number of blind-pairs and maximizing the fault-observability level of the network. These objective functions are optimized using Multi-Objective Particle Swarm Optimization (MOPSO) technique. Additionally, to have a better economic evaluation, two scenarios are defined based on the accuracy class of monitoring equipment. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 123-bus distribution test system.

This paper addresses a multi-objective approach for simultaneous dynamic expansion planning of conventional sub-transmission grid and Regional Virtual Power Plant (RVPP). In such a feed-in tariff electricity market, it will assist RVVP’s stakeholders in deciding whether or not they should invest in new equipment’s installation. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm as a heuristic optimization method is proposed to eliminate the conventional centralized planning, which has led to competition between Regional Electric Company (REC) and RVPP for power delivery in sub-transmission system. Two objective functions are considered for simultaneous expansion planning of these two systems. The first one takes the minimum cost of the REC, as the sub-transmission grid operator, into account while the other one considers profit maximization for RVPP. To achieve the goals, MOPSO algorithm is employed to find the best expansion of REC and best location and capacity of RVPP’s resources. Having solved the proposed multi-objective optimization problem, a Pareto front is determined to show the trade-off between REC and RVPPs' contributions in joint optimal expansion planning of conventional sub-transmission grid and the RVPP. To demonstrate the applicability and effectiveness of the proposed approach, a realistic sub-transmission system in Guilan Province, Iran is used as a test system, and the results are evaluated accordingly.

The Vertical bearing offset is vital for the safe and efficient operation of marine shafting systems. The bearing offset is made relative to the shaft and must be tested for various operational conditions to prevent overheating, whipping, vibration, and wear that can lead to costly repairs and downtime. The offset of each bearing is achieved by minute adjustment of the bearing pedestal performed during dry-docking. However, the hull load variation can cause the preset bearing offsets to be inadequate resulting in potential bearing damage. Classification Society of ABS and DNV specifies the limit of bearing reaction force, shaft slope, and bending moment to ensure safe operation. These values are usually determined using a finite element model or experimentally using costly strain gauges. A more cost-effective solution is using the stiffness matrix of the bearing shaft system to derive the influence coefficients for the bearing reaction forces (BIC), shaft slope (SIC), and bending moment (MIC). This approach is validated with the existing model of a marine shafting system of a 50 DWT oil tanker that consists of one tail and intermediate shaft supported by stern tube bearing (STB), and intermediate bearing (IB) respectively along with six bearings at the engine shaft using the finite element method. Besides, the study applies the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimize the vertical position of bearings. It uses BIC, Slope SIC, and MIC to construct the objective functions and find a Pareto-optimal solution that satisfies the requirements of ABS and DNV in terms of bearing reaction forces, bending moments, and slope at the main stern tube and intermediate bearing. The optimal bearing offsets show improved shaft bending state for the operating condition under evaluation and reduced the slope at the STB by 34%, indicating its effectiveness in solving optimization problems.

Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID’s good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.

In this chapter, a new multi-objective blended particle swarm optimization (MOBPSO) technique is proposed for the selection of significant and informative genes from the cancer datasets. As the basic optimization algorithm suffers from the local trapping, a blended Laplacian operator is integrated with it to overcome the drawback. The concept is also implemented for differential evolution, artificial bee colony, genetic algorithm and subsequently multi-objective blended differential evolution (MOBDE), multi-objective blended artificial bee colony (MOBABC) and multi-objective blended genetic algorithm (MOBGA) are proposed to extract the relevant genes from the cancer datasets. Proposed methodology utilizes two objective functions to sort out the genes which are differentially expressed from class to class as well as provides good results for the classification of disease. Experimental result reveals that the proposed methodology very efficiently selects differential and biologically relevant genes which are effective for the classification of disease which in turn offers more useful information about the gene–disease association.

Hydrogen–Fuel Cell (HFC) systems have become one of the most promising zero-carbon shipping solutions, with high energy-conversion efficiency, silent operation and potential for full decarbonization when coupled with renewable hydrogen production. Yet, the operational efficiency of HFC marine power systems is significantly affected by nonlinear electrochemical kinetics, interactions between subsystems and extremely wide fluctuating load profiles typical for naval service missions. The resulting complex multi-variable control laws open up trade-offs between efficiency, hydrogen consumption, dynamic response and emission of auxiliary by-products like NOx, unconverted hydrogen and water vapor exhaust. To tackle these issues, the present work puts forward a complete Multi Objective Particle Swarm Optimization (MOPSO) framework for the combined performance and emissions optimization of hydrogen marine–FC propulsion systems. The MOPSO technique is used to optimize decision variables such as fuel cell operating current density, stack temperature, air stoichiometric ratio, compressor control and the proportion at which ESS is lead on hybrid with a Pareto-optimal trade-offs between: (i) system efficiency, (ii) hydrogen consumption, (iii) emissions and by-product reduction, and (iv) component degradation and thermal stress reduction. A high-fidelity dynamic simulation model has been developed, combining PEM fuel cell electrochemical kinetics, onboard power demand information and compressor-humidifier dynamics with marine propulsion load cycles for patrol, ferry and tugboat duties. Results show that the MOPSO optimized control strategy increases net fuel cell efficiency by 12-18%, decreases hydrogen consumption by 15-27%, and decreases auxiliary emissions and stack temperature excursions by up to 35% as compared with the baseline, rule based, and single-objective tuning approaches. Moreover, degradation parameters such as voltage decay rate and membrane hydration instability were dramatically mitigated which enabled extension of the projected stack life time by 20–30%.

This study employs a multi-objective particle swarm optimization (MOPSO) algorithm to address the dual-objective challenge in the robotic polishing of Ti-6Al-4V. The aim is to determine optimal parameters that minimize surface roughness while maximizing the material removal rate (MRR), thereby improving both surface quality and processing efficiency. First, a material removal depth model for end-face polishing is established based on Preston's equation and theoretical analysis, from which the MRR model is derived. Subsequently, orthogonal experiments are conducted to investigate the influence of process parameters and their interactions on surface roughness, followed by the development of a quadratic polynomial roughness prediction model. Analysis of variance (ANOVA) and model validation confirm the model's reliability. Finally, the MOPSO algorithm is applied to obtain the Pareto optimal solution set, yielding the optimal parameter combination. Experimental results demonstrate that at a normal contact force of 7.58 N, a feed rate of 4.52 mm/s, and a spindle speed of 5851 rpm, the achieved MRR and Ra values are 0.2197 mm3/s and 0.291 μm, respectively. These results exhibit errors of only 5.64% and 2.65% compared to model predictions, validating the proposed method's effectiveness.

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

In order to enhance the convergence and distribution of multi-objective particle swarm algorithm, an improved multi-objective particle swarm optimization algorithm was proposed. Linear decreasing inertia weight was used to update. The method can improve the deficiency that the algorithm falls into local optimal easily. The improved Logistic mapping was used to increase ergodicity of the particles. The method can expand the search scope. At the same time, the elite archiving mechanism and the mutation probability were introduced to increase the disturbance. The method can improve the local optimal. Compared with the real Pareto front, NSGA-II and MOEAD algorithm, the simulation shows that the algorithm proposed in the paper is effective.

In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOSSASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the MOSCA, MOPSO, MOEAD, and NSGA-II and the MOSSASCA are 0.53, 1.31, 1.36, and 1.44, respectively. Additionally, the MOSSASCA has higher MTBHS value than MOSCA, MOPSO, MOEAD, and NSGA-II by 362.49, 274.70, 585.73 and 672.94, respectively, and the proposed method has lower SLAV values than MOPSO, MOEAD, and NSGA-II by 0.41, 0.28, and 1.27, respectively.

An integrated optimization strategy based on Kriging model and multi-objective particle swarm optimization (PSO) algorithm was constructed. As a new surrogate model technology, Kriging model has better fitting precision for nonlinear problem. The Kriging model was adopted to replace computer aided engineering (CAE) simulation as fitness function of multi-objective PSO algorithm, and the computation cost can be reduced greatly. By introducing multi-objective handling mechanism of crowding distance and mutation operator to multi-objective PSO algorithm, the entire Pareto front can be approximated better. It is shown that the multi-objective optimization strategy can get higher solving accuracy and computation efficiency under small sample.