Swarm Approach Research Articles

Experimental and numerical investigation of mid-infrared laser in Pr3+-doped chalcogenide fiber**Project supported by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 61605095), the Natural Science Foundation of Zhejiang Province, China (Grant No. LY19F050004), the Natural Science Foundation of Ningbo City (Grant No. 2015A610038), the Open Fund of the Guangdong Engineering Technology Research and Development

We report on a chalcogenide glass fiber doped with Pr3+ that can be used for commercialized 1.5- and 2- laser excitations by emitting broadband – fluorescence, which is extruded into a preform and then drawn into a step-index fiber. The spectroscopic properties of the fiber and glass are reported, and the mid-infrared fiber lasers are also numerically investigated. Cascade lasing is employed to increase the inversion population of the upper laser level. The particle swarm approach is applied to optimize the fiber laser parameters. The output power can reach 1.28 W at 4.89- wavelength, with a pump power of 5 W, excitation wavelength at , Pr3+ ion concentration at 4.22 ×1025 ions/m3, fiber length at 0.94 m, and fiber background loss at 3 dB/m.

Trajectory optimization of an innovative-turbofan-powered aircraft based on particle swarm approach for low environmental impact

Flight route planning in civil aviation seeks to minimize the total cost of the operation while maintaining high safety standards. Therefore, the increase in the number of air routes as well as air...

A Self-Adapted Swarm Architecture to Handle Big Data for “Factories of the Future”

Currently, the manufacturing sector is facing a technological evolution with the so-called Industry 4.0. This poses a paradigm shift, enabling companies to be more competitive by taking advantage of innovative technologies(cloud computing, cyber-physical systems, big data analytics and deep learning), pursuing near-zero fault, near real-time reactivity to any problem, better traceability, more predictability in manufacturing, while working to achieve cheaper product customization. The challenges arise when the dimensionality of the data generated by manufacturing processes grows, affecting the performance of algorithms, decreasing it quickly as the dimension of the search space increases. Handling large datasets with a good performance in a limited time should be the main concern in Big Data analytics. This paper focuses on a logistic process of car manufacturing, where batteries are unloaded from trucks to warehouse, and then to the point of fit, where they are assembled into the car. It presents a complete data-driven architecture, using a swarm approach for distributed data processing among all data stages, where processing nodes with different tasks and technologies can work cooperatively to complete a job. The work presented in this paper is funded by the EU project BOOST4.0, focusing on a smart manufacturing scenario for the automotive sector.

Swarm Approach Combined With Artificial Neural Networks to Constructive Data Organization and Information Extrapolation

Swarm Approach Combined With Artificial Neural Networks to Constructive Data Organization and Information Extrapolation

A Swarming Approach to Optimize the One-Hop Delay in Smart Driving Inter-Platoon Communications

Multi-platooning is an important management strategy for autonomous driving technology. The backbone vehicles in a multi-platoon adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit vehicles’ kinematics information through inter-platoon communications, and then forward the information to the member vehicles through intra-platoon communications. In this case, each vehicle in a multi-platoon can acquire the kinematics information of other vehicles. The parameters of DCF, the hidden terminal problem and the number of neighbors may incur a long and unbalanced one-hop delay of inter-platoon communications, which would further prolong end-to-end delay of inter-platoon communications. In this case, some vehicles within a multi-platoon cannot acquire the emergency changes of other vehicles’ kinematics within a limited time duration and take prompt action accordingly to keep a multi-platoon formation. Unlike other related works, this paper proposes a swarming approach to optimize the one-hop delay of inter-platoon communications in a multi-platoon scenario. Specifically, the minimum contention window size of each backbone vehicle is adjusted to enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. The simulation results indicate that, the one-hop delay of the proposed approach is reduced by 12% as compared to the DCF mechanism with the IEEE standard contention window size. Moreover, the end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been significantly improved.

Multiobjective Optimization for Arrangement of the Asymmetric-Paths Winding Based on Improved Discrete Particle Swarm Approach

The asymmetric-paths winding is a novel ac winding with a fractional ratio of the number of poles to the number of paths, which is beneficial for the optimized design of machines. Resulting from the numerous combinations of slot-vectors for path windings, the optimized arrangement with a low asymmetric degree can hardly be achieved by existing methods. Focusing on the optimization for arrangements of this winding, the mathematical optimization model considering two objectives for the percentage of the electromotive force error and the total harmonic distortion is built. In addition, an improved discrete particle swarm optimization approach is proposed and includes a setting method for the initial positions, a discrete operator with chaotic mutation for the velocity update, and an aggregate strategy combined with fuzzy inference. To validate the proposed approach, a winding design of a hydrogenerator is presented combined with the finite element analysis. The results show that the optimized arrangement presents a low asymmetric degree and harmonic. This approach provides a technical support for the arrangement optimization of an asymmetric-paths winding.

Solving large-scale problems using multi-swarm particle swarm approach

Several metaheuristics have been previously proposed and several improvements have been implemented as well. Most of these methods were either inspired by nature or by the behavior of certain swarms such as birds, ants, bees, or even bats. In the metaheuristics, two key components (exploration and exploitation) are significant and their interaction can significantly affect the efficiency of a metaheuristic. How-ever, there is no rule on how to balance these important components. In this paper, a new balancing mechanism based on multi-swarm approach is proposed for balancing exploration and exploitation in metaheuristics. The new approach is inspired by the concept of a group(s) of people controlled by their leader(s). The leaders of the groups communicate in a meeting room where the overall best leader makes the final decisions. The proposed approach applied on Particle Swarm Optimization (PSO) to balance the exploration and exploitation search called multi-swarm cooperative PSO (MPSO). The proposed approach strived to scale up the application of the (PSO) algorithm towards solving large-scale optimization tasks of up to 1000 real-valued variables. In the simulation part, several benchmark functions were per-formed with different numbers of dimensions. The proposed algorithm was tested on several test functions, with four different number of dimensions (100, 500, and 1000) it was evaluated in terms of performance efficiency and compared to standard PSO (SPSO), and master-salve PSO algorithm. The results showed that the proposed PSO algorithm outperformed the other algorithms in terms of the optimal solutions and the convergence.

Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method

In an electrical power system, the coordination of the overcurrent relays plays an important role in protecting the electrical system by providing primary as well as backup protection. To reduce power outages, the coordination between these relays should be kept at the optimum value to minimize the total operating time and ensure that the least damage occurs under fault conditions. It is also imperative to ensure that the relay setting does not create an unintentional operation and consecutive sympathy trips. In a power system protection coordination problem, the objective function to be optimized is the sum of the total operating time of all main relays. In this paper, the coordination of overcurrent relays in a ring fed distribution system is formulated as an optimization problem. Coordination is performed using proposed continuous particle swarm optimization. In order to enhance and improve the quality of this solution a local search algorithm (LSA) is implanted into the original particle swarm algorithm (PSO) and, in addition to the constraints, these are amalgamated into the fitness function via the penalty method. The results achieved from the continuous particle swarm optimization algorithm (CPSO) are compared with other evolutionary optimization algorithms (EA) and this comparison showed that the proposed scheme is competent in dealing with the relevant problems. From further analyzing the obtained results, it was found that the continuous particle swarm approach provides the most globally optimum solution.

A particle swarm approach for tuning washout algorithms in vehicle simulators

The MCA tuning problem involves finding the most appropriate values for the parameters (or coefficients) of Motion Cueing Algorithms (MCA), also known as washout algorithms. These algorithms are designed to control the movements of the robotic mechanisms, referred to as motion platforms, employed to generate inertial cues in vehicle simulators. This problem can be approached in several different ways. The traditional approach is to perform a manual pilot-in-the-loop subjective tuning, using the opinion of several pilots/drivers to guide the process. A more systematic approach is to use optimization techniques to explore the vast parameter space of the MCA, using objective motion fidelity indicators, so that the process can be automated. A genetic algorithm (GA) has been recently proposed to perform this process, with promising results. Following this approach, this paper proposes applying Particle Swarm Optimization (PSO) to solve the MCA tuning problem. The PSO-based proposed solution is assessed using the classical washout MCA, comparing its performance, convergence and correctness against the GA-based solution. Results show that a PSO-based tuning of MCA can provide better results and converges faster than a GA-based one. In addition, PSO is easier to set-up than GA, since only one parameter of the optimization algorithm itself (the number of particles) needs to be set-up, instead of a minimum of four in the case of the GA.

A framework for a hierarchical model of cooperation between unmanned airplanes

ABSTRACTIn this article we propose a framework for a hierarchical model for cooperation between unmanned airplanes in large groups. We argue that the swarm approach to cooperation between a large number of mobile robots is ineffective when robots can be equipped with efficient communication, precise location hardware and with complex control algorithms. The proposed model, analogous to the organisation of a team of people, is an intermediate solution, between the swarm approach and central control. The bottom layer of the model includes unmanned airplanes that perform tasks related to the mission goals, such as capturing images, looking for objects through image analysis, etc. These airplanes are organised in teams, and each team is controlled by the superior airplane. Two top layers are ground-based: the central computer and the human operator, and their functions are mostly defining the goals, planning and optimisation of the mission. We present two examples of response to events, related to failure or loss of an airplane. We discuss how the proposed model can manage possible interruptions of communication and security issues, and how collision avoidance can be implemented. Finally, we point out that, although the control structure is different than in the swarm approach, swarm intelligence can still be used to optimise missions.

Two swarm intelligence-based approaches for the <i>p</i>-centre problem

The p-centre problem is an important facility location problem. In this problem, the objective is to find a set Y of p vertices on an undirected weighted graph G = (V, E) in such a way that Y ⊆ V and the maximum distance over all the distances from vertices to their closest vertices in Y is minimised. The vertices in set Y are called centres. In this paper, we have proposed two swarm intelligence-based approaches for the p-centre problem. The first approach is based on artificial bee colony (ABC) algorithm, whereas the latter approach is based on invasive weed optimisation (IWO) algorithm. The ABC algorithm and IWO algorithm are relatively new metaheuristic techniques inspired respectively from collective intelligent behaviour shown by honeybees while foraging and the sturdy process of weed colonisation and dispersion in an ecosystem. Computational results on the well-known benchmark instances of p-centre problem show the effectiveness of our approaches in finding high quality solutions.

A Swarm Approach for Improving Voltage Profiles and Reduce Power Loss on Electrical Distribution Networks

As a mean to provide reactive power compensation to radial distribution networks, capacitor banks are commonly installed within these electrical distribution systems. When properly allocated, capacitor banks provide several benefits, including an improvement on the feeder’s voltage profile and significant reductions on power loss, which, in turn, leads to important amounts of energy savings and cost reductions. This formulation is known in the literature as optimal capacitor placement (OCP) problem. From an optimization perspective, OCP is considered remarkably complex due to its high multi-modality, discontinuity, and non-linearity. In this paper, the swarm optimization algorithm known as locust search (LS) is proposed for solving the OCP problem. The proposed approach has been tested by considering several IEEE’s radial distribution test systems, and its performance has also been compared against that of other techniques currently reported on the literature to solve the OCP problem. Our experimental results suggest that the proposed LS-based method is able to competitively solve the OCP problem in terms of accuracy and robustness.

Nature inspired algorithm using particle swarm approach with variations in inertia weights for automatic test data generation based on dominance concepts

In software testing, testing of all program statements is a very crucial issue as it consumes a lot of time, effort and cost. The time, effort and cost can be reduced by using an efficient technique to reduce the test case and a good optimization algorithm to generate efficient, reliable and unique test cases. In this paper, the concept of dominance tree is used which covers all edges/statement by using minimum test case. Nature inspired algorithm - PSO (Particle Swarm Optimization) by applying different inertia weights is used to generate unique, reliable and efficient test cases to cover the leaf nodes of dominance tree. Inertia weights like fixed inertia weight (FIW), global-local best (GLbestIW), Time-Dependent weight (TDW), and proposed GLbestRandIW weights are used with PSO to investigate the effect of inertia weights on the execution of PSO with respect to number of generation required, percentage coverage , total test cases generated to test the software under consideration.

Toward modeling and optimization of features selection in Big Data based social Internet of Things

The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.

Chaos particle swarm optimization combined with circular median filtering for geophysical parameters retrieval from Windsat

This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s−1 and 0.88°C, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the QuikSCAT measurements, and the RMS error was 13.3°. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.

Multi swarm bare bones particle swarm optimization with distribution adaption

Bare bones PSO is a simple swarm optimization approach that uses a probability distribution like Gaussian distribution in the position update rules. However, due to its nature, Bare bones PSO is highly prone to premature convergence and stagnation. The characteristics of the probability distribution functions used in the update rule have a tense impact on the performance of the bare bones PSO. As a result, this paper investigates the use of different methods for estimating the probability distributions used in the update rule. Four methods or strategies are developed that are using Gaussian or multivariate Gaussian distributions. The choice of an appropriate updating strategy for each particle greatly depends on the characteristics of the fitness landscape that surrounds the swarm. To deal with issue, the cellular learning automata model is incorporated with the proposed bare bones PSO, which is able to adaptively learn suitable updating strategies for the particles. Through the interactions among its elements and the learning capabilities of its learning automata, cellular learning automata gradually learns to select the best updating rules for the particles based on their surrounding fitness landscape. This paper also, investigates a new and simple method for adaptively refining the covariance matrices of multivariate Gaussian distributions used in the proposed updating strategies. The proposed method is compared with some other well-known particle swarm approaches. The results indicate the superiority of the proposed approach in terms of the accuracy of the achieved results and the speed in finding appropriate solutions.

Identification of inelastic parameters based on deep drawing forming operations using a global–local hybrid Particle Swarm approach

Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress–strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder–Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global–local PSO–Nelder–Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder–Mead algorithm to obtain the minimum itself.

Closed loop supply chain networks: Designs for energy and time value efficiency

Product recovery has become a viable option for many industries to realize economic gains while protecting the environment. However, insufficient investment and inefficient supply chains have hampered the viability of reuse and/or recycling because of the extended time intervals between the recycling process of recovery and reuse. Manufacturers and distributors face the challenge and necessity to reduce these process delays in order to recover the maximum value of the returned products through an effective, responsive closed loop supply chain (CLSC). This paper quantitatively measures the effective responsiveness of the CLSC model in terms of time and energy efficiency. The proposed multi-objective mixed integer linear programming (MOMILP) model evaluates delay parameters with decision variables that maximize profit, optimize customer surplus and minimize energy use. The model suggests decision makers may achieve an optimal tradeoff among differing objectives in a multiple-objective CLSC scenario. We employed a multi-objective particle swarm optimization (MOPSO) approach to solve the proposed MOMILP model and compared our approach with the Non-Dominated Sorted Genetic Algorithm (NSGA-II) for optimal solution. Results of the comparative evolutionary approaches shows that MOPSO outperforms NSGA-II in almost all cases in achieving the best trade-off solutions. Sensitivity analysis carried out to test the robustness of the model confirms that substantially less cost is feasible through the reduction of return process delays. This paper aims to formulate a multi-objective CLSC problem based on a network-flow model measuring the time value to recover maximum assets lost due to delay at different stages of the recycle process. We also developed a particle swarm approach for a multi-objective CLSC. Our study also offers valuable insights for designers wishing to create a product flow network with an optimal capacity level in case of prioritized objectives scenarios.

A particle swarm approach for optimizing a multi-stage closed loop supply chain for the solar cell industry

In order to implement sustainable strategies in a supply chain, enterprises should provide highly favorable and effective solutions for reducing carbon dioxide emissions, which brings out the issues of designing and managing a closed-loop supply chain (CLSC). This paper studies an integrated CLSC network design problem with cost and environmental concerns in the solar energy industry from sustainability perspectives. A multi-objective closed-loop supply chain design (MCSCD) model has been proposed, in consideration of many practical characteristics including flow conservation at each production/recycling unit of forward/reverse logistics (FL/RL), capacity expansion, and recycled components. A deterministic multi-objective mixed integer linear programming (MILP) model capturing the tradeoffs between the total cost and total CO2 emissions was developed to address the multistage CSLC design problem. Subsequently, a multi-objective PSO (MOPSO) algorithm with crowding distance-based nondominated sorting approach is developed to search the near-optimal solution of the MCSCD model. The computational study shows that the proposed MOPSO algorithm is suitable and effective for solving large-scale complicated CLSC structure than the conventional branch-and-bound optimization approach. Analysis results show that an enterprise needs to apply an adequate recycling strategy or energy saving technology to achieve a better economic effectiveness if the carbon emission regulation is applied. Consequently, the Pareto optimal solution obtained from MOPSO algorithm may give the superior suggestions of CLSC design, such as factory location options, capacity expansion, technology selection, purchasing, and order fulfillment decisions in practice. An integrated CLSC problem by using PSO algorithm in the solar energy industry has been studied.A deterministic multi-objective mixed integer programming model has been established.The results Pareto CLSC solutions between CPLEX and MOPSO have been compared.MOPSO provides a rapid algorithm to search Pareto solution compare to CPLEX in large scale issues.

A particle swarm approach for optimization of secondary cooling process in slab continuous casting

Secondary cooling control is the key factor for stabilizing and enhancing slab quality in continuous casting. In view of practical importance of critical boundary conditions in offline or online simulation and control during continuous casting process, accurate estimation for heat transfer coefficient of secondary cooling zone is of utmost significance. To optimize the cooling process and temperature behavior of continuous casting slab, a novel method was presented to predict the heat transfer behavior in secondary cooling process. This approach applies the particle swarm optimization (PSO) algorithm in conjunction with the mathematical heat transfer model and the experimental temperature to determine the heat transfer coefficient. Through verifying the validity and efficiency of the integrated method proposed, the temperature variation of slab surface is more coincident with measured temperatures along the casting direction. The calculation results confirm that the heat transfer coefficient could be estimated precisely with measurement temperatures using PSO algorithm. The combined approach offers an applicable technology for optimization of cooling strategy and solidifying process in continuous casting.