Basic Particle Swarm Optimization Algorithm Research Articles

An Improved Multi-Objective Particle Swarm Optimization-Based Hybrid Intelligent Algorithm for Index Screening of Underwater Manned/Unmanned Cooperative System of Systems Architecture Evaluation

An improved multi-objective particle swarm optimization algorithm is combined with a machine learning classifier to meet the needs of underwater manned/unmanned cooperative warfare architecture evaluation. Firstly, based on the traditional Cauchy variation method, the particles in the population are disturbed in a dynamic way so that the particles trapped in the local optimal can jump out of the local optimal, and the convergence performance of the particle swarm optimization is improved. Secondly, the accuracy of the index set is analyzed based on the CART decision tree algorithm and the IWRF algorithm. A screening method of key indexes with fewer evaluation indexes and high evaluation accuracy is developed to solve the problem of a large number of evaluation indexes and unclear correlation of the underwater combat system. Through simulation, the extraction results of key indicators were verified, and the reliability coefficient of the final simulation experiment was 0.93, which can be considered as high reliability and effectiveness of the key indicators extracted in this study. By combining multi-objective optimization with machine learning and weighing evaluation efficiency and accuracy, a high-precision and rapid evaluation of a few indicators is achieved, which provides support for establishing an evaluation model of SoS architecture for underwater manned/unmanned cooperative operations. This research result can provide inspiration for the evaluation and evaluation of the system in order to analyze the accuracy of indicators and the evaluation effect and carry out research by simulating the actual system with tools with a high simulation degree.

Numerical Insight Into the Origin of Nonreciprocity and Performance Enhancement in Nonreciprocal Bandpass Filters Using Evolutionary Algorithm

In this paper, a non-reciprocal filter with enhanced directivity is analyzed methodically, and the filter parameters are optimized using an evolutionary algorithm. The non-uniform spectral dispersion of power among the harmonics in opposite directions is substantiated with the aid of filtering polynomials. These polynomials indicate the origin of at least one transmission zero within the passband in the reverse direction, thereby promoting the non-reciprocal filter response. The filter’s return loss, insertion loss, and isolation characteristics exhibit a trade-off that demands an optimization algorithm to effectuate the judicious choice of circuit parameters. This problem is addressed by designing a 150 MHz lumped element non-reciprocal bandpass filter based on the parameters extracted using an evolutionary algorithm based particle swarm optimization (PSO). The simulated and measured results comply well with the modeling, and the results exhibit maximum directivity of 28.2 dB without degradation in insertion loss (1.1 dB) and return loss (16.2 dB) within the passband. The algorithm can be utilized in designing non-reciprocal filters having different center frequencies and bandwidths.

An Effective Local Particle Swarm Optimization-Based Algorithm for Solving the School Timetabling Problem

This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances showed that the proposed algorithm achieved the proven optima provided from an integer programming method presented in earlier research. In almost all cases, the current algorithm beat the integer programming method, either concerning the lower bound yielded or the execution time needed.

Intelligent computing for WPT–MEC-aided multi-source data stream

Due to its low latency and energy consumption, edge computing technology is essential in processing multi-source data streams from intelligent devices. This article investigates a mobile edge computing network aided by wireless power transfer (WPT) for multi-source data streams, where the wireless channel parameters and the characteristic of the data stream are varied. Moreover, we consider a practical communication scenario, where the devices with limited battery capacity cannot support the executing and transmitting of computational data streams under a given latency. Thus, WPT technology is adopted for this considered network to enable the devices to harvest energy from the power beacon. In further, by considering the device’s energy consumption and latency constraints, we propose an optimization problem under energy constraints. To solve this problem, we design a customized particle swarm optimization-based algorithm, which aims at minimizing the latency of the device processing computational data stream by jointly optimizing the charging and offloading strategies. Furthermore, simulation results illustrate that the proposed method outperforms other benchmark schemes in minimizing latency, which shows the proposed method’s superiority in processing the multi-source data stream.

A hybrid Gaussian mutation PSO with search space reduction and its application to intelligent selection of piston seal grooves for homemade pneumatic cylinders

To make the motion tracking control of the homemade pneumatic cylinder as accurate as possible, it is necessary to properly match the seal groove and seal ring on the piston to generate the appropriate friction. However, since the relationship between friction and motion control accuracy is not yet clear, and the friction affected by many factors cannot be accurately modeled, it is impossible to design the optimal seal groove with the highest possible motion control accuracy for pneumatic cylinder through theoretical calculation or simulation optimization. For this reason, an experimental optimization method is considered to select the optimal one from the six empirically designed seal grooves through a particle swarm optimization (PSO) algorithm. To cope with the shortcomings of slow convergence rate and the tendency to fall into local optimum of the basic PSO algorithm (BPSO), three improved PSO algorithms (HGMPSO-0, HGMPSO and HGMPSO-SSR) are successively proposed in this study. The first two improved algorithms are compared with other PSO variants on 23 benchmark functions tested, and the results show that HGMPSO has better overall performance. To significantly improve the search space search efficiency and make the algorithm converge faster, the search space contraction mechanism is introduced into the HGMPSO algorithm to form the HGMPSO-SSR algorithm. The experimental results show that the HGMPSO-SSR algorithm significantly outperforms other PSO variants used for comparison and successfully achieves intelligent selection of piston seal grooves for the designed homemade pneumatic cylinder.

Optimization Model and Algorithm of Logistics Vehicle Routing Problem under Major Emergency

The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%.

Swarm Optimization of Fuzzy Systems for Mobile Robots with Remote Control

This paper is dedicated to the development and research of the advanced approach for optimization of fuzzy control systems (FCS) for mobile robots (MR) with remote control based on bioinspired swarm techniques. The proposed approach makes it possible to create effective intelligent control systems for MRs based on the principles of hierarchical multi-level control, remote IoT-based control, fuzzy logic control, and intelligent optimization of fuzzy control devices. The applied hybrid particle swarm optimization (PSO) techniques with elite strategy allow effectively optimizing various parameters of FCSs, finding the optimal solution to the problem, and, at the same time, have a higher convergence rate compared with the basic PSO algorithms. To evaluate the effectiveness of the obtained advanced approach based on hybrid swarm techniques, the optimization process of the FCS for the speed control of the multi-purpose caterpillar MR, which can move on inclined and vertical ferromagnetic surfaces, is carried out. The presented research results fully confirm the high efficiency of the proposed approach, as well as the expediency of its application for the optimization of fuzzy control systems for various remotely controlled mobile robots.

Trajectory Tracking of an Electro-Hydraulic Servo System With an New Friction Model-Based Compensation

Nonlinear friction is the main factor affecting the dynamic and static performances of robotic excavator electrohydraulic servo systems. The traditional LuGre friction model is only related to the velocity. However, the friction characteristics of the electrohydraulic servo system are also related to the position and direction. An improved LuGre is proposed to describe the friction characteristics between position, velocity and direction, and the instability problem is also solved by stopping the observation of the internal state <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</i> . Then, inertia weight, asynchronous change method and elite mutation operation are added into the basic particle swarm optimization algorithm to identify the friction model parameters accurately. Moreover, a friction compensator (FC) based on the LuGre model is designed. Three controllers are implemented with different trajectory experiments on a 23 ton excavator. Experimental results demonstrated that the FC based on the improved LuGre can improve the comprehensive performance of the electrohydraulic servo system.

Mining high utility itemsets using Genetic Algorithm Based-Particle Swarm Optimization (GA-PSO)

Mining high utility itemsets (HUIs) from transaction databases is one of the current research areas in the data mining field. HUI mining finds itemsets whose utility meets a predefined threshold. It enables users to quantify the usefulness or preferences of products by utilizing different values. Since utility mining approaches do not satisfy the downward closure property, the cost of candidate generation for HUI mining in terms of time and memory space is excessive. This paper presents Genetic Algorithm based Particle Swarm Optimization (GA-PSO), which can efficiently prune down the number of candidates and optimally acquire the complete set of high utility itemsets. The proposed algorithm’s performance is assessed using the synthetic dataset T20.I6.D100K and the real-time supermarket dataset, which comprises 38765 transactions and 167 unique products. It performs very effectively in terms of time and memory on large databases constituted of small transactions, which are challenging for existing high utility itemsets mining algorithms to manage. Experiments on real-world applications show the importance of high utility itemsets in business decisions, as well as the distinction between frequent and high utility itemsets.

Research on Improved PSO in Communication Site Planning

Aiming at the planning problem of mobile communication station location, this study first uses the base station distribution data to screen, and on this basis, establishes the objective function and constraint conditions of station number and station location positioning. Secondly, based on the basic particle swarm optimization algorithm and Yangwei particle swarm optimization algorithm, the original algorithm is optimized by adding inertia weight disturbance to obtain the optimal number of station sites and station location, and then the station location is clustered. It is concluded that the establishment of 455 macro base stations and 1902 micro base stations is the most suitable, and the weak coverage base stations are divided into 35 categories for management.

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization.

Aiming at the problems of single planning technology and relatively few resource types in the process of regional comprehensive energy system planning at the current stage, this paper proposes a method based on the construction of regional comprehensive energy system planning and the operation model. Through the integration of the particle swarm optimization algorithm, this method can better realize the optimization and economic operation of the regional comprehensive energy system and build a system optimization mode based on two stages of planning and operation to pursue the optimal configuration of system equipment. Through the simulation algorithm, it is found that the solution time of the traditional basic particle swarm optimization algorithm is 10.49s, while the average solution time of the particle swarm optimization system proposed in this study is 7.93s; the efficiency is increased by 24.4%, and the system operation efficiency is significantly improved, providing theoretical and technical guidance for the economic operation of the regional comprehensive energy system.

Performance Analysis and Optimization for Jammer-Aided Multiantenna UAV Covert Communication

Unmanned aerial vehicles (UAVs) have attracted a lot of research attention because of their high mobility and low cost in serving as temporary aerial base stations (BSs) and providing high data rates for next-generation communication networks. To protect user privacy while avoiding detection by a warden, we investigate a jammer-aided UAV covert communication system, which aims to maximize the user’s covert rate with optimized transmit and jamming power. The UAV is equipped with multi-antennas to serve multi-users simultaneously and enhance the Quality of Service. By considering the general composite fading and shadowing channel models, we derive the exact probability density (PDF) and cumulative distribution functions (CDF) of the signal-to-interference-plus-noise ratio (SINR). The obtained PDF and CDF are used to derive the closed-form expressions for detection error probability and covert rate. Furthermore, the covert rate maximization problem is formulated as a Nash bargaining game, and the Nash bargaining solution (NBS) is introduced to investigate the negotiation among users. To solve the NBS, we propose two algorithms, i.e., particle swarm optimization-based and joint two-stage power allocation algorithms, to achieve covertness and high data rates under the warden’s optimal detection threshold. All formulated problems are proven to be convex, and the complexity is analyzed. The numerical results are presented to verify the theoretical performance analysis and show the effectiveness and success of achieving the covert communication of our algorithms.

Quantum behaved particle swarm optimization of inbound process in an automated warehouse

The inbound process is of great importance in enhancing the efficiency of automated warehouse operations. This study investigates an optimization problem on the inbound warehouse process by coordinating multiple resources in a type of automated warehouse system, i.e., Shuttle-Based Storage and Retrieval System (SBS/RS). A mixed-integer programming model is formulated to determine the assignment decisions of the pallets towards three types of the resources in the SBS/RS (i.e., forklifts, lifts and shuttles), the sequencing & timing decisions of these three types of resources for transporting the pallets. Then, a novel solution method, called Adaptive Quantum behaved Particle Swarm Optimization (AQPSO) algorithm, is designed to solve the proposed model. The introduction of the quantum mechanism prevents the algorithm from falling into a local minimum. The integration of the adaptive adjustment strategy improves the efficiency of the algorithm by dynamically adjusting the search scale. The efficiency of the proposed algorithm is verified by comparative experiments that use the CPLEX solver and the basic particle swarm optimization algorithm as rivals. The experimental results indicate that the proposed algorithm have an advantage in the solution quality and the computing time. A series of sensitivity analyses are also conducted to bring out some managerial insights. For example, it is beneficial to reduce energy consumption by adjusting the relative velocity and power of the three types of equipment, and setting the best ratios of shuttles to forklifts.

Quay crane and yard truck dual-cycle scheduling with mixed storage strategy

In order to enhance the efficiency of port operations, the scheduling problem of the quay cranes and yard trucks is crucial. Conventional port operation mode lacks optimization research on efficiency of port handling operation, yard truck scheduling, and container storage location. To make quay crane operations and horizontal transportation more efficient, this study uses a dual-cycle strategy to focus on a quay crane and yard truck scheduling problem in conjunction with a mixed storage strategy. A dispatching plan for yard trucks is considered, as well as the storage location of inbound containers. Based on the above factors, a mixed-integer programming model is formulated to minimize vessels’ berth time for completing all tasks. The proposed model is solved using a particle swarm optimization-based algorithm. Validation of the proposed model and algorithm is conducted through numerical experiments. Additionally, some managerial implications which may be potentially useful for port operators are obtained.

An Artificial Neural Network-Based Approach to Optimizing Energy Efficiency in Residential Buildings in Hot Summer and Cold Winter Regions.

Resource depletion and ecological crisis have prompted human beings to reflect on the behavior patterns based on industrial civilization so as to seek ways of sustainable development of human society, economy, technology, and environment. The energy consumed in the construction process, commonly known as building energy consumption, accounts for more and more of the total social energy consumption, and with the continuous development of social economy and the improvement of living standards, this proportion will be larger and larger. The structure of the neural network directly determines its performance and work efficiency. The structure optimization of the neural network is not only a hot issue in this field but also an insurmountable key step in engineering applications. With the increase of network depth, the structural optimization difficulty index of the neural network increases, so solving this problem has important theoretical and practical significance for the design and application of the neural network. In this paper, the energy saving of buildings is optimized based on the optimization of structures such as particle swarm optimization (PSO) algorithm and restricted Boltzmann machine. The experimental results show that the BPNN optimized by the improved PSO algorithm is significantly better than the non-optimized BPNN and the BPNN optimized by the basic PSO algorithm. The comprehensive output rate of the optimized neural network can reach 64.5%. In general, the error rate of the optimized artificial neural network (ANN) will be 57.65% lower than the original one.

Wavefront shaping using improved sparrow search algorithm to control the scattering light field

An improved sparrow search algorithm (ISSA) is proposed to control the light field that passes through the scattering medium. The principles of elite reservation and random perturbation are first introduced by ISSA. Then, the position (input mask) update formulas of producer, scrounger and scouters are modified to improve the wavefront shaping ability. It is showed that ISSA significantly improves the wavefront shaping performance of focusing and imaging in turbid media compared with the basic sparrow search algorithm (SSA) and particle swarm optimization algorithm (PSO). Therefore, ISSA has the potential to be applied to related research in the future.

The Improvement of DV-Hop Model and Its Application in the Security Performance of Smart Campus

In the smart campus, sensors are the basic units in the whole the Internet of Things structure, which play the role of collecting information and transmitting it. How to transmits more information at a certain power level has attracted the attention of many researchers. In this paper, the DV-Hop algorithm is optimized by combining simulated annealing-interference particle swarm optimization algorithm to improve the node localization of wireless sensor networks and enhance the security performance of smart campus. To address the problem that particle swarm optimization easily falls into local optimum, a perturbation mechanism is introduced in the basic particle swarm optimization algorithm. Meanwhile, the acceptance probability P is introduced in the simulated annealing algorithm to determine whether a particle is accepted when it “flies” to a new position, which improves the probability of finding a global optimal solution. Comparing the average localization error and optimization rate of the DV-Hop algorithm, PSO-DV-Hop algorithm, and the optimized algorithm. The results show a greater advantage of the algorithm. This will greatly enhance the safety performance and efficiency of the smart campus.

Deep neural network based UAV deployment and dynamic power control for 6G-Envisioned intelligent warehouse logistics system

The intelligent warehouse logistics system (IWLS) is an essential component in the emerging industry 5.0. To well assist the IWLS, advanced network architecture and control policies with the adaptive capability to the variation of traffic should be specially designed. In this paper, we consider an air-and-ground cooperative wireless network that enables dynamic coverage to support the flexible scheduling of the automatic guided vehicles (AGVs) in the IWLS. Jointly considering the dynamic deployment of unmanned aerial vehicles (UAVs) as well as the adaptive power control of AGVs, we formulate a two time-scale network control problem to minimize the transmission power consumption of all AGVs under their individual rate requirement. On large time scales, we first propose a particle swarm optimization based algorithm (PSOA) to obtain the deployment position of the ABS. Then, using the results of the PSOA as training data, we design a deep neural network (DNN) framework aimed at reducing the computational time of the PSOA. On small time scales, we devise an online power control algorithm (OPCA) by using some of stochastic network optimization methods. With current channel conditions, the OPCA can generate the real-time power control policy and ensure the long-term rate requirement. Numerical simulations indicate that the DNN framework enhances the coverage performance of the network only consuming a few milliseconds of computation time. Incorporated with the OPCA, the total transmission power of the AGVs is significantly reduced.

Distributed DRL-Based Downlink Power Allocation for Hybrid RF/VLC Networks

Hybrid radio frequency (RF) and visible light communication (VLC) networks can provide high throughput and energy efficiency with VLC access points (APs) while ensuring ubiquitous coverage with RF APs. Due to dynamic channel conditions and limited resources, the hybrid RF/VLC networks’ resource allocation problem is complex and challenging. Conventional resource allocation techniques fail to overcome these challenges. Heuristic methods can solve high complexity problems; however, they are not robust against changes such as dynamic channel conditions or alternating user requirements. Heuristic methods require centralized control for stability which adds communication overhead between APs. Deep Reinforcement Learning (DRL) based solutions can solve high complexity, dynamic channel conditions, and alternating user requirements while not requiring centralized control. In this paper, we formulate a distributed downlink power allocation problem to optimize the transmit power for users to reach target data rates in hybrid RF/VLC networks. Then, we propose a distributed DRL-based algorithm Deep Deterministic Policy Gradient (DDPG), to solve the formulated computationally-intensive problem. We implement a simulation environment to benchmark the proposed distributed DRL-based method against other methods such as Q-Learning (QL) and Deep Q-Networks (DQN), and centralized heuristic power allocation algorithms. Our simulation results show that the distributed DDPG-based algorithm learns to adapt against changes in the channel or user requirements, while centralized Genetic Algorithm and Particle Swarm Optimization-based algorithms fail to endure against these changes even with coordination between APs. Additionally, we quantify the performance of the DDPG-based algorithm to prevail amid DRL-based algorithms at the expense of higher implementation complexity.

An Improved Particle Swarm Optimization Algorithm for Unmanned Aerial Vehicle Route Planning

A global path planning method based on improved particle swarm optimization (PSO) algorithm was proposed to find a high quality flight trajectory in three-dimensional complex environment under multiple threats for UAV. The improved path planning algorithm combines the standard PSO with A* method to compensate for the slow convergence rate of PSO. The objective function with multiple constraints of A* method is used to evaluate the quality of the waypoint, and the objective function of PSO algorithm is designed to evaluate the quality of the candidate path. To verify the effectiveness of the improved algorithm, the improved PSO algorithm is used to compare with basic PSO algorithms. The experiment in complex environment shows it has stronger search ability, convergence ability due to the improvement of population diversity and convergence speed.