Self-adaptive Particle Swarm Optimization Algorithm Research Articles

Optimal DoS attack on multi-channel cyber-physical systems: A Stackelberg game analysis

In this commentary, optimal denial-of-service (DoS) attack strategies on multi-channel cyber-physical systems (CPSs) are considered, which focus on energy allocation on communication channels. For simplicity, a Stackelberg game between one defender and one attacker is constructed. Compared with the existing literature, which mainly pay attention to static equilibrium, the dynamic process of the game is also exhibited in this paper, which fills the gap in the demonstration of dynamic decision-making between both players of the game. In the solution of Stackelberg equilibrium, a self-adaptive particle swarm optimization (PSO) algorithm with Sigmoid-like update function is applied to cope with the nonlinearity of the reward function with faster convergence and wider adaptability. Besides, to acquire better performance of both sides to allocate energy, an online computation algorithm is proposed for dynamic Stackelberg game. Finally, numerical examples are provided to illustrate similarities between theoretic static equilibrium and optimal strategies obtained by Monte Carlo simulations.

Optimal DoS attack strategy for cyber-physical systems: A Stackelberg game-theoretical approach

In this paper, the optimal DoS attack on cyber-physical systems is studied in Stackelberg game framework, which is manifested by energy allocation on communication channels. To give a realistic picture of DoS attack on a normal user launched by several hackers, a Stackelberg game between one defender and multiple attackers is considered. Compared with the existing literature, which mainly focus on equilibrium in static games, the dynamic process of the Stackelberg game is also reflected in this paper, which demonstrates the intelligence of attackers switching channels to allocate aggressive energy. In the solution of Stackelberg equilibrium, self-adaptive Particle Swarm Optimization (PSO) algorithm is utilized to cope with the nonlinearity of the reward function. Besides, for the purpose of better performance of both sides in choosing channels to allocate energy, an online computation algorithm is proposed. Finally, numerical examples are equipped to illustrate the theoretical results proved in this work.

Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

Day-Ahead Optimal Scheduling of an Integrated Energy System Based on a Piecewise Self-Adaptive Particle Swarm Optimization Algorithm

The interdependency of electric and natural gas systems is becoming stronger. The challenge of how to meet various energy demands in an integrated energy system (IES) with minimal cost has drawn considerable attention. The optimal scheduling of IESs is an ideal method to solve this problem. In this study, a day-ahead optimal scheduling model for IES that included an electrical system, a natural gas system, and an energy hub (EH), was established. The proposed EH contained detailed models of the fuel cell (FC) and power to gas (P2G) system. Considering that the optimal scheduling of an IES is a non-convex complex optimal problem, a piecewise self-adaptive particle swarm optimization (PCAPSO) algorithm based on multistage chaotic mapping was proposed to solve it. The objective was to minimize the operating cost of the IES. Three operation scenarios were designed to analyze the operation characteristics of the system under different coupling conditions. The simulation results showed that the PCAPSO algorithm improved the convergence rate and stability compared to the original PSO. An analysis of the results demonstrated the economics of an IES with the proposed EHs and the advantage of cooperation between the FC and P2G system.

The cold rolling load distribution of the nuclear power zirconium alloy based on the self-adaptive particle swarm optimization algorithm

Aiming at the problem of load distribution during multi-pass cold rolling of nuclear zirconium alloy strip, the load distribution model with good plate shape is established by the self-adaptive particle swarm optimization (SAPSO) algorithm, considering the main constraint conditions including rolling force, reduction, and torque in cold rolling process. Based on the penalty function method transforming the constraint problem into the unconstrained problem, the particle swarm optimization algorithm (PSO) combined with self-adaptive inertia weight factor optimized the load distribution model is developed to improve the local search ability of the particle swarm optimization algorithm. Compared with the original nuclear zirconium alloy cold rolling schedule, the simulation results of load distribution based on the SAPSO algorithm can keep good plate shape in multi-pass cold rolling process with the high prediction accuracy. The industrial experiments demonstrate that the proportional crown difference value is consistent, and the plate shape flatness is good.

Self-Adaptive Multi-Task Differential Evolution Optimization: With Case Studies in Weapon–Target Assignment Problem

Multi-task optimization (MTO) is related to the problem of simultaneous optimization of multiple optimization problems, for the purpose of solving these problems better in terms of optimization accuracy or time cost. To handle MTO problems, there emerges many evolutionary MTO (EMTO) algorithms, which possess distinguished strategies or frameworks in the aspect of handling the knowledge transfer between different optimization problems (tasks). In this paper, we explore the possibility of developing a more efficient EMTO solver based on differential evolution by introducing the strategies of a self-adaptive multi-task particle swarm optimization (SaMTPSO) algorithm, and by developing a new knowledge incorporation strategy. Then, we try to apply the proposed algorithm to solve the weapon–target assignment problem, which has never been explored in the field of EMTO before. Experiments were conducted on a popular MTO test benchmark and a WTA-MTO test set. Experimental results show that knowledge transfer in the proposed algorithm is effective and efficient, and EMTO is promising in solving WTA problems.

An analytical inverse kinematics solution with joint limits avoidance of 7-DOF anthropomorphic manipulators without offset

The inverse solution calculation speed and the configuration control are very important for anthropomorphic manipulators with 7 degree-of-freedom (DOF). In order to realize the combined control of global arm configuration manifolds and local self-motion of 7-DOF anthropomorphic manipulators without offset, an analytical inverse kinematics solution method is proposed in this paper. By defining a new arm reference plane for 7-DOF anthropomorphic manipulators, it uses arm angle to deal with local self-motion and orientation control to deal with global arm configuration manifolds. All possible inverse kinematics solutions for target pose can be intuitively expressed with the assistance of the parameters of arm angle and orientation control. Meanwhile, the corresponding singularity is also taken into account and a detection metric is provided. Furthermore, the problem of joint limits avoidance is mapped to the selection of arm angle in null space, and the Self-Adaptive Particle Swarm Optimization algorithm is introduced to obtain the optimal arm angle that can maximize the avoidance of joint limits. Simulation results show the proposed method has such advantages as fast calculation speed, high precision and high stability.

Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification

Many evolutionary computation (EC) methods have been used to solve feature selection problems and they perform well on most small-scale feature selection problems. However, as the dimensionality of feature selection problems increases, the solution space increases exponentially. Meanwhile, there are more irrelevant features than relevant features in datasets, which leads to many local optima in the huge solution space. Therefore, the existing EC methods still suffer from the problem of stagnation in local optima on large-scale feature selection problems. Furthermore, large-scale feature selection problems with different datasets may have different properties. Thus, it may be of low performance to solve different large-scale feature selection problems with an existing EC method that has only one candidate solution generation strategy (CSGS). In addition, it is time-consuming to find a suitable EC method and corresponding suitable parameter values for a given large-scale feature selection problem if we want to solve it effectively and efficiently. In this article, we propose a self-adaptive particle swarm optimization (SaPSO) algorithm for feature selection, particularly for large-scale feature selection. First, an encoding scheme for the feature selection problem is employed in the SaPSO. Second, three important issues related to self-adaptive algorithms are investigated. After that, the SaPSO algorithm with a typical self-adaptive mechanism is proposed. The experimental results on 12 datasets show that the solution size obtained by the SaPSO algorithm is smaller than its EC counterparts on all datasets. The SaPSO algorithm performs better than its non-EC and EC counterparts in terms of classification accuracy not only on most training sets but also on most test sets. Furthermore, as the dimensionality of the feature selection problem increases, the advantages of SaPSO become more prominent. This highlights that the SaPSO algorithm is suitable for solving feature selection problems, particularly large-scale feature selection problems.

Optimization of Network-Load Interaction With Multi-Time Period Flexible Random Fuzzy Uncertain Demand Response

This paper represents a network-load interaction optimization framework integrated multi-time period flexible random fuzzy uncertain demand response (DR) model, which involved shifting of loads’ operation time and uncertain response to the price signal. The network-load interaction is to optimize load profile and network topology under the guidance of electricity price at the aim of achieving the economic and secure operation of the distribution network and at the same time satisfying the consumers’ satisfaction. This framework is a two-level framework, which includes the price-based DR level to minimize the daily load variance and maximize the customers’ satisfaction, and the reconfiguration level to minimize the network reconfiguration cost and power unbalancing. Firstly, the price-based DR level determines the price and the load profile subject to the multi-time period flexibility and uncertainties of the DR. Then, the reconfiguration level optimizes the network configuration topology according to the load profile and feeds the results back to the price-based DR level. This network-load interaction optimization model is tackled by the proposed multi-objective self-adaptive particle swarm (SAPSO) optimization algorithm. The proposed network-load interaction optimization model is applied to the IEEE33-bus distribution system and a real system. The results show that this model is efficient to solve the network economic operation and load profile optimization problem simultaneously.

Feedforward neural network based on ensemble evolutionary algorithm with self-adaptive strategy and parameter for intrusion detection

The applications of Wireless Sensor Networks (WSNs) have driven the development of many fields, and the information security is an important issue in the WSNs. Intrusion Detection System (IDS) is a kind of effective system for identifying network attacks and protecting data security, which has a great significance for WSNs. In this paper, we transform the intrusion detection problem into a classification problem and introduce the Feedforward Neural Network (FNN) classifier to solve it. At the same time, we propose a novel Self-adaptive Parameter and Strategy Differential Particle Swarm (SPS-DPS) optimisation algorithm to find the optimal weights for the FNN. Experiments are performed on eight data sets which are constructed based on the intrusion detection data set KDDCUP99, and the results are compared with five Evolutionary Computation (EC) methods. The results show that the SPS-DPS based FNN method can solve the intrusion detection problem well compared with the other methods.

Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data

Vessel traffic flow forecasting is indispensable for the development of national shipping industry and the coordinated development of regional economy. In this paper, an improved PSO-BP (particle swarm optimization-back propagation) prediction model is established for the prediction of the total vessel traffic flow in a designated port area. The presented prediction model is referred to as SAPSO-BP neural network which utilizes the SAPSO (self-adaptive particle swarm optimization) algorithm to adjust the structure parameters of BP neural network. Facilitated by the establishment of foundation networks and satellite communication of Automatic Identification System (AIS) receivers, the detailed information of vessel is becoming increasingly obtainable. Therefore, a large number of real-observed vessel traffic flow data based on AIS records of Port area of Los Angeles (LA) has been chosen as the testing database to validate the effectiveness of the SAPSO-BP prediction model in vessel traffic flow forecasting. The grey correlation analysis (GCA) is employed to confirm the input dimension of the prediction model. Finally, simulation results demonstrate that the presented prediction approach can achieve vessel traffic flow trend predictions with reasonable, satisfactory convergence and stability.

Hybridisation of feed forward neural network and self-adaptive PSO with diverse of features for anomaly detection

The objective of this paper is to activity recognition using hybridisation of self-adaptive learning particle swarm optimisation algorithm with feed forward neural network (SLPSO-FFNN). Basically, the system consists of four phases namely, Background Estimation (BE), Object Segmentation (OS), Feature Extraction (FE), and Activity Recognition (AR). At first, we generate the high quality background using BE phase. Then OS model is used to extract the object from the videos and then object tracking process is used to track the object through the overlapping detection scheme. From the tracked objects, the FE module extracts some useful features. Finally, SLPSO-FFNN based approach is used to detect the anomaly present in the videos. In experimentation, we used two types of dataset UCSD pedestrian dataset (Ped_1, Ped_2) and CDNET dataset. The experimental result shows our proposed anomaly detection system obtains the minimum Equal Error Rate (EER) of 0.08% and achieves the maximum accuracy of 97.64%.

A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm

This paper develops a particle swarm optimisation (PSO) based framework for multi-objective optimisation (MOO). As a part of development, a new PSO method, named self-adaptive PSO (SAPSO), is first proposed. Since the convergence of SAPSO determines the quality of the obtained Pareto front, this paper analytically investigates the convergence of SAPSO and provides a parameter selection principle that guarantees the convergence. Leveraging the proposed SAPSO, this paper then designs a SAPSO-based MOO framework, named SAMOPSO. To gain a well-distributed Pareto front, we also design an external repository that keeps the non-dominated solutions. Next, a circular sorting method, which is integrated with the elitist-preserving approach, is designed to update the external repository in the developed MOO framework. The performance of the SAMOPSO framework is validated through 12 benchmark test functions and a real-word MOO problem. For rigorous validation, the performance of the proposed framework is compared with those of four well-known MOO algorithms. The simulation results confirm that the proposed SAMOPSO outperforms its contenders with respect to the quality of the Pareto front over the majority of the studied cases. The non-parametric comparison results reveal that the proposed method is significantly better than the four algorithms compared at the confidence level of 90% over the 12 test functions.

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithm

A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithm

A self-adaptive particle swarm optimisation and bacterial foraging hybrid algorithm

When used to deal with complex functions with high dimension, Bacterial Foraging Algorithm BFA converges slowly and Particle Swarm Optimisation PSO algorithm tends to premature convergence and low accuracy. Aiming at these shortcomings, an improved hybrid optimisation algorithm based on PSO and BFA is proposed in the paper ABSO for short. The ABSO algorithm adds extremum disturbance to PSO. It also adaptively improves learning factors and inertial weight of PSO, chemotaxis step-length and disperse probability of BFA, respectively. BFA is used as the whole frame of the hybrid algorithm. After the chemotaxis operation of BFA, PSO is introduced to help BFA escape from local optima. This combines organically the optimisation update mechanism of PSO and the chemotaxis update mechanism of BFA, and can well balance the global search and local development capabilities. Simulation results on four benchmark functions show that the ABSO algorithm is superior to BFA, PSO, self-adaptive PSO and two other kinds of BFA hybrid algorithm in convergence speed, accuracy and robustness. This proves the validity of the ABSO algorithm in high-dimensional function optimisation problems.

Probabilistic evaluation for static voltage stability for unbalanced three‐phase distribution system

To investigate the impact of the randomness of renewable energy generation on distributed network, the authors propose a non-linear three-phase maximum loadability model. Considering uncertainty such as wind power generation and load forecasting deviation, a probabilistic evaluation of static voltage stability for distributed network is presented based on cumulants method. Correlations between adjacent wind farms are also considered. Maximal load increment models of three-phase balanced/unbalanced power system are solved by improved self-adaptive particle swarm optimisation algorithm. Two approximation expansions named Cornish–Fisher expansion and Gram–Charlier expansion are employed to obtain probability distribution of random variables. Relation of probabilistic performances including means and variances between balanced system and unbalanced system could be obtained. 25-bus/33-bus unbalanced system and 33-bus balanced system are studied as examples to validate effectiveness of their proposed method, and results based on cumulants method are compared with Monte Carlo simulations.

Solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self-adaptive particle swarm optimization and K-harmonic means

Excellent prediction modeling of CO2 solubility in polymers using hybrid computation algorithm.

A Self-adaptive Global Particle Swarm Optimization Algorithm for Unconstrained Optimization Problems

This paper aims to present a self-adaptive global particle swarm optimization (SGPSO) algorithm for solving unconstrained optimization problems. In the new algorithm, the inertia weights are generated based on Gaussian distribution, which is helpful to improve the diversity of the population. In addition, the worst particle is updated by averaging the other particles, which is beneficial to improving the quality of the population. Finally, a global disturbance is adopted to increase the convergence rate of SGPSO. In the disturbance process, a disturbance factor is utilized to control the searching ranges of the population, which can effectively keep a balance between the global exploration and local exploitation. Twenty well-known benchmark functions are considered to evaluate the performance of SGPSO, and 50 runs are implemented in each case. Numerical experiments and comparisons demonstrate that SGPSO is superior to the other three algorithms according to means, standard deviations and convergence rate.