Chaotic Particle Swarm Optimization Research Articles

Node Localization Algorithm Based on Modified Archimedes Optimization Algorithm in Wireless Sensor Networks

Node localization information plays an important role in wireless sensor networks (WSNs). To solve the problem of low localization accuracy of distance vector hop (DV-Hop) localization algorithm in wireless sensor networks, an improved localization algorithm called MAOADV-Hop based on the modified Archimedes optimization algorithm (MAOA) and DV-Hop is proposed, which can achieve the balance between the localization speed and the localization precision. Firstly, tent chaotic mapping and particle swarm optimization (PSO) algorithm are introduced into Archimedes optimization algorithm to improve the initial population diversity and change the update rules of density and volume, which improve the global convergence ability and convergence speed of the algorithm. Secondly, the MAOA is used to replace the least square part of the DV-Hop localization algorithm to improve the localization accuracy of the algorithm. Finally, MAOADV-Hop is verified through four different network environments and compared with DE_DV-Hop, BOA_DV-Hop, and DV-Hop. The simulation results show that the localization speed of the proposed approach is faster than that of DE_DV-Hop and BOA_DV-Hop, and the localization error is less than that of DV-Hop, DE_DV-Hop, and BOA_DV-Hop.

An improved DECPSOHDV-Hop algorithm for node location of WSN in Cyber–Physical–Social-System

Cyber-Physical Social System (CPSS) refers to the interaction between cyber-physical systems and social networks. The accuracy of information transmission in Cyber-Physical System is very important, and the information transmission of WSN node positioning occupies an important position. Aiming at the ubiquitous positioning accuracy and resource allocation problems in the node positioning, we propose an improved Chaotic Particle Swarm Optimization Hybrid Distance Vector Hopping Algorithm Based on Differential Evolution (DECPSOHDV-Hop) algorithm. This algorithm uses an improved algorithm parallel mechanism in the WSN node positioning, which can be more accurately positioned unknown node through dynamic optimization. In addition, when solving high-dimensional function optimization problems, particle swarm optimization (PSO) algorithms tend to converge prematurely and fall into local extremes. Therefore, based on the (DE)/current-to-best/1 operator, this paper proposes chaotic PSO algorithms of hybrid and parallel. For particle optimization in the difference operator, Gaussian/normal distribution is used for balance optimization. The hybrid method selects the basic particle swarm update strategy according to the probability, simplified the particle swarm update strategy or differential optimization to update the individual. The parallel method uses the improved model to update the Gaussian mutation operator of DE to update the entire population. In the simulation experiment results, the results of testing 20 high-dimensional benchmark functions show that compared with PSO and CPSO, the above two improved algorithms have better search accuracy and higher convergence speed, as well as the optimization results of the test function; In WSN node positioning, the positioning accuracy of the DECPSOHDV-Hop algorithm is as high as 90%, and the excellent positioning stability of the algorithm is verified from three different standards.

Chaotically Enhanced Meta-Heuristic Algorithms for Optimal Design of Truss Structures with Frequency Constraints

The natural frequencies of any structure contain useful information about the dynamic behavior of that structure, and by controlling these frequencies, the destructive effects of dynamic loads, including the resonance phenomenon, can be minimized. Truss optimization by applying dynamic constraints has been widely welcomed by researchers in recent decades and has been presented as a challenging topic. The main reason for this choice is quick access to dynamic information by examining natural frequencies. Also, frequency constraint relations are highly nonlinear and non-convex and have implicit variables, so using mathematical and derivative methods will be very difficult and time consuming. In this regard, the use of meta-heuristic algorithms in truss weight optimization with frequency constraints has good results, but with the introduction of form variables, these algorithms trap at local optima. In this research, by applying chaos map in meta-heuristic algorithms, suitable conditions have been provided to escape from local optima and access to global optimums. These algorithms include Chaotic Cyclical Parthenogenesis Algorithms (CCPA), Chaotic Biogeography-Based Optimization (CBBO), Chaotic Teaching-Learning-Based Optimization (CTLBO) and Chaotic Particle Swarm Optimization (CPSO), respectively. Also, by using different scenarios, a good balance has been achieved between the exploration and exploitation of the algorithms.

Data-based variable universe adaptive fuzzy controller with self-tuning parameters

Data-based variable universe adaptive fuzzy controllers (VUAFCs) with self-tuning parameters are developed for complex systems with unknown universes in this paper. The main feature of the proposed VUAFC is the new defined contraction–expansion (C-E) factor on an infinite universe, which is more flexible and practical in real applications. Moreover, the data-based methods to tune the parameters including the peak points of output fuzzy subsets and the C-E factor parameters are proposed. The peak points of the output fuzzy subsets are mined by an improved Wang–Mendel method based on conflicting rules. The parameters of the VUAFC are optimized by solving an offline optimization problem using the chaotic particle swarm optimization (CPSO) algorithm. The simulation results on the strip temperature control of the radiant-tube indirect-fired furnace of annealing furnace show that our proposed method has strong practicability and good control performance.

Multi-objective stochastic project scheduling with alternative execution methods: An improved quantum-behaved particle swarm optimization approach

This paper addresses the multi-objective resource-constrained project scheduling problems with stochastic activity durations and alternative execution methods. Three objectives are considered: minimizing expected makespan, expected cost and robustness. Chance constrained programming is applied for formulating this stochastic problem. A hybrid approach that integrates sample average approximation (SAA) and an improved multi-objective chaotic quantum-behaved particle swarm optimization (MOCQPSO) algorithm is proposed. To improve the diversity of solutions and enhance the global search ability, a two-stage learning strategy that balances the exploration and the exploitation is proposed for MOCQPSO. In addition, chaotic operators including chaotic initialization, crossover and mutation are also introduced. Six benchmark functions and an instance generator based on the RCPSP dataset of PSPLIB are designed to validate the performance of the proposed algorithm. The experimental results demonstrate that our proposed method outperforms the original algorithms in solution diversity and quality.

Secure Encryption of Color Images with Chaotic Systems and Particle Swarm Optimization

Secure Encryption of Color Images with Chaotic Systems and Particle Swarm Optimization

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Abstract Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.

TUNING OF TWO-DEGREE-OF-FREEDOM IMC BASED ON A STRANGE NONCHAOTIC OPTIMIZATION APPROACH FOR LARGE TIME-DELAY PROCESSES

The internal model control (IMC) has a good control effect for large time-delay systems, but the parameter tuning of the controller is a challenge for researchers. As a typical fractal (strange) attractor, a strange nonchaotic attractor has the dynamic characteristics of randomness and ergodicity. Based on these characteristics, an optimization method is proposed to tune the parameters of an internal model controller for a large time-delay process, which is named a strange nonchaotic optimization algorithm (SNOA). SNOA has the advantages of easy implementation, short execution time and having robust mechanisms to avoid local optimization. It can find the optimal parameters of the internal model controller in large time-delay systems. The results show that SNOA can effectively improve the dynamic stability of the large time-delay systems. It has also proved to be superior to chaotic optimization (CO) algorithm and particle swarm optimization (PSO) algorithm.

The Strategy of Considering the Participation of Doubly-Fed Pumped-Storage Units in Power Grid Frequency Regulation

Facing the challenges of grid stability brought by the large-scale access of new energy to the grid, as one of the important means of frequency regulation of the grid, the doubly-fed pumped-storage units (DFPSU) have great practical significance in the study of the joint frequency regulation strategy with the thermal power unit (TPU). In order to reduce the frequency regulation pressure on the grid caused by the large-scale entry of new energy sources, this paper proposes a joint frequency regulation strategy for the TPU and DFPSU. Based on establishing the optimal scheduling model of the system, taking into account the minimum total operating cost and the optimal time as goals, the chaotic particle swarm optimization (CPSO) is proposed to solve the model. Finally, through MATLAB simulation, the relevant examples are studied and analyzed, and the simulation results verify that the joint frequency regulation strategy in this paper can effectively improve the frequency regulation performance of the unit and the operation economy is good.

Enhanced multiclass support vector data description model for fault diagnosis of gears

This work reports a study aimed at identifying the operating state of gear through an enhanced multiclass support vector data description (eMSVDD) model where the multiple hyperspheres corresponding to different operating states of gears are established. The model addresses the following issues: (1) the overlap of multiple hyperspheres deteriorates the diagnostic performance for boundary samples and severely weakens the generalization ability of the model; (2) the original support vector data description can only be applied to handle binary classification tasks; and (3) the performance of the SVDD depends heavily on the penalty factor C and kernel parameter δ. In the proposed model, we first implement the multi-label classification task by building multiple hyperspheres. Then, a novel fitness function for adaptive simplified chaotic particle swarm optimization is proposed to determine the model parameters, which considers the empirical risk minimization and model generalization capability. Finally, experimental results demonstrate that the eMSVDD outperforms deep neural networks and support vector machine by 3.4% and 0.5%, respectively, and this performance improvement respectively reaches 17.8% and 2.1% in the case of a very small sample size. The proposed approach provides a means for gear fault diagnosis with small samples.

Parameter identification of permanent magnet synchronous motors using quasi-opposition-based particle swarm optimization and hybrid chaotic particle swarm optimization algorithms

Parameter identification of permanent magnet synchronous motors using quasi-opposition-based particle swarm optimization and hybrid chaotic particle swarm optimization algorithms

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

The losses in electrical power systems are a great problem. Multiple methods have been utilized to decrease power losses in transmission lines. The proper adjusting of reactive power resources is one way to minimize the losses in any power system. Reactive Power Optimization (RPO) problem is a nonlinear and complex optimization problem and contains equality and inequality constraints. The RPO is highly essential in the operation and control of power systems. Therefore, the study concentrates on the Optimal Load Flow calculation in solving RPO problems. The Simple Particle Swarm Optimization (PSO) often falls into the local optima solution. To prevent this limitation and speed up the convergence for the Simple PSO algorithm, this study employed an improved hybrid algorithm based on Chaotic theory with PSO, called Chaotic PSO (CPSO) algorithm. Undeniably, this merging of chaotic theory in PSO algorithm can be an efficient method to slip very easily from local optima compared to Simple PSO algorithm due to remarkable behavior and high ability of the chaos. In this study, the CPSO algorithm was utilized as an optimization tool for solving the RPO problem; the main objective in this study is to decrease the power loss and enhance the voltage profile in the power system. The presented algorithm was tested on IEEE Node-14 system. The simulation implications for this system reveal that the CPSO algorithm provides the best results. It had a high ability to minimize transmission line losses and improve the system's voltage profile compared to the Simple PSO and other approaches in the literature.

MUCPSO: A Modified Chaotic Particle Swarm Optimization with Uniform Initialization for Optimizing Software Effort Estimation

Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence speed. Nevertheless, since its origin in 1995, Particle swarm optimization still suffers from two primary shortcomings, i.e., premature convergence and easy trapping in local optima. Therefore, this study proposes modified chaotic particle swarm optimization with uniform particle initialization to enhance the comprehensive performance of standard particle swarm optimization by introducing three additional schemes. Firstly, the initialized swarm is generated through a uniform approach. Secondly, replacing the linear inertia weight by introducing the nonlinear chaotic inertia weight map. Thirdly, by applying a personal learning strategy to enhance the global and local search to avoid trap in local optima. The proposed algorithm is examined and compared with standard particle swarm optimization, two recent particle swarm optimization variants, and a nature-inspired algorithm using three software effort estimation methods as benchmark functions: Use case points, COCOMO, and Agile. Detailed investigations prove that the proposed schemes work well to develop the proposed algorithm in an exploitative manner, which is created by a uniform particle initialization and avoids being trapped on the local optimum solution in an explorative manner and is generated by a personal learning strategy and chaotic-based inertia weight.

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods.

Chaotic Particle Swarm Optimization with Attractive Search Space Border Points for Optimal Reactive Power Dispatch

This paper presents a reliable approach for optimal reactive power dispatch based on chaotic particle swarm optimization with attractive search space border points, where the particles are randomly attracted to the boundary points of the search space in each direction avoiding stagnation of the population. The introduction of chaotic dynamics improves the stability and rate of convergence. The algorithm is further improved by using Latin Hypercube Sampling (LHS) to create diversity in the population. The proposed algorithm is used for optimal reactive power dispatch with three objective functions, namely: minimization of real power loss, voltage stability index, and sum squared voltage deviations. The algorithm is tested on a standard 30-bus system of the Institute of Electrical and Electronics Engineers (IEEE) and on a practical 75-bus Indian Power System. The results obtained with the proposed algorithm are compared with the conventional interior point method and the basic particle swarm optimization algorithm, and the effectiveness of the proposed algorithm is demonstrated.

Bio-Inspired Ant Lion Optimizer for a Constrained Petroleum Product Scheduling

Real-world optimization problems demand sophisticated algorithms. Over the years bio-inspired approach, a subset of computational intelligence has demonstrated remarkable success in real-world use cases, especially where exact or deterministic algorithms are ineffective. Petroleum product scheduling is a complex optimization task belonging to the combinatorial problem category. The problem size and the constraints compound the complexity of the petroleum product scheduling problem. However, conventional optimization methods such as the exact or deterministic algorithm produced a poor solution quality to the petroleum products scheduling problem. Therefore, this study leverages the potency of a bio-inspired approach, Ant Lion Optimizer (ALO) in its basic state to enhance the solution quality. This is in line with She-Shin Yang’s proposition, father of bio-inspired algorithms who advocated for the application of existing bio-inspired algorithms to tackle real-world problems rather than developing new algorithms. Bio-inspired is a computational paradigm that models the characteristics of natural biological entities to solve complex problems. We also used the Chaotic Particle Swarm Optimization (CPSO) algorithm for the same problem to unveil the efficacy of the roulette wheel function in ALO. The results show a 24.8% and 23.9% reduction in the original cost of distribution on ALO and CPSO respectively. Also, 99.5% of the constraints are met. Thus, problems of scarcity, minimum allocation and product availability are solved using the penalty constraint handling method. The exact algorithm showed a 14% reduction in the original cost. However, despite the effectiveness, further work on constraint handling methods and other bio-inspired computation approaches such as Genetic algorithms and their variants could be possible in the future scope. Moreover, other real-world problem domains such as power distribution in the power, sector could be a possible application of the ALO.

Research on Temperature Control of Incubator Based on Chaotic Particle Swarm Optimization and Fuzzy PID

In the process of performance testing of precision devices in the aerospace field, it is difficult to apply the traditional PID control to the high-precision incubators, which is unable to achieve the ideal control accuracy. For this problem, fuzzy PID is optimized by using nonlinear piecewise Logistic chaos mapping to initiate particle group, optimize the parameters of ACPSO-Fuzzy-PID and introduce shrinkage factor to ensure the convergence of the algorithm, making the overall and local search capability of particle group more efficient. Firstly, the algorithm was tested by standard functions, and finally, PID, fuzzy PID, PSO-Fuzzy-PID, ACPSO-Fuzzy-PID were contrast simulated by SIMULINK at a constant temperature of 75℃. Simulation results show that the ACPSO-Fuzzy-PID control system has faster response time, less overshoot, shorter adjustment time, temperature control fluctuation more stable and stronger anti-disturbance ability, which significantly improves the temperature control accuracy of the incubator in the aerospace field.

Distribution Network Equipment Location and Capacity Planning Method Considering Energy Internet Attribute from the Perspective of Life Cycle Cost

For facing the challenges brought by large-scale renewable energy having access to the system and considering the key technologies of energy Internet, it is very necessary to put forward the location method of distribution network equipment and capacity from the perspective of life cycle cost. Compared with the traditional energy network, the equipment capacity problem of energy interconnected distribution network which involves in electricity network, thermal energy network and natural gas network is comprehensively considered in this paper. On this basis, firstly, the operation architecture of energy interconnected distribution network is designed. Secondly, taking the grid connection location and configuration capacity of key equipment in the system as the control variables and the operation cost of system comprehensive planning in the whole life cycle as the goal, the equipment location and capacity optimization model of energy interconnected distribution network is established. Finally, an IEEE 33 bus energy mutual distribution grid system is taken for example analysis, and the improved chaotic particle swarm optimization algorithm is used to solve it. The simulation results show that the method proposed in this paper is suitable for the equipment location and capacity planning of energy interconnected distribution network, and it can effectively improve the social and economic benefits of system operation.

Dynamic Data Scheduling of a Flexible Industrial Job Shop Based on Digital Twin Technology

Aiming at the problems of premature convergence of existing workshop dynamic data scheduling methods and the decline in product output, a flexible industrial job shop dynamic data scheduling method based on digital twin technology is proposed. First, digital twin technology is proposed, which provides a design and theoretical basis for the simulation tour of a flexible industrial job shop, building the all‐factor digital information fusion model of a flexible industrial workshop to comprehensively control the all‐factor digital information of the workshops. A CGA algorithm is proposed by introducing the cloud model. The algorithm is used to solve the model, and the chaotic particle swarm optimization algorithm is used to maintain the particle diversity to complete the dynamic data scheduling of a flexible industrial job shop. The experimental results show that the designed method can complete the coordinated scheduling among multiple production lines in the least amount of time.

A novel data-driven controller for plug-in hybrid electric vehicles with improved adaptabilities to driving environment

Instantaneous application optimality is one of the indispensable indicators to assess energy management performance of plug-in hybrid electric vehicles (PHEVs). The momentary optimality, nevertheless, cannot be flexibly reachable under various driving environments due to the partial unobservabilities in control algorithms. To cope with it, a novel data-driven controller for PHEVs is proposed in this paper to achieve the instantaneous optimality of energy management. The well-designed machine learning based controller translates the knowledge of global optimization to real-time controlling scheme with the consideration of adaptabilities to disperse driving conditions. To start with, the universal global optimal control policies for varying driving environment are generated offline based on the chaotic quantum particle swarm optimization with sequential quadratic programming (CQPSO-SQP). Then, the offline optimized global control policies are assembled to construct the dataset for training the least square support vector machine (LSSVM) based controller, which features the superior capability in instantly optimal policy making under different driving conditions. At last, the detailed assessment is performed in simulation test and hardware-in-loop (HIL) test to validate the promising role of CQPSO-PSO and LSSVM in designing the novel energy management controller, and the corresponding results highlight the preferable controlling performance of the proposed novel controller in practical applications.