Cooperative Particle Swarm Optimization Research Articles

A self-learning particle swarm optimization for bi-level assembly scheduling of material-sensitive orders

In modern assembly production, maximizing fulfillment becomes challenging amid resource scarcity and process unpredictability. This study addresses such issues with an effective approach to solving the integrated problem. This confluence presents a scenario where resource allocation mirrors a multidimensional knapsack and job sequencing corresponds to distributed assembly permutation flowshop scheduling, factoring in sequence-dependent setup times. Employing a mixed-integer mathematical model, we capture the configurations and objectives of the integrated problem. A special dual-level cooperative self-learning particle swarm optimization (SLPSO) metaheuristic is developed for improved hierarchical decision-making. Adaptive learning is harnessed to guide dynamic job selection under uncertainty, aiming for the highest total value. To minimize tardiness, a neighborhood search operator customizes sequences for each shop, considering changing setup times between jobs. Benchmark tests have been performed, showing that SLPSO surpasses established genetic algorithms and swarm techniques by 8%–15%. When implemented in an operational traditional pharmaceutical manufacturer, significant improvements were observed. The integrated structure harmonizes planning and scheduling stages, enhancing the algorithm via bidirectional feedback between decisions at various levels under uncertainty, thus providing guidance for real-world production.

Fault Diagnosis of Centrifugal Chiller Based on Extreme Gradient Boosting

Centrifugal chillers have been widely used in medium- and large-scale air conditioning projects. However, equipment running with faults will result in additional energy consumption. Meanwhile, it is difficult to diagnose the minor faults of the equipment. Therefore, the Extreme Gradient Boost (XGBoost) algorithm was used to solve the above problem in this article. The ASHRAE RP-1043 dataset was employed for research, utilizing the feature splitting principle of XGBoost to reduce the data dimension to 23 dimensions. Subsequently, the five important parameters of the XGBoost algorithm were optimized using Multi-swarm Cooperative Particle Swarm Optimization (MSPSO). The minor fault diagnosis model, MSPSO-XGBoost, was established. The results show that the ability of the proposed MSPSO-XGBoost model to diagnose eight different states is uniform, and the diagnostic accuracy of the model reaches 99.67%. The accuracy rate is significantly improved compared to that of the support vector machine (SVM) and back propagation neural network (BPNN) diagnostic models.

Multi-subswarm cooperative particle swarm optimization algorithm and its application

Particle swarm optimization (PSO) is a classical evolutionary algorithm and has been widely used to solve continuous optimization problems. However, the performance of the PSO algorithm is highly sensitive to its control parameters and learning strategies. To address this problem, a multi-subswarm cooperative particle swarm optimization algorithm (MSC-PSO) is proposed in this paper. In MSC-PSO, firstly, a population muti-subswarm division method is designed to enhance the global exploration capability. Secondly, a level-based particle adaptive inertia weight strategy is introduced to adjust control parameters. Finally, a level-based update learning mechanism is used for particles adaptive selection learning strategy. The performance of the MSC-PSO is evaluated by the CEC2020 test suite, and five heuristic algorithms are compared with the MSC-PSO in the experiments. The experimental results demonstrate that the proposed MSC-PSO algorithm has significant advantages in terms of convergence speed and solving optimal solutions. Furthermore, the proposed MSC-PSO algorithm is used to solve the plant protection unmanned aerial vehicles (UAV) path planning problem. The experimental results proved that the proposed MSC-PSO algorithm effectively solves the real-world UAV path planning problem, and achieves at most a 34 % reduction in round-trip distance and a 24.1 % reduction in total non-spraying time compared to classical PSO.

Identifying the plane position of the receiver coil in a multi‐transmitter IPT system through the identification of parameters

AbstractThe study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%.

Multi-swarm surrogate model assisted PSO algorithm to minimize distribution network energy losses

The computational analysis of real-world engineering problems often relies on time-consuming simulation calculations. This presents challenges in balancing computational burden and precision when applying traditional metaheuristic algorithms to large-scale complex problems. While there have been numerous surrogate-assisted metaheuristic optimization algorithms proposed, most of them are designed for solving expensive problems within 30–50 dimensions. In this paper, a novel approach called Parallel-multi-swarm Gaussian Process Regression surrogate-based particle swarm optimization (Parallel-MS-GPRS-PSO) is proposed for high-dimensional problems. This approach combines a standard particle swarm optimization (PSO) algorithm, multi-swarm cooperative PSO (MSCPSO), and a GPR surrogate-based PSO (PSO-GPR). By integrating PSO-GPR and MSCPSO, the proposed method aims to effectively explore and exploit the search space while enhancing the global and local performance of the surrogate model. To validate the effectiveness of the proposed algorithm, it is compared with several existing PSO algorithms on seven benchmark functions and an engineering problem. The results illustrate that the Parallel-MS-GPRS-PSO approach outperforms the existing algorithms. Moreover, the method shows promising performance in computationally expensive engineering optimization problems with 288 variables.

Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries

At present, the accurate establishment of the battery model and the effective state of health (SOH) estimation under actual energy storage conditions have become the main problems in new energy storage stations. Therefore, a SOH estimation method based on cooperative competitive particle swarm optimization (CCPSO) and nonlinear coefficient temperature decreasing simulated annealing-back propagation (NSA-BP) is proposed. The novelty of this research mainly includes the design of extraction methods in different health indicators (HIs) and the construction of developed NSA-BP network for SOH estimation. In this research, the contributions of SOH estimation are mainly to assist in battery replacement and provide relevant economic reference. Low-rate constant current energy storage degradation experiments and a variable-rate energy storage degradation experiment are performed for different battery packs at 25 °C. The experimental results indicate that the root mean square error (RMSE) and the mean absolute error (MAE) of the proposed method are 0.00588 and 0.00481 under the 0.5 rate condition, and the corresponding values are 0.00732 and 0.00639 under the variable-rate condition. Under the same condition, the proposed SOH estimation method is superior to the methods before improvement in RMSE and MAE, which can provide a basis for efficient monitoring of energy storage batteries.

Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

Decomposition and merging cooperative particle swarm optimization with random grouping for large-scale optimization problems

Decomposition and merging cooperative particle swarm optimization with random grouping for large-scale optimization problems

Paired Swarm Optimized Relational Vector Learning for FDI Attack Detection in IoT-Aided Smart Grid

IoT-aided smart grid heavily depends on the most innovative communication technologies that could make the grid system susceptible to False Data Injection Attacks (FDIA). The main objective of the FDI attackers remains in damaging or corrupting the state estimation strategy in the smart grid resulting in blackouts and/or to influence the electricity market. With a number of features involved in the smart grid system, FDIA detection is said to be complicated. By the conventional bad data detection systems, the FDIA detection accuracy and validation made by Receiver Operating Characteristic (ROC) curve were marginally acceptable. However, due to the time complexity and overhead incurred, the detection of FDIA is a hot research topic. In this work, we design an efficient FDIA detection method by coupling Cooperative Paired Swarm Optimization and Relational Vector Learning techniques (CPSO-RVL), to address the above-said issues. First, the cooperative paired particle swarm optimization model is proposed to attain an appropriate feature for improving the computational efficiency of FDIA detection. Next, with the obtained significant features, the relational vector learning-based FDIA detection model is designed for robust classification between FDIA and non-FDIA with minimum overhead. The extensive experiments show that the proposed method outperforms existing baseline approaches by 16%, and 34% in terms of computation time, and computation overhead respectively.

MOCPSO: A multi-objective cooperative particle swarm optimization algorithm with dual search strategies

Particle swarm optimization (PSO) is a widely embraced meta-heuristic approach to tackling the complexities of multi-objective optimization problems (MOPs), renowned for its simplicity and swift convergence. However, when faced with large-scale multi-objective optimization problems (LSMOPs), most PSOs suffer from limited local search capabilities and insufficient randomness. This can result in suboptimal results, particularly in high-dimensional spaces. To address these issues, this paper introduces MOCPSO, a Multi-Objective Cooperative Particle Swarm Optimization Algorithm with Dual Search Strategies. MOCPSO incorporates a diversity search strategy (DSS) to augment perturbation and enhance the local search scope of particles, alongside a more convergent search strategy (CSS) to expedite particle convergence. Moreover, MOCPSO utilizes a three-category framework to effectively leverage the benefits of both DSS and CSS. Experimental results on benchmark LSMOPs with 500, 1000, and 2000 decision variables demonstrate that MOCPSO outperforms existing state-of-the-art large-scale multi-objective evolutionary algorithms on most test instances.

A multi-objective cooperative particle swarm optimization based on hybrid dimensions for ship pipe route design

Pipe route design has an important influence on ship performance. The main objective is to generate pipe routes satisfying complicated constraints in 3D space. The design process includes two parts: mathematical modeling and automatic optimization algorithm, where facility simplifying is an essential part of the mathematical modeling. However, previous studies mainly focused on pipe layout algorithms based on single objective optimization, and facility simplifying need to rely on artificial assistance. Nowadays, the optimization algorithm which can give several feasible solutions for designers, and the implementation of automatic pipe route design receive much attention. Therefore, this paper presents a multi-objective cooperative particle swarm optimization based on hybrid dimensions (HDMCPSO) for ship pipe route design (SPRD), which adopts multi-particle dimensions strategy and cooperative update mechanism via representative of each subswarm based on multi-objective particle swarm optimization (MOPSO). The framework can solve the two separate NP-hard problems of facility simplifying and pipe layout compatibly, and obtain high-quality Pareto solutions. To fully evaluate the effectiveness of HDMCPSO, the pipe length, bend number and assembly are optimized as the objectives, and a real piping system of ship engine room is used to compare different methods reported in the literature. The results show that HDMCPSO has generality which can simplify different facilities models and obtain better engineering pipe layout solutions.

A cooperative particle swarm optimization with difference learning

Particle swarm optimization (PSO) is applied in complex model solving problems due to its fast convergence rate and concise algorithm structure. However, the balancing ability of PSO between exploration and exploitation still faces significant challenges. Therefore, a cooperative-based difference learning particle swarm optimization (CDLPSO) is proposed. In CDLPSO, dual swarm time-varying adjustment (DST) based on the subswarm division method is introduced on different periods, while using an appropriate subswarm division method to promote the cooperative learning of particles. Furthermore, a pros-cons coevolution mechanism (PCC) is designed in cooperative learning based on swarms. The elite subswarm can adaptively select a group of proper particles as the swarm updates, thereby obtaining a better exploration performance. Meanwhile, a stage-based cross-learning strategy (SBC) which is adopted to utilize common subswarm rationally adjusts different learning strategies on different stages. To further enhance the convergence accuracy, the common subswarm is added in more superior particles for learning. Ultimately, CDLPSO is used to solve the complex function problems on the CEC2013, CEC2022 test suite and a practical application. The experimental results demonstrate that CDLPSO can significantly balance exploitation and exploration abilities.

Hybrid multi-group stochastic cooperative particle swarm optimization algorithm and its application to the photovoltaic parameter identification problem

The accurate estimation of model parameters is significant for the simulation, evaluation, control, and optimization of photovoltaic systems. Recently, meta-heuristic algorithms(MHAs) have been proposed to solve the photovoltaic parameter identification problem. However, extracting the accurate and reliable parameters of PV models is still a great challenge, and many HMAs may present unsatisfactory performance due to their premature or slow convergence. Therefore, how to develop algorithms efficiently balancing the exploration and exploitation to improve the accuracy and reliability of the algorithm is extremely important. This paper proposes a Hybrid multi-group stochastic cooperative optimization algorithm (HMSCPSO). In the proposed algorithm, we designed a multi-group cooperation search mechanism to enhance the global search capability: Each group utilized different strategies. The first group used classic velocity and position updates, the second group employed the chaos strategy, and the third group utilized the lévy flight strategy. Through cooperation search between groups to increase the diversity of the population and reduce the possibility of falling into local optimum, but also concentrate some individuals to explore the current global optimum to improve the accuracy of the solution. HMSCPSO and its variants were tested on 27 benchmark functions to verify the proposed algorithm’s effectiveness. Then, the HMSCPSO is applied to solve four PV parameter identification problems of different photovoltaic models. Statistical experiment results demonstrate that the proposed algorithm has excellent advantages compared with other meta-heuristic algorithms in terms of accuracy, reliability, and convergence speed. Therefore, HMSCPSO is expected to be an effective parameter identification method for solar cells and photovoltaic modules.

A Novel Feedforward Model of Piezoelectric Actuator for Precision Rapid Cutting.

The piezoelectric actuator has been widely used in modern precision cutting technology due to its fast response speed and high positioning accuracy. In recent years, with the development of precision technology, modern cutting requires higher and higher cutting accuracy and efficiency. Therefore, this paper proposes a feedforward control method based on the modified Bouc-Wen (MBW) model. Firstly, a novel asymmetrical modified Bouc-Wen model with an innovative form of shape control function is developed to model the hysteresis nonlinearity property of piezoelectric actuators. Then, a self-adaptive cooperative particle swarm optimization (PSO) algorithm is developed to identify the parameters of MBW model. The comparative evaluation reveals that the MBW model outperforms the classical Bouc-Wen (CBW) model by 66.4% in modeling accuracy. Compared with traditional PSO algorithm, the self-adaptive cooperative PSO algorithm can obtain minimum fitness in parameter identification. Furthermore, the feedforward control strategy is realized to improve the position tracking accuracy. A position tracking experiment verifies that the feedforward control strategy improves the tracking accuracy of piezoelectric actuators significantly compared with the open-loop control strategy.

Online Weapon-target Assignment based on Distributed Auction Mechanism

To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in advance, considering the consumability of resources in the attack process, the integrated search and attack task flow is established. Considering the communication restricted environment, the unmanned aerial vehicles (uavs) are grouped, with centralized architecture within the group and distributed structure between the groups, and the corresponding distributed auction mechanism is constructed to achieve OWTA within the communication range limited. In order to solve the problem that it is difficult to ensure the time consistency of the coordinated attack target, a dubins cooperative path planning based on cooperative particle swarm optimization (CPSO) algorithm is proposed. Particle swarm optimization algorithm is used to adjust the radius of the dubins path of each bomb, so that the uav in the same group can hit the target simultaneously without collision and have the shortest flight range. The simulation results show that the designed distributed auction algorithm takes into account the consumption of attack resources, and quickly redistributes the firepower to the new targets in the dynamic uncertain environment, which ensures the maximization of the execution efficiency of the multi-machine cluster fire allocation task.

Cooperative Particle Swarm Optimization With a Bilevel Resource Allocation Mechanism for Large-Scale Dynamic Optimization.

Although cooperative coevolutionary algorithms are developed for large-scale dynamic optimization via subspace decomposition, they still face difficulties in reacting to environmental changes, in the presence of multiple peaks in the fitness functions and unevenness of subproblems. The resource allocation mechanisms among subproblems in the existing algorithms rely mainly on the fitness improvements already made but not potential ones. On the one hand, there is a lack of sufficient computing resources to achieve potential fitness improvements for some hard subproblems. On the other hand, the existing algorithms waste computing resources aiming to find most of the local optima of problems. In this article, we propose a cooperative particle swarm optimization algorithm to address these issues by introducing a bilevel balanceable resource allocation mechanism. A search strategy in the lower level is introduced to select some promising solutions from an archive based on solution diversity and quality to identify new peaks in every subproblem. A resource allocation strategy in the upper level is introduced to balance the coevolution of multiple subproblems by referring to their historical improvements and more computing resources are allocated for solving the subproblems that perform poorly but are expected to make great fitness improvements. Experimental results demonstrate that the proposed algorithm is competitive with the state-of-the-art algorithms in terms of objective function values and response efficiency with respect to environmental changes.

Dynamic Population Cooperative

Particle swarm optimization (PSO) has attracted wide attention in the recent decade. Although PSO is an efficient and simple evolutionary algorithm and has been successfully applied to solve optimization problems in many real-world fields, premature maturation and poor local search capability remain two critical issues for PSO. Therefore, to alleviate these disadvantages, a dynamic population cooperative particle swarm optimization for global optimization problems (DPCPSO) is proposed. Firstly, to enhance local search capability, an elite neighborhood learning strategy is constructed by leveraging information from elite particles. Meanwhile, to make the particle easily jump out of the local optimum, a crossover-mutation mechanism is utilized. Finally, a dynamic population partitioning mechanism is designed to balance exploration and exploitation capabilities. 16 classic benchmark functions and 1 real-world optimization problem are used to test the proposed algorithm against with 6 typical PSO algorithms. The experimental results show that DPCPSO is statistically and significantly better than the compared algorithms for most of the test problems. Moreover, the convergence speed and convergence accuracy of DPCPSO are also significantly improved. Therefore, the algorithm is highly competitive in solving global optimization problems.

A cooperative PSO algorithm for Volt-VAR optimization in smart distribution grids

The penetration level of photovoltaic (PV) systems is set to increase in the following years and already distribution networks (DNs) are straining to overcome the adverse voltage effects caused by reverse power flows and intermittent generation phenomena. Moreover, regular voltage optimization approaches that employ PV inverters as control devices, cannot deal effectively with the escalating dimensionality of the problem. This paper introduces a reactive power optimization method for PV-heavy DNs based on cooperative particle swarm optimization (CPSO). The proposed approach makes use of multiple swarms, each swarm containing a group of design variables that are interrelated with respect to the optimization objective; a community detection algorithm is employed to assign the design variables to the different swarms, by identifying voltage-decoupled zones of the grid. The different swarms cooperate by exchanging information in order to better explore the search space, while still solving the optimization problem as a whole. The feasibility and effectiveness of the proposed scheme are demonstrated through comparisons with other approaches for various load and generation scenarios on the IEEE 123-bus distribution system.

Coordinated Control of Intelligent Fuzzy Traffic Signal Based on Edge Computing Distribution.

With the development of Internet of Things infrastructures and intelligent traffic systems, the traffic congestion that results from the continuous complexity of urban road networks and traffic saturation has a new solution. In this research, we propose a traffic signal control scenario based on edge computing. We also propose a chemical reaction–cooperative particle swarm optimization (CRO-CPSO) algorithm so that flexible traffic control is sunk to the edge. To implement short-term real-time vehicle waiting time prediction as a collaborative judgment of CRO-CPSO, we suggest a traffic flow prediction system based on fuzzy logic. In addition, we introduce a co-factor (collaborative factor) set based on offline learning to take into account the experiential characteristics of intersections in urban road networks for the generation of strategies by the algorithm. Furthermore, the real case of Changsha County is simulated on the SUMO simulation platform. The issue of traffic flow saturation is improved by our method. Compared with other methods, our algorithm enhances the proportions of vehicles that reach their destinations on time by 13.03%, which maximizes the driving experience for drivers. Meanwhile, our algorithm reduces the driving times of vehicles by 25.34%, thus alleviating traffic jams.

The Improved Cooperative Particle Swarm Optimization (ICPSO) with Dynamic Information Adjustment and Controllable Speed and Its Application in Neural Network Optimization

In view of the premature convergence of particle swarm optimization (PSO) that is often caused by the loss of diversity, an improved cooperative PSO (ICPSO) is proposed. The method can dynamically combine the optimum values of the particles themselves, the global particles and the optimum values in groups, use the current optimization stage to dynamically adjust the shared proportion of information and effectively fuse various reference information, which can obtain superior global and local optimization performance. Additionally, to improve the diversity of the algorithm, a dynamic adjustment method using the grouping coefficient [Formula: see text] for the convergence rate is put forward. This method makes the algorithm have a more appropriate convergence rate while improving the convergence precision and enhancing the performance of the algorithm. Finally, the algorithm is used to optimize a neural network. The convergence condition and convergence rate of the algorithm are assessed by theoretical analysis and simulation experiments. The results show that ICPSO has more advantages in its diversity and the adjustment of the convergence rate compared to other related algorithms. Regarding neural network optimization, the training speed and optimization precision of the ICPSO-BP neural network are the highest, which has reached the best and average level of classification accuracy 98.5%, 96.3% for 20 iterations in Iris, and 98.7%, 95.1% in Wine. Its average iteration times score the best in five problems out of six.