Cooperative Particle Swarm Optimization Research Articles

Chaos Cooperative Particle Swarm Optimization Based Water Level Control for Nuclear Steam Generator

The Stability of SG (Steam Generator) water level plays an important role in the safety of nuclear power plants, but it is difficult to tune the parameters of water level PID controller. A proposed novel algorithm, CCPSO (chaos cooperative particle swarm optimization), is used for tuning PID controller parameters. The (chaos particle swarm optimization)CPSO algorithm has the ability to avoid falling into local minimum and the (cooperative particle swarm optimization)CPSO-S k has fast convergence in certain functions, so CCPSO algorithm is proposed to utilize the advantages of CPSO and CPSO-S k . Therefore, half of the particles are updated in the CPSO-S k , and the other half are updated in the CPSO. The information exchange of the optimal solutions obtained after the end of each iteration is the performance of CPSO-Sk and CPSO collaboration.The simulation results: compared with the PID controller whose parameters are tuned by ZN method, CCPSO show smaller overshoot, better stability, and shorter adjustment time. The simulation results show that the proposed method is effective for tuning PID parameters.

Modified Cooperative Subchannel Allocation Algorithms and PSO Based Power Allocation for an Alamouti Decode and Forward Relaying Protocol in Multiuser OFDMA Systems

The present work is a discussion on the performance analysis of Modified Cooperative Subchannel Allocation (CSA) Algorithms which is used in Alamouti Decoded and Forward (Alamouti DF) Relaying Protocol for wireless multi-user Orthogonal Frequency Division Multiplexing Access (OFDMA) systems. In addition, the performance of approximate Symbol Error Rate (SER) for the Alamouti DF Relaying Protocol with the Cooperative Maximum Ratio Combining Technique (C-MRC) is analyzed and compared with SER upper bound. The approximate SER is asymptotically tight bound at higher Signal-to-Noise Ratio (SNR). From the asymptotic tight bound approximate SER, Particle Swarm Optimization (PSO) based Power Allocation (PA) is determined for the Alamouti DF Relaying Protocol. The simulation results suggested that the Modified Throughput based Subchannel Allocation Algorithm achieved an improved throughput of 6% to 33% compared to that of existing cooperative diversity protocol. Further, the Modified Fairness based Subchannel Allocation Algorithm rendered fairness of 7.2% to 17% among the multiuser against the existing cooperative diversity protocol.

Formalized model and analysis of mixed swarm based cooperative particle swarm optimization

Natural phenomenon of mixed flocks indicates such principles as cooperation and social symbiosis among various species. Inspired by the organization and collective intelligence of natural mixed flocks, a mixed swarm based particle swarm optimization (MCPSO) is proposed to efficiently handle the trade-off between the global and local search in PSO. The approach divides all particles into two species, i.e., exploration species and exploitation species. Exploration species undertakes the coarse search in the solution space to discover new potential area, while the exploitation species is instructed accordingly to conduct fine search in its activity territory. Information sharing plays a crucial role between the two species, through the cooperative mechanism, not only does MCPSO avoid the optimum missed in a coarse search, but also it significantly saves void fine search. The proposed MCPSO is validated with well-known benchmarks confirming that the cooperative mixed swarm is an effective model for the swarm based searching, further proving that MCPSO is a robust global technique for complex optimization problems.

Parallel Implementations of the Cooperative Particle Swarm Optimization on Many-core and Multi-core Architectures

Particle swarm optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. PSO is stochastic yet very robust. Nevertheless, real-world optimizations require a high computational effort to converge to a good solution for the problem. In general, parallel PSO implementations provide good performance. However, this depends heavily on the parallelization strategy used as well as the number and characteristics of the exploited processors. In this paper, we propose a cooperative strategy, which consists of subdividing an optimization problem into many simpler sub-problems. Each of these focuses on a distinct subset of the problem dimensions. The optimization work for all the selected sub-problems is done in parallel. We map the work onto four different parallel high-performance multiprocessors, which are based on multi- and many-core architectures. The performance of the strategy thus implemented is evaluated for four well known benchmark functions with high-dimension and different complexity. The obtained speedups are compared to that yielded by a serial PSO implementation.

Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches—Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.

Efficient Cooperative Particle Swarm Optimization for TSK-Type Neural Fuzzy Systems and Its Classification Applications

This study proposes a cooperative particle swarm optimization (CPSO) to optimize the parameters of the TSK-type neural fuzzy system (TNFS) for classification applications. The proposed CPSO uses cooperative behavior among multiple subswarms to decompose the neural fuzzy systems into rule-based subswarms, and each particle within each subswarm evolves by a specific particle swarm optimization (PSO) separately. Therefore, the CPSO can accelerate the search and increase global search capacity. Finally, the TNFS with CPSO (TNFS-CPSO) is adopted in several classification applications. Experimental results demonstrate that the proposed TNFS-CPSO method has a higher accuracy rate and a faster convergence rate than the other methods.

Parallel and Cooperative Particle Swarm Optimizer for Multimodal Problems

Although the original particle swarm optimizer (PSO) method and its related variant methods show some effectiveness for solving optimization problems, it may easily get trapped into local optimum especially when solving complex multimodal problems. Aiming to solve this issue, this paper puts forward a novel method called parallel and cooperative particle swarm optimizer (PCPSO). In case that the interacting of the elements inD-dimensional function vectorX=[x1,x2,…,xd,…,xD]is independent, cooperative particle swarm optimizer (CPSO) is used. Based on this, the PCPSO is presented to solve real problems. Since the dimension cannot be split into several lower dimensional search spaces in real problems because of the interacting of the elements, PCPSO exploits the cooperation of two parallel CPSO algorithms by orthogonal experimental design (OED) learning. Firstly, the CPSO algorithm is used to generate two locally optimal vectors separately; then the OED is used to learn the merits of these two vectors and creates a better combination of them to generate further search. Experimental studies on a set of test functions show that PCPSO exhibits better robustness and converges much closer to the global optimum than several other peer algorithms.

A hybrid cooperative cuckoo search algorithm with particle swarm optimisation

This paper proposes an improved hybrid cooperative algorithm that combines cooperative cuckoo search algorithm and particle swarm optimisation, called HCCSPSO. The cooperative co-evolutionary framework is applied to cuckoo search algorithm to implement dimensional cooperation. The particle swarm optimisation algorithm, viewed as a cooperative component, is embedded in the back of the cuckoo search algorithm. During iteration, the best solution obtained by the previous cooperative component is randomly embedded in the last one to avoid the pseudo-minima produced by the previous one, while the subcomponents of best solution from the last cooperative component are also randomly planted in the subcomponents of the previous one. The results of experimental simulations demonstrate the improvement in the efficiency and the effect of the cooperation strategy, and the promising of HCCSPSO.

A novel multi-subpopulation cooperative particle swarm optimisation

The basic particle swarm optimisation PSO algorithm is easily trapped in local optima. To deal with this problem, a multi-subpopulation cooperative particle swarm optimisation MCPSO is presented. In the proposed algorithm, the particles are divided into several normal subpopulations and an elite subpopulation. The selected individuals in normal subpopulation are memorised into the elite subpopulation, and some individuals in normal subpopulation are replaced by the best particles from the elite subpopulation. Different subpopulation adopts different evolution model. This strategy can maintain the diversity of the population and avoid the premature convergence. The performance of the proposed algorithm is evaluated by testing on standard benchmark functions. The experimental results show that the proposed algorithm has better convergent rate and high solution accuracy.

An endocrine cooperative particle swarm optimization algorithm for routing recovery problem of wireless sensor networks with multiple mobile sinks

In the wireless sensor networks with multiple mobile sinks, the movement of sinks or failure of sensor nodes may lead to the breakage of the existing routes. In most routing protocols, the query packets are broadcasted to repair a broken path from source node to sink, which cause significant communication overhead in terms of both energy and delay. In order to repair broken path with lower communication overhead, we propose an efficient routing recovery protocol with endocrine cooperative particle swarm optimization algorithm (ECPSOA) to establish and optimize the alternative path. In the ECPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is enriched by the endocrine mechanism, which can enhance the capacity of global search and improve the speed of convergence and accuracy of the algorithm. By using this method, the alternative path from source nodes to the sink with the optimal QoS parameters can be selected. Simulation results show that our routing protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

High Speed Train Rescheduling Based on a Cooperative Particle Swarm Optimization Algorithm

To solve the high speed train rescheduling problem, a mathematical model is constructed and a cooperative particle swarm optimization algorithm is proposed. The train rescheduling model takes the on-schedule rate and total delayed time of all the trains’ arrival at the related stations in the railway dispatching section as the optimization objectives. Minimal intervals between arrivals and departures, arrival and departure tracks, minimal running time in the railway sub-section are considered as the operation constraints. We design two swarms to ensure the global optimum and local optimum respectively when improving the computing performance of particle swarm optimization algorithm. The computing case is based on the actual data from Shanghai-Hangzhou high speed railway. Computing results validate the model and confirm the efficiency of the algorithm. The method proposed in this paper can improve the on-schedule rate of the trains and reduce the total delayed time when rescheduling trains on railway dispatching sections, which can be embedded in the novel train dispatching system.

Dynamic-context cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation

This paper proposes a dynamic-context cooperative quantum-behaved particle swarm optimization algorithm. The proposed algorithm incorporates a new method for dynamically updating the context vector each time it completes a cooperation operation with other particles. We first explain how this leads to enhanced search ability and improved optimization over previous methods, and demonstrate this empirically with comparative experiments using benchmark test functions. We then demonstrate a practical application of the proposed method, by showing how it can be applied to optimize the parameters for Otsu image segmentation for processing medical images. Comparative experimental results show that the proposed method outperforms other state-of-the-art methods from the literature.

Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems

This paper proposes an information sharing mechanism (ISM) to improve the performance of particle swarm optimization (PSO). The ISM allows each particle to share its best search information, so that all the other particles can take advantage of the shared information by communicating with it. In this way, the particles could enhance the mutual interaction with the others sufficiently and heighten their search ability greatly by using the search information of the whole swarm. Also, a competitive and cooperative (CC) operator is designed for a particle to utilize the shared information in a proper and efficient way. As the ISM share the search information among all the particles, it is an appropriate way to mix up information of the whole swarm for a better exploration of the landscape. Therefore, the competitive and cooperative PSO with ISM (CCPSO-ISM) is capable to prevent the premature convergence when solving global optimization problems. The satisfactory performance of CCPSO-ISM is evaluated by comparing it with other variants of PSOs on a set of 16 global optimization functions. Moreover, the effectiveness and efficiency of CCPSO-ISM is validated under different test environments such as biased initialization, coordinate rotated and high dimensionality.

Dynamic partition search algorithm for global numerical optimization

This paper presents a novel evolutionary algorithm entitled Dynamic Partition Search Algorithm (DPSA) for global optimization problems with continuous variables. The DPSA is a population-based stochastic search algorithm in nature, which mainly consists of initialization process and evolution process. In the initialization process, the DPSA randomly generates an initial population of members from a specific search space and finds a leader. In the evolution process, the DPSA applies two groups to balance exploration ability and exploitation ability, in which one group is in charge of exploring new region via a dynamic partition strategy, and the other group relies on Cauchy distributions to exploit the region around the best member. Later, numerical experiments are conducted for 24 classical benchmark functions with 100, 1000 or even 10000 dimensions. And the performance of the proposed DPSA is compared with a state-of-the-art cooperative coevolving particle swarm optimization (CCPSO2), and two existing differential evolution (DE) algorithms. The experimental results show that DPSA has a better performance than the algorithms used for comparison, especially for high dimensional optimization problems. In addition, the numerical computational results also demonstrate that the DPSA has good scalability, and it is an effective evolutionary algorithm for solving large-scale global optimization problems.

A cooperative co-evolutionary particle swarm optimiser based on a niche sharing scheme for the flow shop scheduling problem under uncertainty

The flow shop scheduling problem based on ideal and precise conditions has been a focus of considerable research since the first easy scheduling problem was formulated. In reality, some uncertain factors always restrict the scheduling optimisation problem. In this paper, taking uncertain processing time as an example, we use generalised rough sets theory to transform the rough flow shop scheduling model into the precise scheduling model. We adopt a cooperative co-evolutionary particle swarm optimisation algorithm based on a niche sharing scheme (NCPSO) to minimise the makespan in comparison with the particle swarm optimiser (PSO) and co-evolution particle swarm optimiser (CPSO) algorithms. The new algorithm is characterised by a strengthening of the ability to reserve excellent particles and searching the optimal solution. Experimental results show that the new algorithm is more effective and efficient than the others.

Mean Square Convergent Particle Swarm Optimization Based on Fuzzy Migratory Operator

This paper proposes a novel cooperative particle swarm PSO (particle swarm optimization)algorithm, which makes the use of the property of the fuzzy migratory operator to achieve the optimization performance. To avoid the drawback of the possibility of being trapped in local optimum, the proposed method uses a migrate-based strategy to control the diversity of the swarm. During the iteration aspect of the algorithm, the comprehensive fuzzy evaluation method is employed to evaluate the diversity. Furthermore, the fuzzy migratory operator is then used to remove bad particles once the diversity is far from ideal. Moreover, we have proven that the proposed migrate strategy is a mean square convergence. The experimental results conducted on three benchmark functions also proved that the proposed method is superior to that of classical PSO and conventional cooperative PSO, where the comparison has been based primarily upon the global optimality, solution accuracy and diversity value.

Multi-contour registration based on feature points correspondence and two-stage gene expression programming

Abstract Image registration is a fundamental task in 3D reconstruction from an image sequence. Although this topic has been studied for decades, a general, robust, and automatic image registration method is rare, and most existing image registration methods are designed for a particular application. In this paper, image registration is treated as a formula discovery problem. A novel contour registration pipeline was proposed based on a foot-point-based feature point correspondence algorithm and a two-stage evolutionary algorithm. Our proposal has three objectives. First, we introduce a novel feature point extraction method that uses estimation of the curvature and the support region for every contour in the floating image. Second, we approximate the reference contour using a Gaussian mixture model (GMM) continuous optimization algorithm followed by an order-preserved foot-point detection method used to extract the feature points that correspond to the feature points of the floating contours. Third, we propose a hybrid evolutionary algorithm used to identify the registration formula for the reference image and the floating image. The hybrid evolutionary algorithm is a two-stage algorithm based on gene expression programming (GEP) and the improved cooperative particle swarm optimizer (CPSO). The optimal or near-optimal structure is accomplished using the GEP algorithm, and the parameters embedded in the structure are optimized by an opposition based learning (OBL)-based cooperative particle swarm optimizer (CPSO). Compared with other non-rigid registration methods, the developed registration pipeline produces competitive results with high accuracy.

Batch Task Scheduling-Oriented Optimization Modelling and Simulation in Cloud Manufacturing

Batch task scheduling in cloud manufacturing has dynamic, real-time characteristic and the presence of big data concurrency and exchange requirements, while traditional workshop tasks scheduling models and algorithms can’t fit. In order to effectively save the time and reduce the cost of workshop production, an optimization model is put forward at first. And then improved cooperative particle swarm optimization algorithm with fast convergence and strong ability to avoid local optimization is used to solve the tasks scheduling problems. At last simulation experiment analysis results prove its effectiveness. (Received, processed and accepted by the Chinese Representative Office.)

Gravity-based particle swarm optimization with hybrid cooperative swarm approach for global optimization

Premature convergence has been recognized as one of the major drawbacks of particle swarm optimization PSO algorithms. In particular, the lack of diversity in PSO performance is an essential cause that commonly results in high susceptibility to prematurely converge to local optima especially in complex multimodal problems with high dimensionality. This paper presents a new PSO operational strategy based on gravity concept to address the aforementioned drawback and it is named as gravity-based particle swarm optimizer GPSO. In addition, GPSO is further modified by adopting the cooperation concept of the conventional cooperative particle swarm optimizer CPSO to develop an extended version of GPSO called cooperative gravity-based particle swarm optimizer CGPSO. Simulation results manifest that CGPSO performs satisfactorily on unimodal functions while it generally performs better on multimodal functions than GPSO and other conventional PSO variants. Finally, the proposed GPSO and CGPSO are applied into the problem of optimizing the detection performance of soft decision fusion for cooperative spectrum sensing in cognitive radio networks. For this problem, computer simulations show that the proposed CGPSO outperforms all other PSO variants in terms of quality of solutions whereas GPSO is found to be the best when the computational cost is taken into account.

Cooperative quantum-behaved particle swarm optimization with dynamic varying search areas and Lévy flight disturbance.

This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles' activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem.