Hierarchical Particle Swarm Optimization Research Articles

Efficient Allocation Strategy of Energy Storage Systems in Power Grids Considering Contingencies

This paper addresses the allocation of Energy Storage Systems (ESSs) in power grids by finding the optimal number of ESSs and their locations and sizes with the goal of improving reliability in contingency states. We propose a contingency-sensitivity-based heuristic to decide the optimal number of ESSs and the most effective locations for ESSs support, while circumventing the combinatorial nature of the siting problem. A contingency sensitivity index (CSI) is proposed which represents the impacts of contingencies on the network buses. The CSI ranks the buses, such that those with higher impacts have the privilege for installing ESSs. For the ESSs being fixed, the sizing is formulated as a multi-period AC optimal power flow (OPF) problem and solved by Self-Organizing Hierarchical Particle Swarm Optimization with Time Varying Acceleration Coefficients (HPSO TVAC). The optimal ESSs sizes are selected by minimizing a total cost, which includes investment cost of storage devices, bus voltage deviation cost and average network losses cost. Uncertainties of the renewable generation are accounted by considering different realizations of the generation profiles, then, ESSs sizes are selected by taking the worst case approach. The proposed methodology has been demonstrated on the modified IEEE 30-bus system and Tunisian Grid. The obtained results show the effectiveness of the proposed methodology and the related reliability merits.

Multiswarm heterogeneous binary PSO using win-win approach for improved feature selection in liver and kidney disease diagnosis

Feature selection is a significant preprocessing method in the classification part of an expert system. We propose a new Multiswarm Heterogeneous Binary Particle Swarm Optimization algorithm (MHBPSO) using a Win-Win approach to improve the performance of Binary Particle Swarm Optimization algorithm (BPSO) for feature selection. MHBPSO is a cooperation algorithm, which includes BPSO and its three variants such as Boolean PSO (BoPSO), Self Adjusted Hierarchical Boolean PSO (SAHBoPSO), and Catfish Self Adjusted Hierarchical Boolean PSO (CSAHBoPSO). It performs heterogeneous search on the entire solution space using four different algorithms. Each algorithm shares global best information to another algorithm to select the preeminent global best position. A variant of BoPSO, SAHBoPSO is proposed, in which leaders are identified based on the fitness values for guiding remaining particles and thus forms the hierarchical structure of leaders and its followers. Meanwhile leaders and followers are changed dynamically and consequently changed the hierarchical structure. CSAHBoPSO, which is the version of SAHBoPSO, is also proposed to avoid stagnation in the subsequent iterations. To assess the performance of the proposed algorithms CEC 2013 benchmark functions are employed. In order to validate the proposed algorithms, comparative study with BPSO, BoPSO, VPSO (Mirjalili and Lewis, 2013), HBPSOGA (Wang et al., 2018) and CatfishBPSO (Chuang et al., 2011a) is provided. Experimental results show that SAHBoPSO and CSAHBoPSO algorithm based on BoPSO are promising and significantly better than BPSO, BoPSO, and VPSO. MHBPSO shows the superior improvement in the search ability. In addition, proposed algorithms are tested in the feature selection phase of intelligent liver and kidney cancer diagnostic systems to select elite features from the liver and kidney cancer data. Findings show that the proposed system is proficient in selecting the elite features to classify the tumor as benign or malignant with minimum error rates.

Surrogate-assisted hierarchical particle swarm optimization

Meta-heuristic algorithms, which require a large number of fitness evaluations before locating the global optimum, are often prevented from being applied to computationally expensive real-world problems where one fitness evaluation may take from minutes to hours, or even days. Although many surrogate-assisted meta-heuristic optimization algorithms have been proposed, most of them were developed for solving expensive problems up to 30 dimensions. In this paper, we propose a surrogate-assisted hierarchical particle swarm optimizer for high-dimensional problems consisting of a standard particle swarm optimization (PSO) algorithm and a social learning particle swarm optimization algorithm (SL-PSO), where the PSO and SL-PSO work together to explore and exploit the search space, and simultaneously enhance the global and local performance of the surrogate model. Our experimental results on seven benchmark functions of dimensions 30, 50 and 100 demonstrate that the proposed method is competitive compared with the state-of-the-art algorithms under a limited computational budget.

Cooperative Hierarchical PSO With Two Stage Variable Interaction Reconstruction for Large Scale Optimization.

Large scale optimization problems arise in diverse fields. Decomposing the large scale problem into small scale subproblems regarding the variable interactions and optimizing them cooperatively are critical steps in an optimization algorithm. To explore the variable interactions and perform the problem decomposition tasks, we develop a two stage variable interaction reconstruction algorithm. A learning model is proposed to explore part of the variable interactions as prior knowledge. A marginalized denoising model is proposed to construct the overall variable interactions using the prior knowledge, with which the problem is decomposed into small scale modules. To optimize the subproblems and relieve premature convergence, we propose a cooperative hierarchical particle swarm optimization framework, where the operators of contingency leadership, interactional cognition, and self-directed exploitation are designed. Finally, we conduct theoretical analysis for further understanding of the proposed algorithm. The analysis shows that the proposed algorithm can guarantee converging to the global optimal solutions if the problems are correctly decomposed. Experiments are conducted on the CEC2008 and CEC2010 benchmarks. The results demonstrate the effectiveness, convergence, and usefulness of the proposed algorithm.

Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm

This paper presents an approach for online learning of Takagi–Sugeno (T-S) fuzzy models. A novel learning algorithm based on a Hierarchical Particle Swarm Optimization (HPSO) is introduced to automatically extract all fuzzy logic system (FLS)’s parameters of a T–S fuzzy model. During online operation, both the consequent parameters of the T–S fuzzy model and the PSO inertia weight are continually updated when new data becomes available. By applying this concept to the learning algorithm, a new type T–S fuzzy modeling approach is constructed where the proposed HPSO algorithm includes an adaptive procedure and becomes a self-adaptive HPSO (S-AHPSO) algorithm usable in real-time processes. To improve the computational time of the proposed HPSO, particles positions are initialized by using an efficient unsupervised fuzzy clustering algorithm (UFCA). The UFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modeling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system, enhancing the HPSO’s tuning. The approach is applied to identify the dynamical behavior of the dissolved oxygen concentration in an activated sludge reactor within a wastewater treatment plant. The results show that the proposed approach can identify nonlinear systems satisfactorily, and reveal superior performance of the proposed methods when compared with other state of the art methods. Moreover, the methodologies proposed in this paper can be involved in wider applications in a number of fields such as model predictive control, direct controller design, unsupervised clustering, motion detection, and robotics.

Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition

Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization with respect to two different cost functions. The optimization has been made generic and is applied to each target separately in order to achieve the best possible result for each scenario. The filters obtained using standard particle swarm optimization (PSO) and hierarchal particle swarm optimization algorithms have been compared for various test images with the filter solutions available in the literature. It has been shown that optimization improves the performance of the filters significantly.

AN ADAPTIVE LOCALIZATION SYSTEM USING PARTICLE SWARM OPTIMIZATION IN A CIRCULAR DISTRIBUTION FORM

Tracking the user location in indoor environment becomes substantial issue in recent research High accuracy and fast convergence are very important issues for a good localization system. One of the techniques that are used in localization systems is particle swarm optimization (PSO). This technique is a stochastic optimization based on the movement and velocity of particles. In this paper, we introduce an algorithm using PSO for indoor localization system. The proposed algorithm uses PSO to generate several particles that have circular distribution around one access point (AP). The PSO generates particles where the distance from each particle to the AP is the same distance from the AP to the target. The particle which achieves correct distances (distances from each AP to target) is selected as the target. Four PSO variants, namely standard PSO (SPSO), linearly decreasing inertia weight PSO (LDIW PSO), self-organizing hierarchical PSO with time acceleration coefficients (HPSO-TVAC), and constriction factor PSO (CFPSO) are used to find the minimum distance error. The simulation results show the proposed method using HPSO-TVAC variant achieves very low distance error of 0.19 meter.

Bi-level programming modeling and hierarchical hybrid algorithm for antimissile dynamic firepower allocation problem with uncertain environment

Aiming at the shortcomings of antimissile dynamic firepower allocation (ADFA) researches under uncertain environment, the fuzzy chance-constrained bi-level programming model with complex constraints is proposed by introducing the uncertain programming theory. Firstly, maximization cost-effectiveness ratio and earliest interception time as the upper and the lower objective functions of the model, respectively, are used. In order to close to the battlefield environment, the model constraint includes interception time window, effective damage lower bound and intercept strategy, etc. Secondly, a particle coding scheme and repairing scheme are given with hierarchical structure for multi-constrained bi-level ADFA problem. Furthermore, the improved variable neighborhood PSO algorithm with convergence criterions and the PSO algorithm with doubt and repulsion factor (PSO-DR) are effectively combined. On these bases, the hierarchical hybrid fuzzy particle swarm optimization algorithm is presented with fuzzy simulation technique. Finally, the results of comparison show the proposed algorithm has stronger global searching ability and faster convergence speed, which can effectively solve large-scale ADFA problem and adapt to the requirements of real-time decision.

Markov Chain-Like Model for Prediction Service Based on Improved Hierarchical Particle Swarm Optimization Cluster Algorithm

Since web pages visited by users contain a variety of data resources and the clustering algorithms frequently used for web data do not take the heterogeneous nature into account when processing the heterogeneous data, this paper proposes a new algorithm, namely IHPSOC algorithm, to cluster web log data on the basis of web log mining. Based on particle swarm optimization (PSO), IHPSOC algorithm clusters the web log data through particle swarm iteration. Based on clustering results, this paper establishes Markov chain-like models which create a corresponding Markov chain for users in each different category so as to predict the web resources in users’ need. The results of the experiments show that the proposed model gives better predication.

Nuclear Power Plant Construction Scheduling Problem with Time Restrictions: A Particle Swarm Optimization Approach

In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.

Hierarchical heterogeneous particle swarm optimization: algorithms and evaluations

Particle swarm optimization (PSO) has recently been extended in several directions. Heterogeneous PSO (HPSO) is one of such recent extensions, which implements behavioural heterogeneity of particles. In this paper, we propose a further extended version, Hierarchcial Heterogeenous PSO (HHPSO), in which heterogeneous behaviors of particles are enforced through interactions among hierarchically structured particles. Two algorithms have been developed and studied: multi-layer HHPSO (ml-HHPSO) and multi-group HHPSO (mg-HHPSO). In each HHPSO algorithm, stagnancy and overcrowding detection mechanisms were implemented to avoid premature convergence. The algorithm performance was measured on a set of benchmark functions and compared with performances of standard PSO (SPSO) and HPSO. The results demonstrated that both ml-HHPSO and mg-HHPSO performed well on all testing problems and significantly outperformed SPSO and HPSO in terms of solution accuracy, convergence speed and diversity maintenance. Further computational experiments revealed the optimal frequencies of stagnation and overcrowding detection for each HHPSO algorithm.

Analysis of particle swarm optimization based hierarchical data clustering approaches

Data clustering is one of the most widely used data mining techniques, classifying similar data items into groups on the basis of similarity among the data items. Different issues have been observed while achieving the classification of data into the most suitable grouping. Efficiency of the clustering techniques and accuracy of the resulting groups are two of the main issues. To tackle these issues, recently, optimization based techniques have been used, resulting in enhanced quality of the output and improved efficiency of the clustering process. Swarm Intelligence (SI) is one such technique whose different algorithms have been found effective for this purpose. Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are the two most prominent SI based techniques. In this paper we analyze the use of PSO for data clustering in particular for clustering in a hierarchical manner. We chose PSO based hierarchical techniques, Evolutionary PSO for clustering (EPSO-clustering) and Hierarchical PSO for clustering (HPSO-clustering). Both these techniques work in a hierarchical agglomerative manner, with HPSO-clustering an extension of EPSO-clustering. It combines the properties of hierarchical and partitional clustering and adds SI based optimization to the process. We evaluate our proposed clustering techniques on different benchmark datasets from UCI machine learning data repository as well as real data that we collected locally from a web server. We used inter-cluster and intra-cluster distances, and execution time to measure the performance of our proposed techniques. For evaluation we selected different clustering techniques that were previously used as benchmarks such as k-means, PSO-clustering, Hierarchical Agglomerative Clustering (HAC) and DBSCAN. The results verify that the proposed techniques perform better on the suggested measures against the benchmarks mentioned.

Weather Forecasting using Fuzzy Neural Network (FNN) and Hierarchy Particle Swarm Optimization Algorithm (HPSO)

Background: Weather forecasting has become one of the active research areas due to major scientific and technological problems. An analysis is made to utilize of data mining approaches in weather forecast. Methods: Fuzzy Neural Network (FNN) and Hierarchical Particle Swarm Optimization (HPSO) are used for predicting the weather changes occur in the atmosphere. The FNN uses biological neurons for exact calculation and the neural network adjust their weights by the practice of training. The HPSO is used here for better optimization of weights, also that to improve the overall performance Adaptive Inertia Weight Algorithm (AWA) is proposed. Results: As a result, the total error is reduced with little tolerance which results in accurate weather prediction. The network is trained in such a manner that the model will provide 94% of optimum results. Conclusion: The performances of the proposed algorithm were compared with other existing algorithm using other standard performance metrics which gives best results for the mean weather variables. The results show that the proposed approach is efficient in determining weather forecasting and moreover helps in climate change investigations.

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.

Data-driven minimization of pump operating and maintenance cost

A data-driven model for scheduling pumps in a wastewater treatment process is proposed. The objective is to minimize the cost of pump operations and maintenance. A neural network algorithm is applied to model performance of the pumps using the data collected at a municipal wastewater treatment plant. The discrete-state Markov process is utilized to develop a model of maintenance decisions. The developed pump performance and maintenance models are integrated into a scheduling model. A hierarchical particle swarm optimization algorithm is designed to solve the proposed scheduling model. The concepts developed in this paper are illustrated with two case studies.

Novel Encoding and Routing Balance Insertion Based Particle Swarm Optimization with Application to Optimal CVRP Depot Location Determination

A depot location has a significant effect on the transportation cost in vehicle routing problems. This study proposes a hierarchical particle swarm optimization (PSO) including inner and outer layers to obtain the best location to establish a depot and the corresponding optimal vehicle routes using the determined depot location. The inner layer PSO is applied to obtain optimal vehicle routes while the outer layer PSO is to acquire the depot location. A novel particle encoding is suggested for the inner layer PSO, the novel PSO encoding facilitates solving the customer assignment and the visiting order determination simultaneously to greatly lower processing efforts and hence reduce the computation complexity. Meanwhile, a routing balance insertion (RBI) local search is designed to improve the solution quality. The RBI local search moves the nearest customer from the longest route to the shortest route to reduce the travel distance. Vehicle routing problems from an operation research library were tested and an average of 16% total routing distance improvement between having and not having planned the optimal depot locations is obtained. A real world case for finding the new plant location was also conducted and significantly reduced the cost by about 29%.

Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique

This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non-convex emission constrained economic dispatch (NECED) problems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for non-convex emission constrained economic dispatch (NECED) problems to avoid premature convergence. Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation. Non-convex emission constrained economic dispatch (NECED) problem is obtained by considering both the economy and emission objectives. The performance of the proposed method is demonstrated on two sample test systems. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses.

Hierarchical particle swarm optimizer for minimizing the non-convex potential energy of molecular structure

The stable conformation of a molecule is greatly important to uncover the secret of its properties and functions. Generally, the conformation of a molecule will be the most stable when it is of the minimum potential energy. Accordingly, the determination of the conformation can be solved in the optimization framework. It is, however, not an easy task to achieve the only conformation with the lowest energy among all the potential ones because of the high complexity of the energy landscape and the exponential computation increasing with molecular size. In this paper, we develop a hierarchical and heterogeneous particle swarm optimizer (HHPSO) to deal with the problem in the minimization of the potential energy. The proposed method is evaluated over a scalable simplified molecular potential energy function with up to 200 degrees of freedom and a realistic energy function of pseudo-ethane molecule. The experimental results are compared with other six PSO variants and four genetic algorithms. The results show HHPSO is significantly better than the compared PSOs with p-value less than 0.01277 over molecular potential energy function.

Incorporating domain-specific heuristics in a particle swarm optimization approach to the quadratic assignment problem

We propose two variations on particle swarm optimization (PSO): the use of a heuristic function as an additional biasing term in PSO solution construction; and the use of a local search step in the PSO algorithm. We apply these variations to the hierarchical PSO model and evaluate them on the quadratic assignment problem (QAP). We compare the performance of our method to diversified-restart robust tabu search (DivTS), one of the leading approaches at present for the QAP. Our experimental results, using instances from the QAPLIB instance library, indicate that our approach performs competitively with DivTS.

Real-time stochastic optimal control for traffic signals of multiple intersections

In this paper, a real-time stochastic optimal control method of traffic signal is modified. In addition, H-GA-PSO algorithm is proposed to search optimal traffic signals based on the stochastic model. The H-GA-PSO algorithm is a modified Hierarchical Particle Swarm Optimization (H-PSO) algorithm based on Genetic Algorithm (GA) processing. Finally, the effectiveness of the stochastic optimal control method with H-GA-PSO algorithm is shown through simulations at multiple intersections using a micro-traffic simulator.