Particle Swarm Optimization Strategy Research Articles

Environmental economic dispatch optimization using niching penalized chimp algorithm

The use of traditional fossil fuels has contributed to rapid economic expansion while simultaneously having negative implications, such as increasing global warming and the devastation of the biosphere. Niching Penalized Chimp Optimization (NPChOA) is presented in this research in order to address the Environmental Economic Dispatch (EED) problem. In terms of global search capability, robustness, convergence rate, and durability, the proposed NPChOA outperforms the current ChOA. Following that, a novel constrained handling operator addresses the multi-objective optimization difficulty. The performance of NPChOA is evaluated using an IEEE 30 bus with six generators and a ten-unit system. The result of NPChOA is compared with Grey Wolf Optimizer (GWO), Space Reduction Strategy Particle Swarm Optimization (SRSPSO), Chaotic Biogeography-Based Optimizer (CBBO), Dynamic Population Artificial Bee Colony (DPABC), Modified Bacterial Foraging Algorithm (MBFA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), standard ChOA, Dynamic Levy Flight ChOA (DLF-ChOA), and Weighted ChOA (WChOA) as the most recent modified version of ChOA to confirm its efficiency. NPChOA's evaluation score and convergence rate are outstanding compared to other benchmarks for single and multi-objective optimizations. The NPChOA's efficacy and robustness in dealing with environmental economic dispatch challenges have been demonstrated by discovering a good compromise value.

Protein structure prediction based on particle swarm optimization and tabu search strategy

BackgroundThe stability of protein sequence structure plays an important role in the prevention and treatment of diseases.ResultsIn this paper, particle swarm optimization and tabu search are combined to propose a new method for protein structure prediction. The experimental results show that: for four groups of artificial protein sequences with different lengths, this method obtains the lowest potential energy value and stable structure prediction results, and the effect is obviously better than the other two comparison methods. Taking the first group of protein sequences as an example, our method improves the prediction of minimum potential energy by 127% and 7% respectively.ConclusionsTherefore, the method proposed in this paper is more suitable for the prediction of protein structural stability.

A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization

Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection (LPS) and the search direction used in a velocity update strategy are two crucial factors in PSOs. However, the LPS strategies for most existing PSOs are not so efficient in high-dimensional objective space, mainly due to the lack of convergence pressure or loss of diversity. In order to address these two issues and improve the performance of PSO in high-dimensional objective space, this paper proposes a convergence and diversity guided leader selection strategy for PSO, denoted as CDLS, in which different leader particles are adaptively selected for each particle based on its corresponding situation of convergence and diversity. In this way, a good tradeoff between the convergence and diversity can be achieved by CDLS. To verify the effectiveness of CDLS, it is embedded into the PSO search process of three well-known PSOs. Furthermore, a new variant of PSO combining with the CDLS strategy, namely PSO/CDLS, is also presented. The experimental results validate the superiority of our proposed CDLS strategy and the effectiveness of PSO/CDLS, when solving numerous MaOPs with regular and irregular Pareto fronts (PFs).

Exploring superadditivity of coherent information of noisy quantum channels through genetic algorithms

Machine learning techniques are increasingly being used in fundamental research to solve various challenging problems. Here we explore one such technique to address an important problem in quantum communication scenario. While transferring quantum information through a noisy quantum channel, the utility of the channel is characterized by its quantum capacity. Quantum channels, however, display an intriguing property called super-additivity of coherent information. This makes the calculation of quantum capacity a hard computational problem involving optimization over an exponentially increasing search space. In this work, we first utilize a neural network ansatz to represent quantum states and then apply an evolutionary optimization scheme to address this problem. We find regions in the three-parameter space of qubit Pauli channels where coherent information exhibits this super-additivity feature. We characterised the quantum codes that achieves high coherent information, finding several non-trivial quantum codes that outperforms the repetition codes for some Pauli channels. For some Pauli channels, these codes displays very high super-additivity of the order of 0.01, much higher than the observed values in other well studied quantum channels. We further compared the learning performance of the Neural Network ansatz with the raw ansatz to find that in the three-shot case, the neural network ansatz outperforms the raw representation in finding quantum codes of high coherent information. We also compared the learning performance of the evolutionary algorithm with a simple Particle Swarm Optimisation scheme and show empirical results indicating comparable performance, suggesting that the Neural Network ansatz coupled with the evolutionary scheme is indeed a promising approach to finding non-trivial quantum codes of high coherent information.

A circle chaos random search strategy particle swarm optimization with its application

As a popular optimization, particle swarm optimization has attracted many researcher's attention and also been successfully applied to solve many real world problems. As adopting the strategy of history experience, the convergence rate of PSO is faster than other evolutionary algorithms. But the individuals of PSO are easily be trapped into local optimum, which causes the algorithm could not guaranty confidence of the final results. In this paper, for further enhance the global search ability of PSO, based on comparing different chaos based random strategies, we propose a circle chaos based mutation strategy into PSO algorithm. First, compared with other operators, since the circle chaos based operator could generate more even results, we propose the circle operator based random strategy to balance the global and local search of our PSO algorithm. Second, for attracting individuals to precisely search in a promising region, a new historic experience based elite array is proposed to lead the search direction of population. Then the algorithm could achieve more precise results. For further proving the effectiveness of our algorithm, we apply the algorithm into PID fuzzy logic controller design. Compared with other EA algorithms, the experimental results demonstrate our methods achieve better performance on convergence rate and precise of results.

Research on Fuzzy Control Method of Automobile Antilock Braking System

The automobile Antilock Braking System (ABS) can prevent the wheel from locking by automatically adjusting the brake pressure at a high speed during emergency braking. While improving the braking effect of the car, it can also keep the steering ability of the car and ensure the safety of passengers. The automobile braking process has a strong nonlinearity and uncertainty, so exploring a simple and reliable control strategy is the focus of current research. Based on this, this paper proposes a variable-domain fuzzy proportional integral differential (PID) control strategy using a particle swarm optimization (PSO) algorithm to iteratively find the optimal theoretical domain. First, the PSO strategy is used to obtain the optimal regulation parameters. Then, the dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control, and the PID parameters and variable theory domain expansion factor are optimized by the PSO to increase the utilization degree of fuzzy rules initially set. Finally, compared with the traditional control strategy, the simulation and real vehicle test prove that the proposed system can significantly improve the control accuracy of abs. The proposed system has the advantages of small overshoot, short adjustment time, strong antiinterference ability, and practicability, which greatly improves the tracking performance of the ABS.

A new method for task scheduling in fog‐based medical healthcare systems using a hybrid nature‐inspired algorithm

SummaryThe goal of the healthcare system is to offer a dependable and well‐organized solution for improving human's wellbeing. Examining a patient's history can assist clinicians in considering the patient's wants when building a healthcare system and providing service, resulting in increased patient satisfaction. Thus, healthcare is becoming a more competitive sector. Massive data volume, latency, response time, and security susceptibility are all difficulties resulting from this substantial increase in healthcare systems. As a famous distributed structure, fog computing might thus aid in the resolution of such problems. Processing parts are situated among end devices and cloud components in a fog computing infrastructure and run programs. This design is well suited to real‐time and low‐latency applications, like healthcare systems. Because task scheduling is an NP‐hard optimization issue in fog‐based medical healthcare systems, this work proposes a hybrid genetic algorithm and particle swarm optimization (GA‐PSO) strategy. A powerful single‐objective optimization technique is the GA‐PSO. Individuals in a novel generation are formed in GA‐PSO through mutation and crossover operations in GA‐PSO, which uses a redefined local optimization swarm. Hence, it may avoid local minimums and perform well in global searches. The study's goal in fog‐based medical healthcare systems is to lower the makespan and overall response time. The suggested technique is simulated in MATLAB and compared to the GA and PSO methods. The empirical findings confirmed the improved makespan, making the approach appropriate for medical and real‐time systems applications.

Beamforming Design for Reconfigurable Intelligent Surface via Bayesian Optimization

This letter investigates the transmission design in the multi-user downlink system using the reconfigurable intelligent surface (RIS). The conventional transmission scheme requires the channel estimation by transmitting pilots and feeds back the channel state information (CSI) to the base station, which causes lots of overhead and substantial radio-frequency chains at the RIS. In this letter, we propose a cost-efficient downlink transmission scheme via Bayesian optimization that dispenses with CSI. The beamformer at the BS and the phase rotation at the RIS are jointly designed by minimizing the sum mean square error (MSE). Since the proposed scheme dispenses with CSI, the channel estimation and the CSI feedback are not required, which saves the number of training slots and dispenses with RF chains at the RIS. Moreover, the proposed scheme can be extended to the minimax MSE problem and the energy harvesting problem. Simulation results show that the proposed transmission scheme outperforms the particle swarm optimization scheme.

Stochastic stability analysis of particle swarm optimization with pseudo random number assignment strategy

Particle swarm optimization (PSO) is a population-based optimization method and has been successfully applied to solve many real-world problems. This method belongs to the stochastic optimization method and is mainly driven by two random streams utilized in the stochastic search mechanism, namely, individual (cognition) and social randomness effects. To our best knowledge, no research work has been conducted about the manipulation of the random stream assignment for stochastic search mechanism in the PSO algorithm. In this work, the influences of controlling randomness in the searching scheme of PSO is studied by introducing different pseudo random number (PRN) assignment strategies. The order-1 and order-2 stability analyses for particle dynamics under different PRN assignment strategies are also conducted to understand the influences. Stability analysis is carried out using the stochastic process theory. Our results show that the correlation caused by PRN has no effect on the unbiasedness of the expectation of particle position, but it would reduce or increase the variance of particle dynamics. Second, the convergent conditions of the PSO system under different PRN assignment strategies and the corresponding parameter selection ranges are provided. Finally, an empirical analysis via experimental simulations evaluated by six common swarm diversity measures, eight benchmark test functions, and two parameter tuples is presented.

Numerical treatment for the nonlinear fifth kind of multi-singular differential model: a neuro-swarming approach

In this study, a numerical scheme is proposed for the fifth order (FO) singular differential model (SDM), FO-SDM. The solutions of the singular form of the differential models are always considered difficult to solve and huge important in astrophysics. A neural network study together with the hybrid combination of global particle swarm optimization and local sequential quadratic programming schemes is provided to find the numerical simulations of the FO-SDM. An objective function is constructed using the differential FO-SDM along with the boundary conditions. The correctness of the scheme is observed by providing the comparison of the obtained and exact solutions. The justification of the proposed scheme is authenticated in terms of absolute error (AE), which is calculated in good measures for solving the FO-SDM. The efficiency and reliability of the stochastic approach are observed using the statistical performances to solve the FO-SDM.

Single-particle optimization for network embedding preserving both local and global information

As an emerging topic, network embedding aims to convert the complicated network information into a low-dimensional vector space that largely preserves the network topology and effectively benefits various network tasks. Most existing methods mainly focus on the sketchy preservation of local or global network information, which greatly limits their application scenarios. In this paper, we propose a single-particle optimization algorithm for network embedding problems (SPO-NE) to carefully preserve both the local properties of nodes and the global community structure of networks. Based on the evolutionary scheme of particle swarm optimization, SPO-NE innovatively uses a single particle with two well-designed strategies for optimization. The multi-level learning strategy is devoted to comprehensively saving the local properties of nodes from three aspects of similarity, degree, and community, while the transfer strategy serves to help the nodes located at the community boundary to obtain better community labels. In experiments, SPO-NE is compared with several state-of-the-art baselines on both real-world and synthetic networks. The results indicate the superiority of SPO-NE on various network tasks. Specifically, SPO-NE can outperform the best baselines by over 10% on five datasets in community detection. With only 10% training samples in node classification, SPO-NE can improve the best baselines by 7–10% on five datasets.

A novel design approach for indoor environmental quality based on a multiagent system for intelligent buildings in a smart city: Toward occupant's comfort

AbstractIn a smart city, energy usage for sustaining indoor environmental quality (IEQ) and occupant comfort levels frequently clash. This research aims to design a novel approach to solve the conflict between the use of power and the comfort of the citizens. A multi‐purpose problem for minimum consumption of energy as well as maximum occupant's ease within the energy‐efficient construction for smart city as a future has been formulated. Across suggested design method, three variable namely temperature (°C), illumination level (lux), as well as Carbon dioxide (CO2‐ppm) have been undertaken for occupant's comfort. The Crisscross Search Particle Swarm Optimization (CSPSO), a revolutionary solution for the design method, has been applied to search for the optimal value of environmental factors (temperature, light level, CO2) in relation to user‐set preferences. In the heuristic method, the augmented particle swarm optimization strategy updates an initial solution before updating the best local solutions through length and breadth crossover operators. The CSPSO approach search explores the search space in all dimensions and reduces the stagnation trouble by through length and breadth crossover operators. The temperature, lighting, CO2 sensors data, and user‐defined parameters are all inputs to the CSPSO algorithm, and the optimized parameters are the outputs of the optimization method. Such optimized parameters will become an input to the fuzzy controllers that adjust various actuators' status according to the users' comfort. Utilizing the CSPSO technique, 22.74% energy of 625.372 kWhr energy was saved during a day, and the comfort level has increased from 0.51692 to 0.75685. Therefore, the proposed novel design approach is acceptable in managing building energy consumption by achieving maximum comfort.

Optimal location and sizing of various DG units in real distribution substation using heuristic approach

PurposeThe purpose of the study is distributed generation planning in a radial delivery framework to identify an appropriate location with a suitable rating of DG units energized by renewable energy resources to scale back the power loss and to recover the voltage levels. Though several algorithms have already been proposed through the target of power loss reduction and voltage stability enhancement, further optimization of the objectives is improved by using a combination of heuristic algorithms like DE and particle swarm optimization (PSO).Design/methodology/approachThe identification of the candidate buses for the location of DG units and optimal rating of DG units is found by a combined differential evolution (DE) and PSO algorithm. In the combined strategy of DE and PSO, the key merits of both algorithms are combined. The DE algorithm prevents the individuals from getting trapped into the local optimum, thereby providing efficient global optimization. At the same time, PSO provides a fast convergence rate by providing the best particle among the entire iteration to obtain the best fitness value.FindingsThe proposed DE-PSO takes advantage of the global optimization of DE and the convergence rate of PSO. The different case studies of multiple DG types are carried out for the suggested procedure for the 33- and 69-bus radial delivery frameworks and a real 16-bus distribution substation in Tamil Nadu to show the effectiveness of the proposed methodology and distribution system performance. From the obtained results, there is a substantial decrease in the power loss and an improvement of voltage levels across all the buses of the system, thereby maintaining the distribution system within the framework of system operation and safety constraints.Originality/valueA comparison of an equivalent system with the DE, PSO algorithm when used separately and other algorithms available in literature shows that the proposed method results in an improved performance in terms of the convergence rate and objective function values. Finally, an economic benefit analysis is performed if a photo-voltaic based DG unit is allocated in the considered test systems.

A continuous finite-time convergence fixed-lag FIR smoother using multiple IIR filters

In this paper, we propose a continuous finite-time convergence finite impulse response (FIR) fixed-lag smoother using multiple, or more than two, computationally efficient IIR filters. We describe the optimal design to improve and further optimize an existing scheme based on two IIR filters. Multiple IIR filters are utilized to minimize the estimation error variance of the proposed smoother under the condition that its estimate converges to a real state in a finite time. As the number of adopted IIR filters increases, the proposed smoother improves and its performance approaches that of the heavy computational fixed-lag minimum variance FIR smoother. By choosing the appropriate number of IIR filters, we can balance the trade-off between improved accuracy and increased implementation costs. To realize the optimal design of IIR filters with the limited number of IIR filters, their gains are determined using a particle swarm optimization scheme. Numerical examples are used to show that with an increasing number of IIR filters, the estimation error variance decreases monotonically while guaranteeing finite-time convergence.

Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction

In the investigation, hybrid Gradient Boosting (GB) with four optimization techniques named Simulated Annealing (SA), Random Restart Hill Climbing (RRHC), Particle Swarm Optimization (PSO) and Evolution Strategy Optimization (ESO) are used to develop machine learning models for predicting unconfined compressive strength (UCS) of stabilized soils based on initial soil properties, mix design and effective compaction. The results show that the hybrid model GB_PSO can successfully predict the UCS of stabilized soils with high performance R2 = 0.9655, RMSE = 0.1633 MPa for testing part. The feature importance analyses are performed with using GB_PSO based Permutation Importance of Sklearn library and Shapley Additive Explanations (SHAP value), which reveal that maximum dry density (MDD), linear shrinkage (LS), and Gravel content are the most important characteristics in the UCS prediction performance of the GB_PSO model. The partial dependence plot (PDP) analysis consisting of PDP 1D and PDP 2D show respectively the impact way of each feature on the UCS and the interactions between initial soil properties, mix design and effective compaction in the UCS improvement of stabilized soil. The PDP analysis show that (i) the LS is the most important criterion in determining the success of improving the UCS of stabilized soil, (ii) the predominance of effective compaction over other conditions on the improvement of UCS. The present investigation can be considered to be new insights for better understanding process of stabilized soil and UCS improvement.

An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysis

Aspect level sentiment analysis is a fine-grained task in sentiment analysis. It extracts aspects and their corresponding sentiment polarity from opinionated text. The first subtask of identifying the opinionated aspects is called aspect extraction, which is the focus of the work. Social media platforms are an enormous resource of unlabeled data. However, data annotation for fine-grained tasks is quite expensive and laborious. Hence unsupervised models would be highly appreciated. The proposed model is an unsupervised approach for aspect term extraction, a guided Latent Dirichlet Allocation (LDA) model that uses minimal aspect seed words from each aspect category to guide the model in identifying the hidden topics of interest to the user. The guided LDA model is enhanced by guiding inputs using regular expressions based on linguistic rules. The model is further enhanced by multiple pruning strategies, including a BERT based semantic filter, which incorporates semantics to strengthen situations where co-occurrence statistics might fail to serve as a differentiator. The thresholds for these semantic filters have been estimated using Particle Swarm Optimization strategy. The proposed model is expected to overcome the disadvantage of basic LDA models that fail to differentiate the overlapping topics that represent each aspect category. The work has been evaluated on the restaurant domain of SemEval 2014, 2015 and 2016 datasets and has reported an F-measure of 0.81, 0.74 and 0.75 respectively, which is competitive in comparison to the state of art unsupervised baselines and appreciable even with respect to the supervised baselines.

Location Optimization of VTS Radar Stations Considering Environmental Occlusion and Radar Attenuation

Waterway traffic monitoring is an important content in waterway traffic management. Taking into account that the number of monitored water areas is growing and that waterway traffic management capabilities are insufficient in the current situation in China, this paper investigates the location optimization of the vessel traffic service (VTS) radar station. During the research process, radar attenuation and environmental occlusion, as well as variable coverage radius and multiple covering are all considered. In terms of the radar attenuation phenomenon in the propagation process and obstacles such as mountains and islands in the real world, judgment and evaluation methods in a three-dimensional space are proposed. Moreover, a bi-objective mathematical model is then developed, as well as a modified adaptive strategy particle swarm optimization algorithm. Finally, a numerical example and a case are given to verify the effectiveness of the proposed methods, model, and algorithm. The results show the methods, model, and algorithm proposed in this paper can solve the model efficiently and provide a method to optimize the VTS radar station location in practice.

A Hybrid Approach for Task Scheduling Based Particle Swarm and Chaotic Strategies in Cloud Computing Environment

This paper presents a hybrid approach based discrete Particle Swarm Optimization (PSO) and chaotic strategies for solving multi-objective task scheduling problem in cloud computing. The main purpose is to allocate the summited tasks to the available resources in the cloud environment with minimum makespan (i.e. schedule length) and processing cost while maximizing resource utilization without violating Service Level Agreement (SLA) among users and cloud providers. The main challenges faced by Particle Swarm Optimization (PSO) when used to solve scheduling problems are premature convergence and trapping into local optimum. This paper presents an enhanced Particle Swarm Optimization algorithm hybridized with Chaotic Map strategies. The proposed approach is called Enhanced Particle Swarm Optimization based Chaotic Strategies (EPSOCHO) algorithm. Our proposed approach suggests two Chaotic Map strategies: sinusoidal iterator and Lorenz attractor to enhanced PSO algorithm in order to get good convergence and diversity for optimizing the task scheduling in cloud computing. The proposed approach is simulated and implemented in Cloudsim simulator. The performance of the proposed approach is compared with the standard PSO algorithm, the improved PSO algorithm with Longest job to fastest processor (LJFP-PSO), and the improved PSO algorithm with minimum completion time (MCT-PSO) using different sizes of tasks and various benchmark datasets. The results clearly demonstrate the efficiency of the proposed approach in terms of makespan, processing cost and resources utilization.

Material Innovation of Animation Modeling Design Based on Visual Symbol Theory

Character modeling is an important link in animation design, and it is one of the keys to determining whether an animation is successful or not, as well as determining the value of animation and related industries. This paper examines the role of material innovation in the development of animation art, as well as the impact of various materials on animation production technology and design style, using the visual symbol theory as a guide. GA (genetic algorithm) is used in the comic design process to improve the fidelity and efficiency of the artwork. GA performs data training of multiple components; selection and crossover operations are carried out continuously according to the fitness threshold; and the particle swarm optimization strategy is added. This algorithm increases the rate of design reuse and efficiency, as well as the ability to determine the best component combination scheme.

Nonlinear directed information flow estimation for fNIRS brain network analysis based on the modified multivariate transfer entropy

Due to the specific advantages of functional near-infrared spectroscopy (fNIRS), fNIRS-based brain network research, especially directed causal coupling analysis, is emerging gradually. The transfer entropy (TE) method has proved its superiority and effectiveness in identifying directed information flow in the brain network, which helps us further understand the neurodynamic mechanisms. However, there remain three inherent defects in TE: (1) lack of multivariable effect, (2) poor robustness, and (3) dimensional curse in the high dimensional system. This study introduces a novel multivariate transfer entropy based on the nonuniform embedding, abbreviated “MTENUE,” to solve the above limits and construct the whole brain's directed coupling network. The MTENUE thoroughly considers the potential influence of remaining confounding variables in conditional mutual information estimation, guaranteeing its applicability in a multivariable system. By combining low-dimensional approximation and search strategy of particle swarm optimization, a novel nonuniform embedding (NUE) algorithm was introduced to establish the MTENUE methodology. Using a well-known Hénon map model, we carried out a series of numerical simulations to verify the performances of MTENUE and compared them with the traditional TE methods. The numerical simulations prove that the proposed MTENUE allows directional coupling estimation for a high dimensional, nonlinear, nonstationary system. The MTENUE achieves the best robustness to data length and noise. Meanwhile, it presents the best linearity for coupling parameters “C,” with its higher fitting coefficients of determination (R-square) than other methods. Moreover, by avoiding the dimensional curse, the MTENUE achieves the highest accuracy and specificity, which are higher than 98%; in contrast, the traditional methods are less than 80%. Finally, we applied the proposed method to the actual fNIRS research. The results demonstrate a typical information flow pattern changes during the working memory tasks, which is expected to provide new insights into the underlying neural dynamics in the cognitive process.