Social Learning Particle Swarm Optimization Research Articles

Surrogate-assisted constrained hybrid particle swarm optimization algorithm for propane pre-cooled mixed refrigerant LNG process optimization

The propane pre-cooled mixed refrigerant (C3MR) process is one of the most widely used and efficient natural gas liquefaction processes. However, optimization of this process involves various design and operational constraints, complex thermodynamics, and hence nonconvex in nature. Therefore, it's computationally expensive, often involving trial and error approaches. Existing optimization algorithms often possess flaws such as insufficient accuracy and premature convergence, leading to suboptimal solutions that may violate essential process constraints. This article proposes a novel method to optimize the C3MR process that handles the computational complexity, meets the constraints, and achieves feasible solutions quickly. The proposed method includes modified feasibility-based constraint handling and radial basis function network-assisted surrogate modeling and optimization, where the power consumption is optimized using a hybrid of the particle swarm optimization (PSO) and social learning PSO algorithm. The proposed algorithm achieves the optimum power consumption (121109.31 kW), which is 21.5 % less than the base case (154200 kW). The optimization results are compared with similar optimization algorithms, where the proposed algorithm outperformed the other algorithm regarding optimal solution, convergence, and speed. The results from this study are compared to previous studies from the literature, which validate the accuracy and applicability of the proposed method.

A multi-strategy surrogate-assisted social learning particle swarm optimization for expensive optimization and applications

Evolutionary algorithms (EAs) require extensive fitness evaluations, which constitutes a barrier to solving computationally complex problems. In contrast, surrogate-assisted evolutionary algorithms (SAEAs) have the potential to solve complex expensive optimization problems. This paper proposes a surrogate-assisted social learning particle swarm optimization (SASLPSO) to handle expensive optimization problems. An adaptive local surrogate (ALS) strategy introduced in SASLPSO is introduced to accurately fit the landscape near the global optimum. ALS consists of two layers of surrogate, the part of the particles closest to the optimal particle and the closest to the optimal particle among the particles that have been eliminated historically. The proposed SASLPSO effectively combines global surrogate (GS) and adaptive local surrogate (ALS) to balance global exploration and local exploitation. Furthermore, a novel random group-based pre-screening (RGBPS) strategy is proposed to screen promising particles for real function evaluation. The proposed SASLPSO is compared with four other state-of-the-art SAEAs on 30D, 50D, and 100D benchmark functions. In addition, the significance of the SASLPSO algorithm was also verified using the Wilcoxon rank test. The test results on the benchmark function show that the SASLPSO algorithm performs better than other comparison algorithms, especially when dealing with high-dimensional benchmark functions. To further validate the effectiveness of the SASLPSO algorithm in solving expensive optimization problems, it was also applied to feature selection problems and real-world engineering problems. In addition, in the real application of node deployment in 3D wireless sensor networks, the highest coverage rate of the SASLPSO algorithm can reach 99.96%, confirming its performance advantages in solving real application problems. Finally, in the application of network intrusion detection, SASLPSO has shown more advantages in multiple metrics, proving its versatility.

Eucalyptus Plantation Area Extraction Based on SLPSO-RFE Feature Selection and Multi-Temporal Sentinel-1/2 Data

An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learning has become an important method for eucalyptus plantation classification, but there are still some challenges in feature selection. This study proposes a feature selection method that combines multi-temporal Sentinel-1 and Sentinel-2 data with SLPSO (social learning particle swarm optimization) and RFE (Recursive Feature Elimination), which reduces the impact of information redundancy and improves classification accuracy. Specifically, this paper first fuses multi-temporal Sentinel-1 and Sentinel-2 data, and then carries out feature selection by combining SLPSO and RFE to mitigate the effects of information redundancy. Next, based on features such as the spectrum, red-edge indices, texture characteristics, vegetation indices, and backscatter coefficients, the study employs the Simple Non-Iterative Clustering (SNIC) object-oriented method and three different types of machine-learning models: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM) for the extraction of eucalyptus plantation areas. Each model uses a supervised-learning method, with labeled training data guiding the classification of eucalyptus plantation regions. Lastly, to validate the efficacy of selecting multi-temporal data and the performance of the SLPSO–RFE model in classification, a comparative analysis is undertaken against the classification results derived from single-temporal data and the ReliefF–RFE feature selection scheme. The findings reveal that employing SLPSO–RFE for feature selection significantly elevates the classification precision of eucalyptus plantations across all three classifiers. The overall accuracy rates were noted at 95.48% for SVM, 96% for CART, and 97.97% for RF. When contrasted with classification outcomes from multi-temporal data and ReliefF–RFE, the overall accuracy for the trio of models saw an increase of 10%, 8%, and 8.54%, respectively. The accuracy enhancement was even more pronounced when juxtaposed with results from single-temporal data and ReliefF-RFE, at increments of 15.25%, 13.58%, and 14.54% respectively. The insights from this research carry profound theoretical implications and practical applications, particularly in identifying and extracting eucalyptus plantations leveraging multi-temporal data and feature selection.

A data-driven fuzzy system for the automatic determination of fuzzy set type based on fuzziness

Existing research mainly uses prior knowledge to set all fuzzy sets in a fuzzy system to the same type. In data-driven fuzzy modeling, automatically determining the type of fuzzy set is still a challenging problem. In this paper, a novel data-driven design method is proposed to automatically determine the types of fuzzy sets (type-1 fuzzy sets (T1-FSs) or interval type-2 fuzzy sets (IT2-FSs)) based on fuzziness. In the hybrid-type fuzzy system (HTFS), the fuzzy set type is determined by fuzziness to improve the performance of the fuzzy system, and the interpretability of the fuzzy system is determined by the integrity, distinguishability, and redundancy of the fuzzy sets. First, fuzzy clustering based on fuzzy entropy is used to initialize the rule base and the types of fuzzy sets. Second, social learning particle swarm optimization (SLPSO) is used to optimize the parameters of the HTFS, and the types of the fuzzy sets are determined by fuzziness, which means that the types of the fuzzy sets will change dynamically in the optimization process. Because the fuzzy set types change, a parameter change strategy is also proposed for adaptive fuzzy set types. Finally, the HTFS is compared with the T1 fuzzy system on 10 real-world datasets, and it is shown that the HTFS has better performance than the T1 fuzzy system. Comparing the HTFS with other advanced methods, it is shown that the HTFS has better prediction performance while taking into account the interpretability of the fuzzy set distribution.

Evolutionary self-organizing fuzzy system using fuzzy-classification-based social learning particle swarm optimization

A self-organizing algorithm based on an online cluster and fuzzy sets update algorithm (OCFU) and fuzzy-classification-based social learning particle swarm optimization (FC-SLPSO) is proposed to address the problem of rule initialization and fitness function evaluation. The OCFU algorithm is used to determine the structure of the fuzzy system and build a flexible partition of fuzzy sets in each input variable. The FC-SLPSO algorithm establishes a fuzzy K-Nearest Neighbor (KNN) classifier in each iteration. The fitness evaluation is performed only when the membership degree of the offspring particles belonging to potential particles is greater than that of the parent particles, which effectively reduces the number of fitness function evaluations and accelerates the PSO algorithm. The tracking results of three nonlinear systems show that the tracking accuracy of the proposed method is better. The Pioneer P3DX mobile robot model and simulation environment with a two-dimensional lidar are constructed based on the Coppeliasim robot simulation platform. Using the algorithms proposed in this paper, the tracking task of the pioneer p3dx mobile robot along a wall is achieved. The effectiveness of the proposed algorithm is verified by the cosimulation of Coppeliasim and MATLAB in multiple scenarios.

A new power system active rescheduling method considering the dispatchable plug-in electric vehicles and intermittent renewable energies

The significant penetration of renewable power generations (RGs) and the large-scale use of plug-in electric vehicles (PEVs) have brought tangible impacts in tackling the climate change challenge the mankind has been facing due to substantive green-house gas and pollutant emissions from fossil-fuel based thermal power generation plants. However, the uncertainty of RGs has also exerted significant challenges to the grid operation and control. Therefore, dynamic power system scheduling to accommodate the intermittent RGs and mass roll-out of PEVs has become extremely important. In this paper, a novel power system rescheduling strategy is proposed to tackle this problem. Considering the uncertainty of the wind energy, a set of indices according to different wind power application scenarios is proposed to initiate a rescheduling scheme for power generations. In addition, a social learning particle swarm optimization algorithm based on real-value and binary parallel is proposed to schedule the output of generator units and the charging and discharging of the PEV. The effectiveness of the proposed active rescheduling framework and solving algorithm has been verified by extensive experiments considering different number of generating units and scenarios, achieving up to over 5.3% cost reduction. The experimental results have also shown that through expropriate management of the charging and discharging of PEVs would be significantly alleviate the negative impact on the grid stability caused by the intermittent wind power generations.

Three-learning strategy particle swarm algorithm for global optimization problems

Social Learning Particle Swarm Optimization (SL-PSO) greatly improves the optimization performance of PSO. In solving complex optimization problems, however, it still has some deficiencies, such as poor search ability and low search efficiency. Hence, an improved SL-PSO, namely, Three-Learning Strategy PSO (TLS-PSO) is proposed in this paper. Firstly, a med-point-example learning strategy and a random learning strategy are proposed to replace the imitation component and social influence component of SL-PSO to enhance the exploitation and exploration, respectively. Secondly, the two learning strategies are combined cleverly into an updating equation to balance exploration and exploitation. Finally, a worst-best example learning strategy is merged skillfully to construct TLS-PSO with hybrid learning mechanism and further enhance the search ability. The experimental results on the complex functions from CEC2013 and CEC2017 test sets indicate that TLS-PSO has better performance compared with state-of-the-art PSO variants and other algorithms. For example, TLS-PSO has an advantage over SL-PSO on 50 of the 56 functions from CEC2013, its running time is less than SL-PSO’s and it has higher search efficiency. Simulation results on the 10 engineering problems also show that TLS-PSO outperforms 7 excellent algorithms, such as IUDE and iLSHADE∊. It is expected to solve practical problems better.

Large-Scale Optimization of Decoupling Capacitors Using Adaptive Region Based Encoding Scheme in Particle Swarm Optimization

Power delivery networks are responsible for supplying clean power to the integrated circuits. Power supply noise plays a critical role in determining the performance of high-speed very large scale integration circuits and systems. In order to maintain power integrity in high-speed systems, decoupling capacitors are used to maintain low impedance of the PDN to eventually minimize power supply noise. However, the discrete optimization problem of selecting decoupling capacitors becomes computationally challenging in the systems having stringent PI requirements. In this work, a novel approach using the Social-Learning Particle Swarm Optimization (SLPSO) technique along with Adaptive Region Search (ARS) is used to tackle the Large-Scale Optimization Problem (LSOP) of decoupling capacitor placement. Region Search (RS) is used to guide particles, followed by ARS to dynamical search for the local best positions and for particles to move faster across the search space while maintaining the diversity of the population. To demonstrate the proposed approach, three practical case studies are presented. The obtained results are compared with current state-of-the-art approaches. The proposed approach drastically reduces computation time and is consistent with better results than other approaches. This consistency of improvement in CPU time in the results of all the examples validates the proposed approach.

A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors

In recent years, global warming impact are becoming increasingly severe due to the dramatic green house emission and severe environmental problem. The large integration of PEV and RGs directly affect the supply and demand balance of power grid, which bring challenges to the secure and economic operation of power system. This study proposes a novel parallel social learning particle swarm optimization method for solving the large scale power system scheduling problem with significant integration of RGs and PEVs. The novel algorithm combines the real value and binary decision variables obtained by social learning particle swarm optimization algorithm, aiming to solve large scale mixed integer unit commitment problem considering charging and discharging management of PEV with large RGs integration. To verify the effectiveness of the proposed algorithm, numerical examples are analyzed for multi scale unit numbers and various cases of RGs and PEVs. The results show that the proposed parallel social learning particle swarm optimization method has superior performance in solving UC problems considering new energy sectors. In addition, the case studies shows that the integration of new energy sources and flexible demand side management of plug-in electric vehicles have great potentials to alleviate power grid load and bring considerable economic benefits.

Region Encoding Helps Evolutionary Computation Evolve Faster: A New Solution Encoding Scheme in Particle Swarm for Large-Scale Optimization

In the last decade, many evolutionary computation (EC) algorithms with diversity enhancement have been proposed to solve large-scale optimization problems in big data era. Among them, the social learning particle swarm optimization (SLPSO) has shown good performance. However, as SLPSO uses different guidance information for different particles to maintain the diversity, it often results in slow convergence speed. Therefore, this article proposes a new region encoding scheme (RES) to extend the solution representation from a single point to a region, which can help EC algorithms evolve faster. The RES is generic for EC algorithms and is applied to SLPSO. Based on RES, a novel adaptive region search (ARS) is designed to on the one hand keep the diversity of SLPSO and on the other hand accelerate the convergence speed, forming the SLPSO with ARS (SLPSO-ARS). In SLPSO-ARS, each particle is encoded as a region so that some of the best (e.g., the top P) particles can carry out region search to search for better solutions near their current positions. The ARS strategy offers the particle a greater chance to discover the nearby optimal solutions and helps to accelerate the convergence speed of the whole population. Moreover, the region radius is adaptively controlled based on the search information. Comprehensive experiments on all the problems in both IEEE Congress on Evolutionary Computation 2010 (CEC 2010) and 2013 (CEC 2013) competitions are conducted to validate the effectiveness and efficiency of SLPSO-ARS and to investigate its important parameters and components. The experimental results show that SLPSO-ARS can achieve generally better performance than the compared algorithms.

Source mask optimization for extreme-ultraviolet lithography based on thick mask model and social learning particle swarm optimization algorithm.

Extreme ultraviolet (EUV) lithography plays a vital role in the advanced technology nodes of integrated circuits manufacturing. Source mask optimization (SMO) is a critical resolution enhancement technique (RET) or EUV lithography. In this paper, an SMO method for EUV lithography based on the thick mask model and social learning particle swarm optimization (SL-PSO) algorithm is proposed to improve the imaging quality. The thick mask model's parameters are pre-calculated and stored, then SL-PSO is utilized to optimize the source and mask. Rigorous electromagnetic simulation is then carried out to validate the optimization results. Besides, an initialization parameter of the mask optimization (MO) stage is tuned to increase the optimization efficiency and the optimized mask's manufacturability. Optimization is carried out with three target patterns. Results show that the pattern errors (PE) between the print image and target pattern are reduced by 94.7%, 76.9%, 80.6%, respectively.

AN OPERANT CONDITIONING APPROACH FOR LARGE SCALE SOCIAL OPTIMIZATION ALGORITHMS

The changes that positive or negative results cause in an individual's behavior are called Operant Conditioning. This paper introduces an operant conditioning approach (OCA) for large scale swarm optimization models. The proposed approach has been applied to social learning particle swarm optimization (SL-PSO), a variant of the PSO algorithm. In SL-PSO, the swarm particles are sorted according to the objective function and all particles are updated with learning from the others. In this study, each particle's learning rate is determined by the mathematical functions that are inspired by the operant conditioning. The proposed approach adjusts the learning rate for each particle. By using the learning rate, a particle close to the optimum solution is aimed to learn less. Thanks to the learning rate, a particle is prevented from being affected by particles close to the optimum point and particles far from the optimum point at the same rate. The proposed OCA-SL-PSO is compared with SL- PSO and pure PSO on CEC 13 functions. Also, the proposed OCA-SL-PSO is tested for large-scale optimization (100-D, 500-D, and 1000-D) benchmark functions. This paper has a novel contribution which is the usage of OCA on Social Optimization Algorithms. The results clearly indicate that the OCA is increasing the results of large-scale SL-PSO.

A Hybrid Steady-State Compressor Model for Real-Time Applications in Performance Monitoring, Control and Optimization

In this study, a hybrid steady-state compressor model is proposed that can be used in the real-time performance monitoring, control and optimization of the vapor compression cycle. In the proposed model, first, a detailed analysis of the mass flow rate is presented, which is based on the volumetric efficiency concept and the assumption of a polytropic compression process. Then, discharge temperature of the refrigerant and power consumption of the compressor are also investigated. Three semiempirical models are constructed respectively. Further, to tune the unknown empirical parameters of the models, a social learning particle swarm optimization (SLPSO) algorithm is developed by using the real-time experimental data. An experimental apparatus of a refrigerant system is tested to validate the proposed models. The experimental results demonstrate that the proposed models accurately predict the performance of real-time operating compressors. Meanwhile, the models identified by the SLPSO algorithm are more accurate than those identified by the traditional least-squares method.

Truncation-learning-driven surrogate assisted social learning particle swarm optimization for computationally expensive problem

Surrogate-assisted evolutionary optimization greatly slashes the computational burden of evolutionary algorithms for computationally expensive problems. However, new issues arise concerning the compatibility and fault tolerance of surrogates, evolutionary learning operators, and problem property. To this end, this paper proposes a truncation-learning-driven surrogate assisted social learning particle swarm optimizer (TL-SSLPSO) to coordinate these three ingredients. For avoiding and correcting the deceptions induced by the low confidence exemplars due to the surrogate in behavior learning, TL-SSLPSO equally segments the iterative population into multiple sub-populations with different fitness levels and selects exemplars from the randomly selected high-level sub-populations for the behavior learning of low-level sub-population, while truncating the behavior learning of the highest-level sub-population composed of some of the best approximated or real evaluated particles and retaining the sub-population directly to the next generation. Besides, a greedy sampling strategy is employed to find promising solutions with better fitness versus the global best to complement the truncation learning. Extensive experiments on twenty-four widely used benchmark problems and a stepped cantilever beam design problem with 17 steps are conducted to assess the effectiveness of cooperation between truncation learning and greedy sampling and comparisons with several state-of-the-art algorithms demonstrate the superiority of the proposed method.

Optimal Task Assignment for UAV Swarm Operations in Hostile Environments

This paper proposes the engagement model and optimal task assignment algorithm for small-UAV swarm operations in hostile maritime environments. To alleviate the complexity of a real engagement environment, several assumptions are made: in the proposed engagement model, a vessel can attack the UAV within a certain range with a constant kill probability rate; and the ability of a vessel to attack UAVs is reduced if the multiple UAVs are involved. The objective function for optimal task assignment is constructed from the engagement model which estimates the total damage to vessels as the engagement outcome. Considering computational time and non-convex nature of the optimization problem, a heuristic approach, SL-PSO (social-learning particle swarm optimization), is adopted to maximize the objective function. In particular, a modified SL-PSO approach is introduced to deal with the optimization problem in a discrete domain. Numerical simulation results for two scenarios are presented to analyze the characteristics of the proposed engagement model and the performance of the optimal task assignment algorithm in the given environment.

Real-time traffic signal control with swarm optimization methods

Real-time traffic signal control is the control methods that control the traffic signal according to the instant traffic situation. In this paper, it is suggested to optimize the traffic control problem with the swarm-based heuristic optimization algorithms. The proposed methods are tested with the real traffic data obtained from Kilis city in Turkey. The performance is evaluated in real-time via the SUMO traffic simulator. The obtained results are compared with the actual traffic measurement data, and the success of the proposed method is expressed numerically. Finally, it is proved that the particle swarm optimization algorithm and its variance algorithm could be used successfully to optimize the traffic signals control in real traffic. In this study, Social Learning-Particle Swarm Optimization is used as a traffic signal optimizer for the first time in the known literature.

A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

Social learning particle swarm optimization (SL-PSO) allows individuals to learn from others to improve the scalability with easy parameter settings. However, it still suffers from the poor convergence for those multi-modal problems due to the loss of swarm diversity. To improve both the diversity and the convergence, this paper proposes a novel algorithm to apply the mechanism of molecular interactions to SL-PSO, in which the molecular attraction aims to improve the convergence, and the molecular repulsion intends to enhance the diversity. In the experiments, we compare our algorithm with the SL-PSO algorithm and other representative PSO and evolutionary algorithms on 49 benchmark functions. The results show the performance of the proposed algorithm is better than that of the SL-PSO algorithm and other representative PSO and evolutionary algorithms on average. This work builds the solid foundation for the integration of the molecular interaction mechanism with PSO and other optimization algorithms.

Multiobjective Infill Criterion Driven Gaussian Process-Assisted Particle Swarm Optimization of High-Dimensional Expensive Problems

Model management plays an essential role in surrogate-assisted evolutionary optimization of expensive problems, since the strategy for selecting individuals for fitness evaluation using the real objective function has substantial influences on the final performance. Among many others, infill criterion driven Gaussian process (GP)-assisted evolutionary algorithms have been demonstrated competitive for optimization of problems with up to 50 decision variables. In this paper, a multiobjective infill criterion (MIC) that considers the approximated fitness and the approximation uncertainty as two objectives is proposed for a GP-assisted social learning particle swarm optimization algorithm. The MIC uses nondominated sorting for model management, thereby avoiding combining the approximated fitness and the approximation uncertainty into a scalar function, which is shown to be particularly important for high-dimensional problems, where the estimated uncertainty becomes less reliable. Empirical studies on 50-D and 100-D benchmark problems and a synthetic problem constructed from four real-world optimization problems demonstrate that the proposed MIC is more effective than existing scalar infill criteria for GP-assisted optimization given a limited computational budget.

Training an Approximate Logic Dendritic Neuron Model Using Social Learning Particle Swarm Optimization Algorithm

With the rapid development of artificial neural networks, recent studies have shown that dendrites play a vital role in neural computations. In this study, we propose a dendritic neuron model called the approximate logic dendritic neuron model (ALDNM) to solve classification problems. The ALDNM can be divided into four layers: the synaptic layer, the dendritic layer, the membrane layer, and the soma body. Considering the limitation of the back-propagation (BP) algorithm, we employ a heuristic optimization called the social learning particle swarm optimization algorithm (SL-PSO) to train the ALDNM. In order to investigate the effectiveness of SL-PSO for training the ALDNM, we compare this training method with BP and four other typical heuristic optimization methods. Moreover, the proposed ALDNM is also compared with seven classifiers to verify its performance. The experimental results and statistical analysis on four classification problems indicate that the proposed ALDNM trained by SL-PSO can provide a competitive performance for solving the classification problems. It is worth emphasizing that the structure of the trained ALDNM can be greatly simplified owing to the unique pruning operations. Furthermore, the simplified ALDNM for a specific problem can be converted into a corresponding logic circuit classifier for a fast classification.

Differential mutation and novel social learning particle swarm optimization algorithm

Social Learning Particle Swarm Optimization (SLPSO) is an improved Particle Swarm Optimization (PSO) algorithm, which greatly improves the optimization performance of PSO. However, SLPSO still has some deficiency, such as poor balance between exploration and exploitation and low search efficiency, so that it cannot yet do well in solving many complex optimization problems. Thus, this paper proposes an improved SLPSO algorithm, that is, Differential mutation and novel Social learning PSO (DSPSO). Firstly, in order to balance exploration and exploitation better, a dynamic inertia weight is introduced to replace the random inertia weight of SLPSO, and a single-example learning approach and an example-mean learning one are proposed to replace the imitation component and the social influence component of SLPSO respectively. Secondly, the dimension-based velocity updating equation of SLPSO is divided into two particle-based updating equations with the two approaches, and the two are executed alternately to form a novel social learning PSO (NSLPSO), which enhance the exploitation of SLPSO. Finally, a dynamic differential mutation strategy is used in NSLPSO to update the three best particles to enhance the exploration to obtain DSPSO. Experimental results on the complex functions from CEC2013 reveal that DSPSO outperforms SLPSO and quite a few state-of-the-art and classic PSO variants.