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Background: Recently, Particle Swarm Optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence. Objective: However, PSO lacks population diversity and may fall to local optima. Hence, an Improved Hybrid Particle Swarm Optimizer with Sine-Cosine Acceleration Coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions in Transient Electromagnetic (TEM) nonlinear inversion. Method: A reverse learning strategy is applied to optimize population initialization. The sine-cosine acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population diversity during the search process. In addition, the mutation method is used to reduce the probability of premature convergence. Results: The application of IH-PSO-SCAC in the test functions and several simple layered models are demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test our algorithm. The first model contains an underground low-resistivity anomaly body and the second model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases, resistivity profiles are obtained, and the inverse problem is solved for verification. Conclusion: The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.

In this study, single and aggregated forecasters of half-hourly global horizontal irradiance are assessed. The models are the standard persistent model and four newly proposed static, dynamic, moving average, and amplified persistent models. These sub-forecasters are aggregated using equal, annual optimal, and monthly optimal weights. A particle swarm optimizer was used to find those weights. Measured data, obtained from two desert sites for the years 2015–2018, was used for fitting and training the different models, while the data of the year 2019 was used to test their prediction capabilities. For the single forecasters, the dynamic model is the most accurate, followed by the static and average models. When the aggregated model of annual optimal weights was tested, the three contributing forecasters were the dynamic, average, and amplified models. The dynamic forecaster held the largest weight due to its prediction superiority during overcast and partially cloudy days. When monthly optimal weights were used, all forecasters contributed, and the dynamic model held the largest weight during winter but not in the summer when the clear sky condition is dominant. The aggregated model was the most precise, with relative mean square errors lower than 15.0% and coefficients of determination higher than 98.8%.

As a result of rapid financial development and natural disasters, energy efficiency research, and high-quality electricity alternative energy options, as well as efficient electricity sources. In particular, the use of green energy sources has become a hot issue; As a result, distributed electricity supply in the micro grid is the basis for the achievement of the vital objectives of successfully providing the customer with currency and stability. The article proposes a hybrid metaheuristic approach based on the Eagle strategy Technique (ES) and Particular Swarm Optimizing (PSO) Technology, which will minimize low-voltage running costs from a renewable energy source such as an electricity generator, solar panels, wind generators, micro turbines and fuel cells. The cost optimization problem is set up as a nonlinearly constrained problem. In order to maximize distributed generation, a mathematical problem must be solved. The proposed hybrid solution is evaluated on low-voltage micro grids, and its optimal performance is compared to that of other hybrid approaches and variety of other metaheuristic techniques

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

Forecasting gasoline consumption is of great significance for formulating oil production, foreign trade policies, and ensuring the balance of domestic refined oil supply. Based on grey system theory, a fractional accumulation operator is constructed to optimize the accumulation method of the traditional discrete grey model, and the Particle Swarm Optimization algorithm is used to solve the fractional nonlinear parameters. This model was used in the prediction of gasoline consumption in Chongqing, China, and compared with the existing 7 models. The results show that the fractional discrete grey model optimized by PSO has better prediction accuracy. The fractional discrete grey model optimized by PSO can be used as a quantitative method in the field of energy forecasting.

Integrated optimisation of production scheduling and distribution decision is necessary for reducing the whole cost of the supply chain in the make-to-order business environment. This paper studies...

This study proposes the application of an efficient and hybrid meta-heuristic algorithm of Harris Hawk-Particle Swarm Optimizer (HHOPSO) for solving voltage constrained reactive power planning (VCR...

In this paper, the optimal formation and optimal matching of a multi-robot system are investigated with a projection-based algorithm designed to get the optimal formation moving in real time. The formation-related optimization problem is proposed under the consideration of two cases: the free formation and the formation with anchor(s). For the latter, equality constraints are formulated for the anchor, and the objective of the optimal formation is to minimize the total distance to the initial formation of the multi-robot system. Here, the objective function with mixed norm is considered to get a compact formation. Sufficient conditions on the design parameter for global convergence of the proposed algorithm are provided in the theoretical results. Furthermore, the projection particle swarm optimizer is investigated for getting the optimal matching between the initial/intermediate formation and the optimal formation. Finally, simulations on several numerical examples are presented to validate the effectiveness of the proposed method.

The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer (GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number of features.

Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems. However, the performance of PSO on a specific problem highly depends on the velocity updating strategy. For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable. To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO). The proposed algorithm consists of three existing efficient and simple PSO searching strategies. The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation. Particles in the three indicator subpopulations update their velocities by different strategies. During every learning period, the improved function values of the three strategies are recorded. At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy. Therefore, the appropriate PSO searching strategy can have more computational expense. The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants. These results suggest that MPEPSO ranks the first among these algorithms. Moreover, MPEPSO is applied to solve four engineering design problems. The results show the advantages of MPEPSO. The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.

Generally, the actual explosive is not suitable for the training of security personnel due to its danger. Hence, it is significant to create the simulant as similar as possible to the real explosive, where the difficulties are derived from finding safe compounds from the compound database and their related proportion. In this paper, a cooperative co-evolutionary comprehensive learning particle swarm optimizer is proposed to obtain the formulation design of explosive simulant. To be specific, the proposed algorithm employs particle swarm optimization as the optimizer and creates two cooperative populations focusing on finding compounds and their proportions, respectively. Moreover, a comprehensive cooperative strategy is designed to improve the solution diversity and thus enhance the search performance. To the best of our knowledge, this is the first attempt to employ evolutionary algorithm to design explosive simulant formulation. Comprehensive experiments are conducted on several typical explosives and results demonstrate the superiority of the proposed algorithm in comparison to other algorithms.

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

Estimating the location of a target is essential for many applications such as asset tracking, navigation, and data communications. Time-difference-of-arrival (TDOA) based localization has the main advantage that it does not require synchronization between the transmitting and the receiving sides. Phase-difference-of-arrival (PDOA) provides additional information that can be leveraged to enhance localization performance. The combination of TDOA and PDOA for localization has not been reported in the literature. In this letter, we propose a novel approach that incorporates both TDOA and PDOA to achieve improved position estimation. In the proposed approach, an initial location estimate is obtained by optimizing a TDOA cost function. Next, a PDOA, or a hybrid TDOA-PDOA cost function is optimized using a particle swarm optimizer to obtain the final location estimate. Simulation results show that the proposed approach sufficiently, and justifiably, improves localization performance relative to pure TDOA methods.

Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models $${\text{ANFIS - GWO}}_{{\text{SWARA}}}$$ , $${\text{ANFIS - PSO}}_{{\text{SWARA}}}$$ , $${\text{ANFIS - GWO}}_{{\text{CF}}}$$ and $${\text{ANFIS - PSO}}_{{\text{CF}}}$$ was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models $${\text{ANFIS - PSO}}_{{\text{CF}}}$$ and $${\text{ANFIS - GWO}}_{{\text{SWARA}}}$$ showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk.

This paper proposes a novel binary particle swarm optimization algorithm (called IRBPSO) to address high-dimensional knapsack problems in dynamic environments (DKPs). The IRBPSO integrates an immune-based response strategy into the basic binary particle swarm optimization algorithm for improving the quantity of evolutional particles in high-dimensional decision space. In order to enhance the convergence speed of the IRBPSO in the current environment, the particles with high fitness values are cloned and mutated. In addition, an external archive is designed to store the elite from the current generation. To maintain the diversity of elites in the external archive, the elite of current generation will replace the worst one in the external archive if and only if it differs from any of the existing particles in the external archive based on the Hamming distance measurement when the archive is due to update. In this way, the external archive can store diversiform elites for previous environments as much as possible, and so as to the stored elites are utilized to transfer historical information to new environment for assisting to solve the new optimization problem. Moreover, the environmental reaction scheme is also investigated in order to improve the ability of adapting to different kinds of dynamic environments. Experimental results on a series of DKPs with different randomly generated data sets indicate that the IRBPSO can faster track the changing environments and manifest superior statistical performance, when compared with peer optimization algorithms.

The lifecycle of a product is getting shorter in today’s market realities. Latest developments in the industry are heading towards achieving products that are easy to recycle, by developing further technological advances in raw materials ought to include input from End of Life (EOL) products so a reduction of natural harm could be achieved, hence reducing the overall production environmental footprint. Therefore, the approach taken as a design for environment, a key request nowadays in order to develop products that would ease the reverse manufacturing process leading to a more efficient element recycling for later use as spare parts or remanufacturing. The methodology proposed compares three probable disassembly sequences following a comparison of literature-found procedures between genetic algorithms and as a “state space search” problem, followed by a hybrid approach developed by the authors. Time and evaluation of these procedures reached to the best performing sequence. A subsequent augmented reality disassembly simulation was performed with the top-scored operation sequence with which the user is better able to familiarize himself with the assembly than a traditional paper manual, therefore enlightening the feasibility of the top performing sequence in the real world.

In this work, the Modified competitive swarm optimizer (MCSO) is applied for solving the economic dispatch problem. This algorithm got inspiration from particle swarm optimizer (PSO) algori...

Chaotic response in the permanent magnet synchronous motor (PMSM) is an undesirable performance that may affect motor stability. This unexpected behavior occurs due to the disruption in the system parameters and load disturbance. To control this unwanted performance, it’s essential to introduce a robust method to determine the PMSM model parameters efficiently and expeditiously. Two PMSM models depending on the type of its nonlinear differential equations are investigated. The first one is the integer-order PMSM model, while the other is the fractional-order model. In this work, novel developed optimization variants called chaotic heterogeneous comprehensive learning particle swarm optimizer (CHCLPSO) is proposed. In CHCLPSO, the standard heterogeneous comprehensive learning particle swarm optimizer (HCLPSO) is cooperated into ten different chaos maps to adjust some of its parameters. Six CHCLPSO variables are introduced in addition to the standard HCLPSO version to estimate the parameters of the integer-order and the fractional-order PMSM models that are corresponding to the chaotic behavior. A comparison among the results of the introduced variants and the original algorithm is carried out. Moreover, a comprehensive comparison with other recent algorithms is performed. The primary outcome proves that the chaos maps have a remarkable influence in both of the consistency and the accuracy of the results of the HCLPSO with less execution time over the integer and the fractional-order models, especially CHCLPSO-III and CHCLPSO-V with sinusoidal and piecewise maps, respectively.

In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (A...

This paper presents an improved opposition-based Particle Swarm Optimizer (PSO) with partial opposition-based learning. The partial opposition-based learning scheme is a new form of opposition-based learning and it is employed to improve the performance. Nowadays, the artificial neural network (ANN), an important machine learning tool, is used in medicine especially in medical disease diagnosis. ANN training is a complex task and a training algorithm has a significant role in ANN’s performance. Therefore, the proposed algorithm is applied in training of Multi-Layer Feed-Forward Neural Network (FFNN) for classification task in medical data mining. The classification task is carried out on 15 well-known medical data sets. A comparative study has been made with Levenberg–Marquardt algorithm, Artificial Bee Colony, Trigonometric Mutation Differential Evolution, basic PSO and other nine opposition-based PSO algorithms. The experimental results with statistical analysis demonstrate that the proposed algorithm performs better than all other algorithms in classification.

A shape control with light activated polymers (LAPs) is designed in present article. Under ultraviolet light irradiation, the structures of crosslink networks in LAPs alter. Such alterations lead t...

N6-methyladenosine (m6A) is the one of the most important RNA modifications, playing the role of splicing events, mRNA exporting and stability to cell differentiation. Because of wide distribution of m6A in genes, identification of m6A sites in RNA sequences has significant importance for basic biomedical research and drug development. High-throughput laboratory methods are time consuming and costly. Nowadays, effective computational methods are much desirable because of its convenience and fast speed. Thus, in this article, we proposed a new method to improve the performance of the m6A prediction by using the combined features of deep features and original features with extreme gradient boosting optimized by particle swarm optimization (PXGB). The proposed PXGB algorithm uses three kinds of features, i.e., position-specific nucleotide propensity (PSNP), position-specific dinucleotide propensity (PSDP), and the traditional nucleotide composition (NC). By 10-fold cross validation, the performance of PXGB was measured with an AUC of 0.8390 and an MCC of 0.5234. Additionally, PXGB was compared with the existing methods, and the higher MCC and AUC of PXGB demonstrated that PXGB was effective to predict m6A sites. The predictor proposed in this study might help to predict more m6A sites and guide related experimental validation.

Due to the lack of global search capacity, most evolutionary or swarm intelligence based algorithms show their inefficiency when optimizing multi-modal problems. In this paper, we propose a grouping particle swarm optimizer (GPSO) to solve this kind of problem. In the proposed algorithm, the swarm consists of several groups. For every several iterations, an elite group is constructed and used to replace the worst one. The thought of grouping is helpful for improving the diversity of the solutions, and then enhancing the global search ability of the algorithm. In addition, we apply a simple mutation operator to the best solution so as to help it escape from local optima. The GPSO is compared with several variants of particle swarm optimizer (PSO) and some state-of-the-art evolutionary algorithms on CEC15 benchmark functions and three practical engineering problems. As demonstrated by the experimental results, the proposed GPSO outperforms its competitors in most cases.

Photovoltaic modeling has attracted researchers’ attention worldwide because of its importance in the photovoltaic system design. Therefore, several photovoltaic models have been introduced as static and dynamic photovoltaic models. Moreover, a novel fractional order dynamic photovoltaic model has been recently developed to enhance the accuracy and flexibility of the conventional integral order one. The unknown parameters of these models should be extracted accurately to achieve a proper photovoltaic system design and operation. In this work, novel Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are introduced, where the Heterogeneous Comprehensive Learning Particle Swarm Optimizer is combined with ten different chaos maps to adapt its parameters. Six Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are proposed in addition to the standard Heterogeneous Comprehensive Learning Particle Swarm Optimizer version to identify the parameters of both the static and the dynamic models based on different experimental datasets. To demonstrate the superiority of the developed variants, their results are compared to the most recent state-of-the-art algorithms with the aid of statistical analysis methods. The main outcome is that, in both of the static and the dynamic photovoltaic models, the Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants show their efficiency, accuracy and robustness not only over Heterogeneous Comprehensive Learning Particle Swarm Optimizer but also over recently published algorithms. They provide better fitting relative to the experimental datasets with the least deviation error and the fastest convergence speed as well. In the case of static models, the fourth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with an iterative map for the single diode model, the third variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with singer map for the double diode model of solar cell. On the other hand, for the dynamic models, the second Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variant with sinusoidal map for the integral order dynamic photovoltaic model and the sixth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with Gauss/mouse map for the fractional order dynamic photovoltaic model offer the best performance.

The balance of exploration and exploitation of particle swarm optimization (PSO) is still a great challenge in large scale optimization. In our previous work, an adaptive particle swarm optimizer with decoupled exploration and exploitation (APSODEE) has been proposed to solve this problem. However, it still shows room for further improvements. First, APSODEE guides particles with the best individual in each sub-swarm, which is adverse to swarm diversity preservation especially in case of large scale optimization. Second, APSODEE fails to lead particles moving to sparse areas which are also potentially promising. To address these issues, two modifications are incorporated into APSODEE including a partial updating strategy and a quality restrained local sparseness diversity measurement. The former is proposed to further enhance the algorithm’s swarm diversity preservation ability while the latter is designed to help guiding updated particles moving towards the areas which are both sparse and potentially promising, resulting in the modified APSODEE. The experiments are conducted based on CEC 2013 benchmarks with 1000 dimensionality. The results show the competitiveness of the proposed algorithm.

The aim of multi-objective particle swarm optimizer (MOPSO) is to find an accurate and well-distributed approximation of the true Pareto Front (PF). The intrinsic character of PSO puts convergence first, which can cause great loss of population diversity. How to maintain the convergence and diversity simultaneously is an essential issue for MOPSO. In this paper, we propose a niche based multi-objective particle swarm optimizer (NMOPSO) to balance the convergence and diversity. First, a niche based on the Euclidean distance is constructed for each particle, then the leading particle is chosen out either from the niche or from the whole swarm. After that, two position update strategies are designed to update the position of each particle. The position update strategies provide two guiding models for leaders, one is utilizing the difference vector between the leader and the current particle, the other is directly taking some components of leaders. Three well-known test suites are employed to verify the performance of NMOPSO. Compared with three popular MOPSOs, simulation results show that NMOPSO performs better on most of test problems.

Diabetes has become a major health concern among people around the world. It may reduce the life span, and enhances the probability of various kind of cardiovascular diseases. Prediction of diabetes may provide an alarm to the people who are required to check their health status. It is a very challenging task as medical data is very much saturated and complex. In this work, we have predicted diabetes from several lifestyle parameters such as BMI, age, pregnancy and symptoms such as weight loss, visual blurring, weight loss, and so on. Several machine learning algorithms were used to predict diabetes. These machine learning algorithms were further optimized using Particle Swarm Optimizer and Social Group Optimizer. Social Group Optimized Gradient Boosted Classifier (GBC) achieved an accuracy of 71.85% in predicting diabetes from lifestyle parameters. The proposed architecture achieved 98.26% accuracy in case of prediction from symptoms using Social Group Optimized Random Forest Algorithm.

A new approach to node localization is proposed by using the combined localization model in Wireless sensor nets (WSN) with a hybridized swarm evolution algorithm (HSEA). Mathematically, based on the distances of the localizing model and Pareto, the fitness function is determined. To achieve better efficiency, the HSEA is implemented by combining the particle swarm optimizer (PSO) with the differential-evolution (DE) algorithms. Applied HSEA deals with the node location situation in WSN. The simulation compares the results of the proposed scheme with the others schemes to demonstrate that the suggested task using HSEA achieves better performance than the competitors.

Optimizing the power flow is one of the recent and emerging problems need to solve immediately to enhance the power flow in any power system applications. To do this, various earlier researchers have used many artificial intelligence methods or optimization methods. The obtained results are not cost and time effective. Still the real-time power system applications need a solution with cost and time effective. This paper aimed to integrate the chaotic particle swarm optimizer (CPSO) and gravitational search algorithm to solve the OPF problems. The proposed methods are simulated in MATLAB software, tested on IEEE buses of 30 and 57, and the results are verified. From the obtained results, it is identified that the GSA method is concluded as better method for solving OPF problems in power system applications.

Particle swarm optimization (PSO) attracts much attention due to its ability in solving complex practical engineering problems effectively. To further improve its performance, a heterogeneous particle swarm optimizer (HPSO) is proposed in this work. Five widely used benchmark functions are selected to test its efficiency. Furthermore, five state-of-the-art improved PSO variants are selected for a comparisons purpose. The results demonstrate that HPSO is better than the other five algorithms. A logistics problem in aircraft manufacturing is then studied and solved. The results show HPSO's superiority over its tested PSO variants.

Bolus, formed by a sheet of material, covers on tumors of patient skins to generate the desired dose distribution in a process of the high-energy radiotherapy. The existing methods of bolus shaping use a manual process to cut the commercial material into the shape, which can cause air gaps between the patient skin surface and bolus in the reduced effect of cancer care treatment. A method of automatic bolus shaping can improve the accuracy and reduce air gaps of bolus shaping, which uses a process of first segmenting 3D patient skin surface models into several patches and then unfolding the patches into 2D patterns for material cutting before folding the cut sheet into the bolus. However, air gaps between the bolus and patient skin surface cannot be directly evaluated to find the bolus accuracy. This paper proposes a method to improve model flattenability and evaluate the air gaps using a particle swarm optimizer (PSO). A 3D surface shape is unfolded only if the air gap and surface flattenability meet the requirement of accuracy. Otherwise, the surface will be segmented into several patches to improve the flattenability and reduce air gaps. The objective and constraint are identified to search for an optimal solution for the 3D surface with a high flattenability. A strategy of the dimension reduction is proposed for the selection of local nodes on a meshed surface to increase the searching efficiency of surface flattenability. A local node selection based PSO (L-PSO) method is developed to search for the optimal solution. The proposed method is verified in two case studies of bolus shaping.

In this paper, a new approach to momentum particle swarm optimization is proposed. We design a particle swarm optimizer that converges faster than the currently available momentum particle swarm optimizers. This simulates gradient descent with momentum and is inspired from the back-propagation algorithm with momentum in neural networks. The proposed optimizing algorithm constantly gives faster convergence time in all the available test optimization functions (constrained or unconstrained). This paper contains graphs and results that summarize the algorithm’s performance in contrast with weighted particle swarm optimizer and momentum particle swarm optimizer.

Particle swarm optimizer was proposed in 1995, and since then, it has become an extremely popular swarm intelligent algorithm with widespread applications. Many modified versions of it have been developed, in which, comprehensive learning particle swarm optimizer is a very powerful one. In order to enhance its performance further, a local search based on Latin hypercube sampling is combined with it in this work. Due to that a hypercube should become smaller and smaller for better local search ability during the search process, a control method is designed to set the size of the hypercube. Via numerical experiments, it can be observed that the comprehensive learning particle swarm optimizer with the local search based on Latin hypercube sampling has a strong ability on both global and local search. The hybrid algorithm is applied in cylindricity error evaluation problem and it outperforms several other algorithms. Published version This research work is supported in part by National Natural Science Foundation of China (NSFC) under Grant No. 61603145, in part by Natural Science Foundation of Hubei Province under Grant No. 2015CFB528, and in part by Open Project of State Key Lab of Digital Manufacturing Equipment & Technology under Grant DMETKF2018011.

The Particle Swarm Optimization (PSO) algorithm is an attractive meta-heuristic approach for difficult optimization problems. It is able to produce satisfactory results when classical analytic methods cannot be applied. However, the design of PSO was usually based on ad-hoc attempts and its behavior could not be exactly specified. In this work, we propose to drive particle into oscillatory trajectories such that the search space can be covered more completely. A difference equation based analysis is conducted to reveal conditions that guarantee trajectory oscillation and solution convergence. The settings of cognitive and social learning factors and the inertia weight are then determined. In addition, a new strategy in directing these parameters to follow a linearly decreasing profile with a perturbation is formulated. Experiments on function optimizations are conducted and compared to currently available methods. Results have confirmed that the proposed Oscillatory Particle Swarm Optimizer (OSC-PSO) outperforms other recent PSO algorithms using adaptive inertia weights.

This paper presents a novel global maximum power point tracking (MPPT) method based on the sorting particle swarm optimizer (PSO). At first, the output characteristics of photovoltaic (PV) array un...

The organic Rankine cycle (ORC) system powered by exhaust heat has great potential in improving engine performance. Many optimizations of the only ORC system were conducted, while the existing literature pays limited attention to the optimization of the engine–ORC combined system. By considering the importance of interaction, cooperation, and influence between the engine and ORC system, a global optimization of the diesel engine–ORC combined system (herein, the combined system) is conducted in this paper with respect to power output and fuel economy. A GT-Suite model of the combined system and a GT-Suite/Simulink co-simulation model are proposed to obtain the optimum operating parameters of the engine and the ORC system under various operating conditions. Furthermore, the effects of the operating parameters, namely, exhaust valve timing, injection timing, expander speed, and pump speed, are evaluated on the combined system. In addition, models of the engine and the ORC system are calibrated, and a particle swarm optimizer (PSO) is designed and adopted for global optimization. Optimization results show improvements of 3.24% and 3.13% on the power output and brake specific fuel consumption (BSFC), respectively, with full engine load when the engine is operated at 3600 r/min. In the optimization of fuel economy with partial engine load, a maximum reduction of 5.71% on the BSFC of the combined system is obtained at 3600 r/min engine speed.

This work examines a strategy for combining two heuristic methods, Differential Evolution (DE) and Particle Swarm Optimization (PSO) to better address the problem of stagnation when solving time-optimal slew-maneuver problems with path constraints. Several approaches are considered, including the use of only PSO or DE, and use of PSO with DE applied to some iterations. Applying a newer form of DE, in which random fractions of the vector-difference components are employed, stagnation is not evident, but the computational time is greater. The combined PSO/DE method handles the stagnation very well, with a 40% reduction in computation time compared with just DE.

A particle swarm optimization algorithm with crossover operation (PSOCO) is proposed. In the proposed PSOCO, two different crossover operations are employed in order to breed promising exemplars. By performing crossover on the personal historical best position of each particle, the effective guiding exemplars are constructed and they maintain a good diversity. In turn, these high quality exemplars are used to guide the evolution of particles. PSOCO is two-layer particle swarm optimization with positive feedback mechanism. In order to test the performance of PSOCO, we use a set of widely used benchmark functions. The experimental results demonstrate that the proposed PSOCO is a competitive optimizer in terms of both solution quality and efficiency.

In the modern era, people spend approximately 90% of their time in indoor settings, such as offices and residential buildings. As prolonged exposure to indoor environments can significantly impact health outcomes, it is important to ensure that good indoor air quality (IAQ) is achieved. The main obstacles to achieving effective control of IAQ are twofold. First, it is very difficult to monitor the values of IAQ parameters, especially within a person's breathing zone. Second, current heating ventilation and air conditioning systems are unable to rapidly predict and optimize IAQ. This study aims to obtain accurate indoor environmental parameters for rapidly predicting and optimizing IAQ. To achieve this, a computational fluid dynamics (CFD)-based back propagation neural network (BPNN) combined with a particle swarm optimizer (PSO) algorithm is proposed. Notably, the BPNN-PSO algorithm can rapidly predict and optimize IAQ while using a limited number of CFD runs. In comparison with other state-of-the-art methods for controlling IAQ, such as the artificial neural network (ANN)-genetic algorithm (GA), the BPNN-PSO algorithm further reduces the concentration of indoor air pollutants by up to 6.44% and computational costs by as much as 23.53%. The use of CFD ensures that the information obtained is accurate and allows for rapid prediction of indoor environmental conditions. The BPNN-PSO algorithm has the potential to contribute very effective and intelligent control strategies for ventilating indoor environments.

Purpose The aim of this paper is to explore the potential of standard quantum particle swarm optimization algorithms to solve single objective electromagnetic optimization problems. Design/methodology/approach A modified quantum particle swarm optimization (MQPSO) algorithm is designed. Findings The MQPSO algorithm is an efficient and robust global optimizer for optimizing electromagnetic design problems. The numerical results as reported have demonstrated that the proposed approach can find better final optimal solution at an initial stage of the iterating process as compared to other tested stochastic methods. It also demonstrates that the proposed method can produce better outcomes by using almost the same computation cost (number of iterations). Thus, the merits or advantages of the proposed MQPSO method in terms of both solution quality (objective function values) and convergence speed (number of iterations) are validated. Originality/value The improvements include the design of a new position updating formula, the introduction of a new selection method (tournament selection strategy) and the proposal of an updating parameter rule.

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 𝑒-6 and (PSO)=3.4948 𝑒-4.

Item classification is an issue among inventory managers who want to achieve key-point management of items with different emphases, thus prompting managers and researchers to pursue efficient and effective classification algorithms. In processing multiple-criteria inventory classifications, back-propagation neural network (BPNN) shows its superiority in balancing items’ multiple competing attributes. However, because the training processes and the final results of BPNN rely on the initial connection weights and thresholds, finding reasonable values of the two parameters is a challenge. This paper introduces biogeography-based optimization (BBO) and an adaptive mutation particle swarm optimizer (AMPSO) to BPNN to optimize the training parameters, i.e., the global initial connection weights and thresholds, of BPNN. On the basis of this, two hybrid classification algorithms—BBO-BPNN and AMPSO-BPNN—are presented. Real-life data from three cases are adopted to verify the effectiveness and feasibility of the two proposed hybrid algorithms. Experimental results demonstrate that BBO-BPNN and AMPSO-BPNN show higher classification accuracy than other hybrid models, i.e., BPNN, PSO-BPNN, DE-BPNN, and GA-BPNN, in the multiple-criteria inventory classification problem.

This paper presents a novel meta-heuristic algorithm, dynamic particle swarm optimizer with escaping prey (DPSOEP), for solving constrained non-convex and piecewise optimization problems. In DPSOEP, the particles developed from two different species are classified into three different types, consisting of preys, strong particles and weak particles, to simulate the behavior of hunting and escaping characteristics observed in nature. Compared to other variants of particle swarm optimizer (PSO), the proposed algorithm takes account of an escaping mechanism for the preys to circumvent the problem of local optimum and also develops a classification mechanism to cope with different situations in the search space so as to achieve a good balance between its global exploration and local exploitation abilities. Simulation results obtained based on thirteen benchmark functions and two practical economic dispatch problems prove the effectiveness and applicability of the DPSOEP to deal with non-convex and piecewise optimization problem, considering the integration of linear equality and inequality constraints.