Global Optimal Position Research Articles

Gyro fireworks algorithm: A new metaheuristic algorithm

In this paper, a novel Gyro Fireworks Algorithm (GFA) is proposed by simulating the behaviors of gyro fireworks during the display process, which adopts a framework of multi-stage and multiple search strategies. At the beginning of the iteration, the gyro fireworks are full of gunpowder; they move via Lévy flight and spiral rotation, and the sprayed sparks are widely distributed and more balanced, which is an effective global exploration method. In the later iteration stages, due to the consumption of gunpowder, the gyro fireworks gradually undergo aggregation and contraction of spiral rotation, which is conducive to the search group to exploit the local area near the global optimal position. The GFA divides the iterative process into four phases, and each phase adopts a different search strategy, in order to enhance the diversity of the search of the population and to balance the exploration capability of the gyro fireworks search group in the global space and the exploitation of the local space. In order to verify the performance of the GFA, it is compared with the latest algorithms, such as the dandelion optimizer, Harris Hawks Optimization (HHO) algorithm, gray wolf optimizer, slime mold algorithm, whale optimization algorithm, artificial rabbits optimization, in 33 test functions. The experimental results show that the GFA obtains the optimal solution for all algorithms on 76% of the functions, while the second-placed HHO algorithm obtains the optimal solution for all algorithms on only 21% of the functions. Meanwhile, the GFA has an average ranking of 1.8 on the CEC2014 benchmark set and 1.4 on the CEC2019 benchmark set. It verifies that the GFA proposed in this paper has better convergence performance and better robustness than the competing algorithms. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the GFA over alternative algorithms.

Distributed Cooperative Quantum Learning for Discrete-Time Multiagent Source Exploration With Information Prompts.

In light of optimization theory and swarm evolutionary schemes, under multiple single-integrator mobile agents equipped with sensors and prompters, this article addresses a discrete-time multiagent source exploration problem with information prompts. Regarding information prompts as constraints on the unknown target, by virtue of penalty function skills (PFSs) and sequential unconstrained minimization techniques (SUMTs), the agents are driven toward the source under the guidance of the control strategy. In two cases of available and unavailable gradient information, a quantum potential well, an average optimal position estimator (AOPE), and a global optimal position estimator (GOPE) are introduced into swarm evolutionary schemes with a periodically oscillating weight, such that distributed cooperative quantum learning (DCQL) policy is proposed as a control strategy under communication restrictions, where AOPE and GOPE are developed relying on distributed consensus theory. In particular, when the gradient is unavailable, we put forth an adaptive generalized Bernstein neural network (AGBNN) to replace it based on excellent properties of Bernstein polynomials and adaptive approaches. Further, a performance analysis for the proposed policy is executed on the convergence and computational complexity, which ensures the accuracy and efficiency of the source exploration in theory. Ultimately, a simulation test is carried out, and the results validate the practicability and effectiveness of the offered method.

Multistrategy Fusion Particle Swarm for Dynamic Economic Dispatch Optimization of Renewable Energy Sources

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching.

The Role of English Language and Literature in the Cultivation of Students’ Language Proficiency in the Context of Deep Learning

Abstract In this paper, the PSO-LSTM algorithm is used to construct a model for evaluating students’ language proficiency, and the improvement of the acceleration factor and inertia weights is used to adjust the influence of the individual optimal position and the global optimal position in the speed update. For model performance judgment after optimization of LSTM hyperparameters, the PSO fitness function is replaced by MAPE and MSE functions. Finally, the student’s English composition texts were used as experimental parameters and input into the language proficiency evaluation model to verify the model performance and analyze the influence of English language literature on language proficiency. The results show that English language literature is effective in predicting 18.55% of the variance in English proficiency level for self-efficacy, 8.17% of the variance in English proficiency level for language attitude, and 0.47% of the variance in English proficiency level for learning anxiety, which indicates that English language literature has a significant effect on students’ English proficiency. This paper investigates the role mechanism of the English language and literature in the cultivation of student’s English proficiency, which provides a theoretical reference for the reform of English teaching in colleges and universities.

Optimization of furniture combination design and space configuration based on graph theory

Abstract This paper is based on graph theory, combined with a particle swarm optimization algorithm to find the optimal solution in the whole particle swarm through continuous iteration and obtain the sum of squares of each variable in the particle cognitive domain. Searching for the global optimal position calculates the inertia weights in the particle swarm algorithm and changes the velocity and position of the particles based on the motion properties of graph theory to improve the global search ability. The domain competition operator is executed after particle swarm initialization, and a hybrid crossover strategy is used to select a crossover point, which improves the algorithm’s performance by exchanging information with the global optimum. Based on graph theory, the spatial configuration optimization of furniture combinations is scored, in which the average score of furniture space ranges from 3.3 to 6.2, and the average score of spatial configuration optimization ranges from 4.5 to 8. Therefore, based on graph theory, the reasonable furniture form for spatial layout optimization can fully reflect the practical function of the space, comfort, and the creation of a spatial atmosphere.

Quasi opposite-based learning and double evolutionary QPSO with its application in optimization problems

Although quantum-behaved particle swarm optimization (QPSO) algorithm has the advantages of few parameters and simple implementation, it suffers from these problems of low precision while calculating high-dimensional complex problems and being caught in local optimum during the later stage of iteration. To tackle these shortages effectively, a modified QPSO (QDQPSO) algorithm is presented. In order to improve the search efficiency and convergence speed of the algorithm, the idea of quasi opposite-based learning is used in the initialization stage. For enhancing the overall performance of the algorithm, the double evolutionary mechanism is applied to update the individual location during the iterative process. Furthermore, perturbation at global optimum position and bound constraint handling are considered to help the algorithm to escape from local optimum and maintain the diversity of population. According to the results obtained by QDQPSO and other nine optimization algorithms on 28 benchmark functions under different dimensions, it is found that QDQPSO performs better on the accuracy and stability of the optimal solution. Subsequently, Wilcoxon rank-sum test and Friedman test demonstrate significant advantages of the improved algorithm. Finally, QDQPSO algorithm displays superior performance in solving five practical optimization problems compared to several optimization methods.

A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness.

Path Planning of Mobile Robots Based on an Improved Particle Swarm Optimization Algorithm

Aiming at disadvantages of particle swarm optimization in the path planning of mobile robots, such as low convergence accuracy and easy maturity, this paper proposes an improved particle swarm optimization algorithm based on differential evolution. First, the concept of corporate governance is introduced, adding adaptive adjustment weights and acceleration coefficients to improve the traditional particle swarm optimization and increase the algorithm convergence speed. Then, in order to improve the performance of the differential evolution algorithm, the size of the mutation is controlled by adding adaptive parameters. Moreover, a “high-intensity training” mode is developed to use the improved differential evolution algorithm to intensively train the global optimal position of the particle swarm optimization, which can improve the search precision of the algorithm. Finally, the mathematical model for robot path planning is devised as a two-objective optimization with two indices, i.e., the path length and the degree of danger to optimize the path planning. The proposed algorithm is applied to different experiments for path planning simulation tests. The results demonstrate the feasibility and effectiveness of it in solving a mobile robot path-planning problem.

Разработка модели оптимизации выбора площадки базовой станции на основе алгоритма PMET-PSO

Existing research in the field of mathematical modeling of base station planning, using intelligent optimization algorithms to solve potential schemes for generating base station distributions, is complex and, for the most part, mathematical models for choosing a base station site require simplification. Also, the existing models for choosing the base station site solve the problem in two-dimensional space and do not take into account the influence of terrain and other factors. Having incorporated the concept of membrane computing into the basis of the MET-PSO algorithm, the PMET-PSO algorithm was developed. The PMET-PSO algorithm is a redesign of the METROPOLIS sample in the simulated annealing algorithm, projects the probability of accepting METROPOLIS in accordance with the number of iterations of the particle swarm algorithm and determines whether to obtain an individual optimal position and a global optimal position generated by a new round of iterations in accordance with probability, thereby adding randomness to the particle swarm algorithm. As the number of iterations increases, the randomness of the particle swarm algorithm decreases, and it converges to an optimal solution. PMET-PSO allows parallel computing, which effectively reduces the time complexity of the MET-PSO algorithm.

Harris hawks optimization based on global cross-variation and tent mapping.

Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms.

Mining Plan Optimization of Multi-Metal Underground Mine Based on Adaptive Hybrid Mutation PSO Algorithm

Mine extraction planning has a far-reaching impact on the production management and overall economic efficiency of the mining enterprise. The traditional method of preparing underground mine production planning is complicated and tedious, and reaching the optimum calculation results is difficult. Firstly, the theory and method of multi-objective optimization are used to establish a multi-objective planning model with the objective of the best economic efficiency, grade, and ore quantity, taking into account the constraints of ore grade fluctuation, ore output from the mine, production capacity of mining enterprises, and mineral resources utilization. Second, an improved particle swarm algorithm is applied to solve the model, a nonlinear dynamic decreasing weight strategy is proposed for the inertia weights, the variation probability of each generation of particles is dynamically adjusted by the aggregation degree, and this variation probability is used to perform a mixed Gaussian and Cauchy mutation for the global optimal position and an adaptive wavelet variation for the worst individual optimal position. This improved strategy can greatly increase the diversity of the population, improve the global convergence speed of the algorithm, and avoid the premature convergence of the solution. Finally, taking a large polymetallic underground mine in China as a case, the example calculation proves that the algorithm solution result is 10.98% higher than the mine plan index in terms of ore volume and 41.88% higher in terms of economic efficiency, the algorithm solution speed is 29.25% higher, and the model and optimization algorithm meet the requirements of a mining industry extraction production plan, which can effectively optimize the mine’s extraction plan and provide a basis for mine operation decisions.

A Multi-Strategy Improved Sparrow Search Algorithm for Solving the Node Localization Problem in Heterogeneous Wireless Sensor Networks

Aiming at the problems of slow convergence and low accuracy of the traditional sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA) was proposed. Firstly, the golden sine algorithm was introduced in the location update of producers to improve the global optimization capability of SSA. Secondly, the idea of individual optimality in the particle swarm algorithm was introduced into the position update of investigators to improve the convergence speed. At the same time, a Gaussian disturbance was introduced to the global optimal position to prevent the algorithm from falling into the local optimum. Then, the performance of the ISSA was evaluated on 23 benchmark functions, and the results indicate that the improved algorithm has better global optimization ability and faster convergence. Finally, ISSA was used for the node localization of HWSNs, and the experimental results show that the localization algorithm with ISSA has a smaller average localization error than that of the localization algorithm with other meta-heuristic algorithms.

A fast 3D-MUSIC method for near-field sound source localization based on the bat algorithm

To improve the computation and real-time performances of the multiple signal classification (MUSIC) algorithm in 3D space, a fast sound source localization method based on the bat algorithm (BA) and the 3D-MUSIC, called BA-based 3D-MUSIC algorithm (3D-BMUSIC), is presented in this paper. 3D-BMUSIC greatly reduces the computation load by replacing the regular grid search with the BA. First, the near-field spherical wave model is established to obtain the spectral function of the 3D-MUSIC. Then, the spectral function is defined as the fitness function, which calculates the fitness value corresponding to each bat position. Finally, the global optimal bat position with the largest fitness value, as sound source localization, is obtained by successive iteration and sorting. The simulation and experiment show that 3D-BMUSIC accurately estimates the DOA and distance of near-field sources, and the root-mean-square error (RMSE) of 3D-BMUSIC is less than that of 3D-MUSIC. In addition, 3D-BMUSIC effectively reduces the computation time by approximately 96–98%. With shorter computation time and higher efficiency, 3D-BMUSIC promotes hardware implementation and is more suitable for high-precision localization of near-field sound sources.

Coverage Control Algorithm for DSNs Based on Improved Gravitational Search

In directional sensor networks (DSNs), coverage control is an important way to ensure efficient communication and reliable data transmission. Aiming at enhancing the DSNs coverage performance, a coverage control algorithm based on improved gravitational search (IGSCCA) was proposed. Firstly, in the coverage enhancement phase, aiming at the defects of gravitational search algorithm that it is easy to fall into local optimum and slow convergence speed when solving the coverage enhancement problem, cosine decay function is used to improve the gravitational function, the global optimum position is introduced into the velocity updating of gravitational search algorithm, and use the uncovered grid points attractive force to disturb the worst particles. Secondly, in the node sleep phase, the node sleep strategy based on security set is introduced to reduce the network redundancy rate. Finally, the simulation results showed that this paper has great advantages in coverage rate, energy utilization rate and redundancy rate.

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research.

Optimal energy allocation strategy for electric vehicles based on the real-time model predictive control technology

Electric vehicles (EVs) are regarded as the clean transportation due to their wide promising application in the energy conservation and vehicle safety. However, how to allocate the battery energy to four individual in-wheel motors is a challenging job. In this paper, a dynamic energy management strategy of the EV is proposed to optimize the battery energy consumption and to reduce the tire slip loss simultaneously. Basically, nonlinear model predictive control is utilized to identify the tire dynamics and vehicle load. Then, the multi-objective optimization problem with the nonlinear constraints is addressed with the modified particle swarm optimization (MPSO) algorithm in which the inertia weight of particle velocity and the acceleration coefficient are further altered for the real-time calculation. Furthermore, the modification of the global optimal position of the population can effectively avoid the local optima dilemma. The numerical test is implemented under US06 and WLTC03 maneuvers to validate the superiority of the proposed energy strategy. The results demonstrate that the proposed dynamic energy strategy can automatically allocate the torque requirement according to the dynamic load and put more weights on the front wheels in the braking condition. Compared with the typical rule-based strategy, the proposed strategy can conserve 12 ∼ 17% of the battery energy and reduce approximate13% of tire slip loss. Moreover, the modified PSO algorithm can reduce the computational time by 55% which further validates its application value in the real-time energy management of EVs.

A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

PurposeTo solve the path planning problem of the intelligent driving vehicular, this paper designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO).Design/methodology/approachFirst, the authors optimized the hyper-parameters of RL to make it converge quickly and learn more efficiently. Then the authors designed a pre-set operation for PSO to reduce the calculation of invalid particles. Finally, the authors proposed a correction variable that can be obtained from the cumulative reward of RL; this revises the fitness of the individual optimal particle and global optimal position of PSO to achieve an efficient path planning result. The authors also designed a selection parameter system to help to select the optimal path.FindingsSimulation analysis and experimental test results proved that the proposed algorithm has advantages in terms of practicability and efficiency. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.Originality/valueThe authors designed a pre-set operation to reduce the participation of invalid particles in the calculation in PSO. And then, the authors designed a method to optimize hyper-parameters to improve learning efficiency of RL. And then they used RL trained PSO to plan path. The authors also proposed an optimal path evaluation system. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification

In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. To solve this problem, this paper proposes a novel multimodal biometric identification system for face-iris recognition.It is based on binary particle swarm optimization. The face features are extracted by 2D Log-Gabor and Curvelet transform, while iris features are extracted by Curvelet transform. In order to reduce the complexity of the feature-level fusion, we propose a modified chaotic binary particle swarm optimization (MCBPSO) algorithm to select features. It uses kernel extreme learning machine (KELM) as a fitness function and chaotic binary sequences to initialize particle swarms. After the global optimal position (Gbest) is generated in each iteration, the position of Gbest is varied by using chaotic binary sequences, which is useful to realize chaotic local search and avoid falling into the local optimal position. The experiments are conducted on CASIA multimodal iris and face dataset from Chinese Academy of Sciences.The experimental results demonstrate that the proposed system can not only reduce the number of features to one tenth of its original size, but also improve the recognition rate up to 99.78%. Compared with the unimodal iris and face system, the recognition rate of the proposed system are improved by 11.56% and 2% respectively. The experimental results reveal its performance in the verification mode compared with the existing state-of-the-art systems. The proposed system is satisfactory in addressing face-iris multimodal biometric identification.

Quasi‐Affine Transformation Evolutionary with Double Excellent Guidance

The Quasi‐Affine Transformation Evolutionary (QUATRE) algorithm is a swarm‐based collaborative optimization algorithm, which has drawn attention from researchers due to its simple structure, easy implementation, and powerful performance. However, it needs to be improved regarding the exploration, especially in the late stage of evolution, and the problem of easy falling into a local optimal solution. This paper proposes an improved algorithm named Quasi‐Affine Transformation Evolutionary with double excellent guidance (QUATRE‐DEG). The algorithm uses not only the global optimal solution but also the global suboptimal solution to guide the individual evolution. We establish a model to determine the guiding force by the distance between the global optimal position and the suboptimal position and propose a new mutation strategy through the double population structure. The optimization of population structure and the improvement of operation mechanisms bring more exploration for the algorithm. To optimize the algorithm, the experiments on parameter settings were made to determine the size of the subpopulation and to achieve a balance between exploration and development. The performance of QUATRE‐DEG algorithm is evaluated under CEC2013 and CEC2014 test suites. Through comparison and analysis with some ABC variants known for their strong exploration ability and advanced QUATRE variants, the competitiveness of the proposed QUATRE‐DEG algorithm is validated.

Multi-objective optimization of space adaptive division for environmental economic dispatch

With the increasingly severe global energy and environmental issues, the traditional economic load dispatching with the single goal of power generation cost has been unable to meet the national macro strategic requirements for energy conservation and emission reduction. This research mainly discusses the multi-objective optimization of spatial adaptive partitioning based on environmental economic dispatch problems. This study uses the method of calculating power generation costs and pollution control costs to select a compromise solution. When initializing the individual optimal position memory bank and the global optimal position memory bank, put the first-generation particle position into the individual optimal position memory bank, and put the optimal position obtained by comparing the first-generation particle into the global optimal position. In the location memory. Then calculate the fitness value corresponding to each objective function of each particle, compare the calculated result with the corresponding results of each Pareto solution in the internal memory bank and the external memory bank, and replace the particle position with the best. In the multi-objective processing strategy, in order to better obtain the Pareto optimal solution set and the Pareto optimal frontier, an external archive is set up to save and update the new elite Pareto optimal solution generated after each iteration. Since the quality of the particles in the random simulation particle swarm algorithm will directly affect the convergence and diversity of the final Pareto optimal solution set, this study selects the historical optimal particles according to the Pareto dominance relationship. When the multi-objective optimized power system economic dispatch research model with wind farms was adopted, the cost of power generation increased by 6.21 million yuan, the cost of polluting gas treatment was reduced by 8.1 million yuan, and the total cost was reduced by 1.89 million yuan. The research results show that the multi-objective optimization model proposed in this research has achieved a relatively satisfactory balance between power generation costs and environmental advantages.