Adoption of computer particle swarm optimization algorithm under thermodynamic motion mechanism

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 ( U [ 0 , 1 ] , U [ − 1 , 1 ] and G ( 0 , 1 ) ), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.

In order to overcome the drawbacks of falling into local extremum and lower optimization precision of standard particle swarm optimization (PSO) algorithm, multipopulation strategy, adaptive dynamic adjustment strategy and co-evolution mode are introduced into the standard PSO algorithm in order to propose an improved coevolutionary PSO(MPACEPSO) algorithm based on multi-strategy evolution mode and multi-population co-evolutionary mechanism. In the evolutionary process of MPACEPSO algorithm, the multi-population strategy is used to divide the population into several subpopulations, which use different co-evolutionary model to evolve. These sub-populations are influenced and promoted each other in order to realize the exchange of information and co-evolution among the sub-populations, improve the convergence speed and search precision of MPACEPSO algorithm, and effectively suppress the appearance of the local optimum. The adaptive dynamic adjustment strategy of inertia weight is used to keep the diversity of population, reduce the probability of falling into the local extremum. Finally, the ZDT functions are selected to test the optimization performance of proposed MPACEPSO algorithm. The experimental results show that the proposed MPACEPSO algorithm has faster convergence speed, stronger global search ability, higher solving precision and better dynamic optimization performance. The experimental result analysis shows that the proposed MPACEPSO algorithm is insensitive to parameters and easy to be used in solving the complex optimization problems.

Flatness error is an important factor for effective evaluation of surface quality. The existing flatness error evaluation methods mainly evaluate the flatness error of a small number of data points on the micro scale surface measured by CMM, which cannot complete the flatness error evaluation of three-dimensional point cloud data on the micro/nano surface. To meet the needs of nano scale micro/nano surface flatness error evaluation, a minimum zone method on the basis of improved particle swarm optimization (PSO) algorithm is proposed. This method combines the principle of minimum zone method and hierarchical clustering method, improves the standard PSO algorithm, and can evaluate the flatness error of nano scale micro/nano surface image data point cloud scanned by atomic force microscope. The influence of the area size of micro/nano surface topography data on the flatness error evaluation results is analyzed. The flatness evaluation results and measurement uncertainty of minimum region method, standard least squares method, and standard PSO algorithm on the basis of the improved PSO algorithm are compared. Experiments show that the algorithm can stably evaluate the flatness error of micro/nano surface topography point cloud data, and the evaluation result of flatness error is more reliable and accurate than standard least squares method and standard PSO algorithm.

The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions.

Evrimsel Algoritmalar (EA’lar), ağ tasarımı problemleri, yol bulma problemleri, sosyal ve ekonomik planlama gibi karmaşık optimizasyon problemlerini çözmek için yaygın olarak kullanılan algoritmalardır. Kullandıkları akıllı yöntemler sayesinde EA’lar, yeni çözümler üretmek için ilk çözümü, yinelemeli olarak, iyileştirirler. Sıklıkla kullanılan EA'lardan biri Parçacık Sürü Optimizasyonu (PSO) algoritmasıdır. PSO algoritması, sürünün sunduğu en iyi sonuç ile sürüdeki her bir bireyin en iyi sonuçlarını temel alarak, verilen bir fonksiyonunun optimum değerine ulaşır. PSO başarılı bir algoritma olmasına rağmen, sadece en iyiyi temel alan yapısı sebebiyle, belli bir düzene sahip olmayan, aldatıcı fonksiyon türlerinde, lokal değere sıkışıp optimum değere ulaşmayabilmektedir. Bu makalede, daha önce yapmış olduğumuz bir çalışmadan ilham alarak, PSO’ya bipolar davranış eklenerek, yeni bir algoritma olan Bipolar Parçacık Sürü Optimizasyonu (BPSO) sunulmuştur. BPSO algoritmasında, PSO’da olduğu gibi sadece en iyi bireylerin değil, kötü bireylerin de algoritmanın işleyişine katılmasına olanak sağlanmıştır. BPSO algoritmasının performansını, standart PSO algoritmasının performansıyla ile kıyaslamak için on test fonksiyonu kullanılmıştır. Test sonuçlarına göre, BPSO, standart PSO’ya göre daha başarılı sonuçlar sunmuştur.

Collaborative particle swarm optimization with a data mining technique for manufacturing cell design

Since the last years different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing Cell Formation using a modified particle swarm optimisation (PSO) algorithm. The main modification made to the original PSO algorithm consists on that in this work it is not used the vector of velocities as the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining techniques. Some simulations are presented and compared. The criterion used to group the machines in cells is based on the minimization of inter-cell movements. The computational results show that the PSO algorithm is able to find the optimal solutions on almost all instances.

Improved Adaptive PSO-based Gain Tuning for PID Controllers in Utility Boilers

Selection of right partners is vital for virtual enterprise (VE) to take the advantage of market opportunity. Partner selection problem belongs to combinatorial optimisation category and known as NP-hard. A modified discrete particle swarm optimisation (PSO) algorithm is proposed, in this work, to select alliances in a VE. The velocity terms, which is commonly used in the standard PSO algorithm, is avoided; rather, the concept of proportional likelihood with modifications, a method used in data mining techniques, is adopted to improve solution quality. A set of experiments have been conducted and results indicate PSO algorithm is an effective and efficient way to solve the partner selection problems with precedence, risk of failure and due date constraints. A design of experiment (DoE) approach is adopted to study statistical significance of various model parameters on solution quality.

Standard Particle Swarm Optimization(PSO) algorithm falls into local optima easily and has low convergence accuracy when it is used to address the problem of complex functions optimization.In order to overcome the shortcomings,an improved PSO algorithm was proposed.The proposed algorithm integrated the attraction-repulsion mechanism in the field of biology into PSO algorithm and took full advantage of the mutual influence between particles to modify velocity updating formula,and thus maintained population diversity and enhanced the ability of particle to escape from the local optima.The experimental results demonstrate that the proposed algorithm outperforms two existing variants of the PSO algorithm in terms of convergence accuracy while improving the velocity of convergence in the later evolution phase and avoiding premature convergence problem effectively.

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - DEPSO is introduced. DEPSO is a standard particle swarm optimization (PSO) algorithm with the addition of a differential evolution step to aid in swarm convergence. The results from DEPSO are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but DEPSO provides better learning capabilities for the functions generated by MLPs and SRNs as compared to BP and PSO. These three algorithms are also trained on several benchmark functions to confirm results.

In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) and Cuckoo Search (CS) is detailed with the application of Optimal Reactive Power Dispatch (ORPD). The Levy flight and the elimination mechanism are introduced to the traditional PSO algorithm. Benchmark tests show that the hybrid algorithm has better optimization efficiency than original PSO and CS algorithms. In the application of the ORPD, the fitness function is the total real loss of the distribution network. Inequality constrains are handled by the limitation of searching space. The simulation results on the IEEE-14 bus standard system and a real distribution network illustrate the feasibility and effectiveness of the algorithm. The total real power losses decrease 7.1% and 8% in two applications. The mathematical tests and engineering applications demonstrate that the hybrid algorithm has better searching ability than the standard PSO algorithm, and it is suitable for the solution of ORPD problem.

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. This paper presents an approach, based on Particle Swarm Optimization (PSO) algorithm, to effectively address the assembly line balance problem with the complicating factors of parallel workstations. The method of linearly decreasing the inertia weight of the PSO (LDWPSO) is used in this paper to compare with the standard PSO. Experimental results show that the LDWPSO algorithm is superior to the standard PSO on some assembly line balancing problem.