3-D Inversion Based on the Particle Swarm Optimization-Quasi-Newton Hybrid Algorithm for SOTEM

Geophysical methods, which use physical properties to reveal the structure of the underground spaces, have been widely applied in geological exploration. However, a single geophysical approach only reflects the distribution of a kind of physical property and therefore has its limitations. To improve the reliability of geophysical methods in actual geological exploration, in this paper, we are trying to combine the use of a variety of geophysical exploration methods, such as gravity, control source audio magnetotelluric method (CSATMT) and electrical source short offset transient electromagnetic method (SOTEM), to a specific metal in Inner Mongolia. Firstly, the gravimetry method is applied to figure out the anomalous area, and then the CSAMT method is adopted to probe the deep electrical distribution. Finally, the SOTEM method is applied for further auxiliary verification. Results show that the underground structure is well revealed by combing the use of these methods. This paper can be used as a reference for the exploration of concealed ore bodies in polymetallic ore deposits. To use a combination of methods to measure. In the geological exploration of a polymetallic silver-copper mine in Inner Mongolia, a variety of geophysical exploration methods such as gravity measurement, controllable source audio magnetotelluric measurement and electrical source short offset transient electromagnetic method were combined, and geological data were combined. The better exploration results provide reference for the layout of the drilling location of the drilling project, and provide assistance for the prospecting breakthrough of the concealed ore body in the polymetallic mining area.

Traditional gradient descent inversion for the short offset transient electromagnetic method (SOTEM) is inefficient, since the computation of the forward modeling and Jacobian matrix is more complex and time consuming than the conventional TEM with the loop source. In this paper, the application of the supervised descent method (SDM) for accelerating the SOTEM inversion is studied. During an offline training stage, the average descent direction can be learned by a set of training models. In an online predicting stage, the inversion process is accelerated by directly using the learned descent directions instead of computing the Jacobian matrix. Numerical examples verify the feasibility and efficiency of SOTEM inversion with SDM.

Dynamic path planning for unmanned surface vehicle in complex offshore areas based on hybrid algorithm

Hybrid Bat & Particle Swarm Algorithm for optimization of labyrinth spillway based on half & quarter round crest shapes

Traditionally, most companies use marketing campaigns to recruit new customers or retain old customers. Customer segmentation is an important technique for a marketing campaign to target the right customers. Most previous clustering algorithms have drawbacks, such as being stuck at local minima. To overcome such drawbacks this study attempts to develop a consumer segmentation model using a swarm intelligence based algorithm, called Particle Swarm Optimization (PSO). The swarm intelligent algorithm has the advantage of using fewer parameters to reach a global optimal solution. In general, the value of customer segmentation is judged by the customer’s lifetime value. Based on many previous researches, the RFM (Recency, Frequency, and Monetary) model is the most well-known model used to compute customer lifetime value. This study calculates the RFM model from a data set into value-based information. Based on this value-based information the PSO algorithm is able to cluster consumers to find customers likely to be the most profitable and valuable. To demonstrate the effectiveness of PSO, we present an empirical case study involving a retail automobile marketing campaign. We compare the performance of the PSO customer segmentation algorithm against that of other segmentation algorithms (K-mean and self-organizing map (SOM)) and hybrid algorithms. The study finds the hybrid S-KMeans -PSO (SOM, K-Means and PSO) algorithms can reach the best performance. Finally, this study proposes effective marketing strategies for two segmented profitable and valuable customers.

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

Recently, the use of the particle swarm optimization (PSO) technique for the reconstruction of microwave images has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, the basic PSO algorithm is easily trapping into local minimum and may lead to the premature convergence. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes di-cult. To overcome the premature convergence of PSO, we propose a new hybrid algorithm of particle swarm optimization (PSO), simulated annealing (SA) and tabu search algorithm (TS) for solving the scattering inverse problem. The incorporation of tabu search (TS) and simulated annealing (SA) as local improvement approaches enable the hybrid algorithm to overleap local optima and intensify its search ability in local regions. Reconstructions of dielectric scatterers from experimental inverse- scattering data are flnally presented to demonstrate the accuracy and e-ciency of the hybrid technique.

Particle Swarm Optimization (PSO) is gaining momentum as a simple and effective optimization technique. However, its performance on complex problem with multiple minima falls short of that of the Ant Clony Optimization (ACO) algorithm. The new algorithm, which we call Hybrid Particle Swarm Optimization, combines the ideas of particle swarm optimizati-on with clonal selection strategy and simplified quadratic interpolation (SQI), which is used to improve its local search ability, and to escape from the local optima. Simulation results on 14 benchmark test functions show that the hybrid algorithm is able to avoid the premature convergence and find much better solutions with high speed.

Hybrid particle swarm optimization (PSO) is such a kind of algorithms, which combine other algorithms or technologies to enhance the basic PSO. This paper compares three representative hybrid PSOs: breeding PSO, immune PSO, and chaos PSO. These comparisons are carried out in following aspects: the goals for hybrid, basic hybrid modes, the key implementation steps, optimization performances and so on. Through these comparisons and analysis, three hybrid algorithms' construction, implementation and respective application scope are summarized, especially, the optimization performances of three hybrid PSOs have been researched carefully. The goal is to find features of the three HPSOs and provide some guidence to develop new kinds of HPSO.

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

BP network is one of the most popular artificial neural networks because of its special advantage such as simple structure, distributed storage, parallel processing, high fault-tolerance performance, etc. However, with its extensive use in recent years, it is discovered that BP algorithm has the defects on slow convergent speed and easy convergence to a local minimum point. The paper proposes a method of BP Neural Network improved by Particle Swarm Optimization (PSO). The hybrid algorithm can not only avoid local minimum, but also raise the speed of network training and reduce the convergence time.

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.

In our previous research applied to the job shop scheduling problem (JSSP) for machine tool production, the multi-objective optimisation model based on the particle swarm optimization (PSO) research had an imbalance performance between the convergence rate and the convergence precision. In this article, a new hybrid algorithm using PSO and generic algorithm (GA) is proposed to solve this particular problem. In this new algorithm, named hybrid PSO-GA algorithm (HPGA), the PSO algorithm is redefined and modified by introducing genetic operators, i.e. the crossover operator and the mutation operator, to update the particles in the population. The HPGA is then applied in heavy machinery company with minimising machines’ makespan and minimising jobs’ tardiness as the two optimal objectives. The comparisons with actual application report have illustrated that the proposed HPGA can obtain higher quality of schedule solution for machine tool production. Furthermore, with solution quality and convergence rate as the two estimation measurements metrics, some comparisons are performed in order to illustrate that the HPGA has superiority over the PSO, GA and simulated annealing algorithm (SA). Results have indicated that, with the combination of the merits of PSO and GA, the proposed HPGA approach can achieve not only better solution quality but also faster convergence rate than the PSO, GA and SA, within a reasonable computation time for high dimensions JSSP.

A hybrid learning algorithm based on simplex method and particle swarm optimization is proposed to train the feedforward neural network in this paper. In the given hybrid algorithm the simplex method which has expansion function and contraction function is embedded in the particle swarm optimization as an operator. Through cross-training mode to train neural network, this hybrid algorithm selects limited elitist particles and executes simplex operator for local searching during each generation of particle swarm optimization, which can make the neural network learning approximate to the global optimum region rapidly and find more excellent solution. The simulation experiments show that comparing with some traditional learning methods this hybrid algorithm enhances the convergence speed and training precision, and improves network performance. It is an effective neural network learning method.

Particle swarm optimization (PSO) and Ant Colony Optimization (ACO) are two important methods of stochastic global optimization. PSO has fast global search capability with fast initial speed. But when it is close to the optimal solution, its convergence speed is slow and easy to fall into the local optimal solution. ACO can converge to the optimal path through the accumulation and update of the information with the distributed parallel global search ability. But it has slow solving speed for the lack of initial pheromone at the beginning. In this paper, the hybrid algorithm is proposed in order to use the advantages of both of the two algorithm. PSO is first used to search the global solution. When it maybe fall in local one, ACO is used to complete the search for the optimal solution according to the specific conditions. The experimental results show that the hybrid algorithm has achieved the design target with fast and accurate search.