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This paper proposed a circular particle swarm optimization least squares (CPSOLS) method which is consisted of the regularized least squares (RLS) method and the adaptive particle swarm optimization (APSO) algorithm. The RLS algorithm optimized the parameters of the RBF network, aiming at the phenomenon of RLS trapping in the local minimum, introduced the penalty factor and used the global optimization ability of the particle swarm optimization algorithm to make it out of the local minimum; simplified the structure of the RBF network and improved the generalization ability of the network. The APSO algorithm weakened the precocious converge phenomena of the particle swarm optimization algorithm, adopted the adaptive selection of the nonlinear dynamic inertia weight which is guided by the control factor of the battery external characteristic temperature parameters, optimized the link weight of the RBF network, improved the state of charge (SOC) estimation accuracy and real-time performance of the RBF network. Using the Arbin multifunctional battery test system BT2000 to collect the sample data of the battery external characteristic parameters, and using the sample data to train and optimize the RBF neural network, and estimate the SOC of the batteries. The results showed that the optimized RBF network improved the SOC estimation accuracy and real-time performance.

This paper aims to present a self-adaptive global particle swarm optimization (SGPSO) algorithm for solving unconstrained optimization problems. In the new algorithm, the inertia weights are generated based on Gaussian distribution, which is helpful to improve the diversity of the population. In addition, the worst particle is updated by averaging the other particles, which is beneficial to improving the quality of the population. Finally, a global disturbance is adopted to increase the convergence rate of SGPSO. In the disturbance process, a disturbance factor is utilized to control the searching ranges of the population, which can effectively keep a balance between the global exploration and local exploitation. Twenty well-known benchmark functions are considered to evaluate the performance of SGPSO, and 50 runs are implemented in each case. Numerical experiments and comparisons demonstrate that SGPSO is superior to the other three algorithms according to means, standard deviations and convergence rate.

Location problem of multi-distribution center is a kind of NP hard problem. To solve such problems, this paper proposes a chaos adaptive mutation particle swarm optimization algorithm. The algorithm uses the ergodic property of chaos to initialize the particle swarm to enhance the diversity of the population, according to the variance of population fitness to adjust the probability of mutation, and adjust the inertia weight factor to improve the global and local search capability of the whole population. In this paper, the algorithm is applied to the location problem of multi-distribution center, established the multi-factor constraints of mathematical model which aiming at timeliness, and on this basis, the corresponding algorithm is designed. It can be seen from the location instance simulation results that the optimization results and efficiency of the adaptive mutation particle swarm optimization algorithm is better than the genetic algorithm and the standard particle swarm optimization algorithm.

Fuzzy clustering is a popular unsupervised learning method used in cluster analysis which allows a point in large data sets belongs to two or more clusters. Prior work suggests that Particle Swarm Optimization based approach could be a powerful tool for solving clustering problems. In this paper, we propose a data clustering algorithm based on modified adaptive particle swarm optimization. We choose to use artificial bee colony algorithm combined with PSO technique to modify the traditional clustering methods due to its fast convergence and the presence of adaptive mechanisms based on the evolutionary factor. On the one hand, Particle Swarm Optimization is proven to be an effective and robust technique for fuzzy clustering. On the other hand, the artificial bee colony algorithm has the capability to generate diversity within the swarm when the guide bees are in the exploration mode. Through numerical analysis and experimental simulation, we verify that our algorithm performs much better compared with other state-of-the-art algorithms. Future research schedule is also discussed in the final part.

With the continuous development of computer science and technology, image processing and analysis gradually form the scientific system. Although history of image processing is not long, it attracts many researchers study on it. Digital media image widely exists in many fields, such as education, video, advertisement, and so on. Process digital media image is an important part of image processing. When analyze the digital media image, we want to extract the image part we care from the original image and then method for image segmentation is quite important. That is to say that the image segmentation will divide the image into a number of regions with specific and unique nature. How to keep the original characteristics of the digital media image is quite important in the image segmentation. In this paper, we propose a new algorithm for digital media image segmentation, and it is also can be used in the image processing. The algorithm is based on asynchronous particle swarm optimization algorithm to obtain the adaptive threshold; take the inertia factor into the algorithm, the optimal threshold has been acquired for the image segmentation. Compared with other particle swarm optimization algorithm, the algorithm has the advantages of stable, easy to converge to the optimal solution, and high segmentation speed.

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

위상 배열 안테나를 이용한 적응형 빔 형성 시스템은 간섭신호가 있는 통신환경에 적응형으로 빔을 형성하여 통신 품질을 향상시킨다. 적응형 빔 형성을 위해서는 위상 배열 안테나의 각 방사소자에 급전되는 신호의 위상을 우수한 조합을 산출해야 한다. 본 논문에서는 우수한 위상 천이 조합 산출 확률을 증가시키기 위해, 개치 밀도에 따른 재확산 절차가 추가된 개선된 개체 군집 최적화 알고리즘을 제안하였다.

An adaptive beam forming system using a phased array antenna improves communication quality by beam forming adaptively to a communication environment having an interference signal. For adaptive beam forming, a good combination of the phases of the excited signals to each radiating element of the phased array antenna should be calculated. In this paper, improved particle swarm optimization algorithm that adds a re-spreading procedure according to particle density was proposed to increase the probability of good phase shift combination output.