Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.

An intelligent market segmentation system using k-means and particle swarm optimization

Abstract: With the development of information technology (IT), how to find useful information existed in vast data has become an important issue. The most broadly discussed technique is data mining, which has been successfully applied to many fields as analytic tool. Data mining extracts implicit, previously unknown, and potentially useful information from data. Clustering is one of the most important and useful technologies in data mining methods. Clustering is to group objects together, which is based on the difference of similarity on each object, and making highly homogeneity in the same cluster, or highly heterogeneity between each group. In this paper, we propose a market segmentation system based on the structure of decision support system which integrates conventional statistic analysis method and intelligent clustering methods such as artificial neural network, and particle swarm optimization methods. The proposed system is expected to provide precise market segmentation for marketing strategy decision making and extended application.

The Application of Particle Swarm Optimization to Passive and Hybrid Active Power Filter Design

Abstract: This paper proposes an optimal design method for passive power filters (PPFs) and hybrid active power filters (HAPFs) set at high voltage levels to satisfy the requirements of harmonic filtering and reactive power compensation. Multiobjective optimization models for PPF and HAPF were constructed. Detuning effects and faults were also considered by constructing constraints during the optimal process, which improved the reliability and practicability of the designed filters. An effective strategy was adopted to solve the multiobjective optimization problems for the designs of PPF and HAPF. Furthermore, the particle swarm optimization algorithm was developed for searching an optimal solution of planning of filters. An application of the method to an industrial case involving harmonic and reactive power problems indicated the superiority and practicality of the proposed design methods.

Layout optimization of satellite module using soft computing techniques

Abstract: This paper presents a hybrid method using soft computing techniques to deal with layout design problem of a satellite module. This problem is a three-dimensional layout optimization problem with behavioral constraints, and is difficult to solve in polynomial time. In this study, we firstly used a Hopfield neural network (HNN) to allocate the given apparatuses and equipment to the bearing plate surfaces in the satellite module. Then, we integrated genetic algorithm/particle swarm optimization (GA/PSO) and quasi-principal component analysis (QPCA) to deal with the further detailed layout optimization. The numerical experimental results showed the feasibility and efficiency of our method for layout optimization of a satellite module.

Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization

Abstract: Inertia weight is one of the most important parameters of particle swarm optimization (PSO) algorithm. Based on the basic idea of decreasing inertia weight (DIW), two strategies of natural exponential functions were proposed. Four different benchmark functions were used to evaluate the effects of these strategies on the PSO performance. The results of the experiments show that these two new strategies converge faster than linear one during the early stage of the search process. For most continuous optimization problems, these two strategies perform better than the linear one.