Data clustering is considered as one of the most promising data analysis methods in data mining and on the other side KMeans is the well known partitional clustering technique. Nevertheless, K-Means and other partitional clustering techniques struggle with some challenges where dimension is the core concern. The different challenges associated with clustering techniques are preknowledge of init...

This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use of particles’ personal bests and offspring for more effective nondomination comparisons. Instead of a single comparison between a particle’s personal best and its offspring, NSPSO compares all particles’ ...

The structural health monitoring (SHM) approach plays a key role not only in engineering but also other various disciplines by evaluating the safety and performance of structures. damage detection methods could be regarded as core SHM strategies. That is because early damages measures to taken repair replace damaged members with healthy ones lead economic advantages would prevent human disaster...

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem ...

Optimization algorithms are proposed to tackle different complex problems in different areas. In this paper, we firstly put forward a new memetic evolutionary algorithm, named Monkey King Evolutionary (MKE) Algorithm, for global optimization. Then we make a deep analysis of three update schemes for the proposed algorithm. Finally we give an application of this algorithm to solve least gasoline ...

Pre-processing plays a vital role in classification tasks, particularly when complex features are involved, and this demands a highly intelligent method. In bioinformatics, where datasets are categorised as having complex features, the need for pre-processing is unavoidable. In this paper, we propose a framework for selecting the discriminatory features from protein sequences prior to classific...

The standard particle swarm optimization (PSO) algorithm allocates the total available budget of function evaluations equally and concurrently among the particles of the swarm. In the present work, we propose a new variant of PSO where each particle is dynamically assigned different computational budget based on the quality of its neighborhood. The main goal is to favor particles with high-qual...

Distant luminous quasars provide important information on the growth of the first supermassive black holes, their host galaxies and the epoch of reionization. The identification of quasars is usually performed through detection of their Lyman-α line redshifted to 0.9 microns at z > 6.5. Here, we report the discovery of a very Lyman-α luminous quasar, PSO J006.1240 + 39.2219 at redshift z = 6.61...

Web service location allocation problem is an important problem in the modern IT industry. In this paper, the two major objectives, i.e. deployment cost and network latency, are considered simultaneously. In order to solve this new multi-objective problem effectively, we adopted the framework of binary Particle Swarm Optimization (PSO) due to its efficacy that has been demonstrated in many opti...

Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have introduced with which performance PSO can be improved. However, existing simply adjust their based on iterations, leading unsatisfactory results because incompatibility between and current state part...