Particle swarm optimization (PSO) is a population-based optimization technique and it has been used to solve many optimization problems successfully. However, more efficient strategies are still needed to control the tradeoff between exploitation exploration in the search process for solving tasks with high complexity. In this work, we present a new hybrid PSO approach to overcome the search di...

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancin...

This paper presents Hybrid Particle Swarm Optimization (PSO) Genetic Algorithm (GA) approaches for the document clustering problem. To obtain an optimal solution using Genetic Algorithm, operation such as selection, reproduction, and mutation procedures are used to generate for the next generations. In this case, it is possible to obtain local solution because chromosomes or individuals which h...

This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm opti...

The clustering in the Wireless Sensor Networks (WSN) is one of the most important investigation areas. There are many cluster head selection algorithms which were developed before today. Hybrid ACO-PSO based algorithm is developed to reduce the energy consumption and enhanced the WSN life-time. The comparison is done among the MSEEC and Hybrid ACO-PSO based MSEEC in the wireless sensor network....

Cloud service provider, infrastructure vendor and clients/Cloud user’s are main actors in any cloud enterprise like Amazon web service’s cloud or Google’s cloud. Now these enterprises take care in infrastructure deployment and cloud services management (IaaS/PaaS/SaaS). Cloud user ‘s need to provide correct amount of services needed and characteristic of workload in order to avoid over – provis...

Deep Web database clustering is a key operation in organizing Deep Web resources. Cosine similarity in Vector Space Model (VSM) is used as the similarity computation in traditional ways. However it cannot denote the semantic similarity between the contents of two databases. In this paper how to cluster Deep Web databases semantically is discussed. Firstly, a fuzzy semantic measure, which integr...

Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a p...

Flexible job-shop scheduling problem (FJSP) has been well known as one of the most difficult NP-hard combinatorial optimization problems. This problem FJSP is an extension of job-shop scheduling problem (JSP) which is to create a sequence of operations satisfying the precedence relationship together with assignment of time and resources for each operation. Thus, the FJSP model is closer to the ...

Literature has proved the individual performance of ABC and PSO while solving various optimization problems. However, as PSO searches the solution by updating the particles and the ABC searches by bees’ wandering behavior, there are drawbacks persist in the individual performance. Hence in our previous work, we have proposed a hybrid swarm optimization technique to outperform the individual per...