The traveling salesman problem (TSP) is a well-known NP-hard combinatorial optimization problem. The problem is easy to state, but hard to solve. Many real-world problems can be formulated as instances of the TSP, for example, computer wiring, vehicle routing, crystallography, robot control, drilling of printed circuit boards and chronological sequencing. In this paper, we present a modified hy...

In this study we describe a method for extending particle swarm optimization. We have presented a novel approach for avoiding premature convergence to local minima by the introduction of diversity in the swarm. The swarm is made more diverse and is encouraged to explore by employing a mechanism which allows each particle to use a different equation to update its velocity. This equation is also ...

Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform...

The clonal selection mechanism and vaccination strategy of immune system are introduced into particle swarm optimization algorithm in this paper, in order to enhance the ability of global exploration of PSO, avoiding getting into local optimum and improving the accuracy and convergence speed of BP networks. The global Cauchy mutation operator and local Gauss mutation operator are used to improv...

To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vag...

It is well known that solutions of k-means algorithm depend on the initialization of cluster centers and the final solution converges to local minima. In this paper, we introduce a clustering approach that combines ideas from modified particle swarm optimization (PSO) and k-means. The potential benefits of this technique are investigated by incorporating the multidimensional asynchronism and st...

This paper proposes a modified particle swarm optimization considering time-varying acceleration coefficients for the economic-emission load dispatch (EELD) problem. The new adaptive parameter is introduced to update the particle movements through the modification of the velocity equation of the classical particle swarm optimization (PSO) algorithm. The idea is to enhance the performance and ro...

Vehicular Ad-hoc Networks (VANETs) developed rapidly in the last decade which is widely used to improve safety and traffic efficiency. VANET is an active research, standardization and development area due to its potential to improve vehicle and road safety, traffic efficiency, and convenience keeping in mind drivers and passengers comfort. Though VANETs geographic routing was recently emphasize...

In this paper, we propose a modified hybridization of electromagnetism-like mechanism (EM) and particle swarm optimization (PSO) algorithms, called mEMPSO, for designing the proposed functional-link based Petri recurrent fuzzy neural system (FLPRFNS). The mEMPSO implements an instant update particle velocity strategy such that each particle updates its information instantaneously. For reducing ...

The fundamental concept of control for a three-legged IGBT-based voltage source regulator-based stabiliser was thoroughly discussed in this paper. proposed technique's effectiveness and efficiency two-machine based model is demonstrated by comparative analysis grid integrated hybrid system. solar wind system produces 400 volts voltage. A the various stabilisation algorithms currently underway. ...