This article proposes an improved particle swarm optimization (PSO) with suggested parameter setting “Selective Particle Regeneration”. To evaluate its reliability and efficiency, this approach is applied to multimodal function optimizing tasks. 12 benchmark functions were tested, and results are compared with those of PSO and GA-PSO. It shows the proposed method is both robust and suitable for...

In this paper an improved Particle Swarm Optimization (PSO) scheme is proposed to solve Static Routing and Wavelength Assignment (static RWA) where the movement of the swarm particles is influenced by their personal-best position searched so far and the position of the global-best particle. Simulation results show that the proposed scheme performs better in terms of particle fitness value and a...

In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a trigger...

In this paper, we formulate the dynamics and philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the whole population level. After that we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization...

To overcome the challenges of Data gathering and enhancement of lifetime of mobile nodes we propose a new data gathering technique with multiple mobile sinks based on particle swarm optimization (PSO) technique. Particle changes its condition according to the following three principles in the basic particle swarm optimization algorithm: (1) to keep its inertia (2) to change the condition accord...

In this paper, a recurrent functional neural fuzzy network (RFNFN) with symbiotic particle swarm optimization (SPSO) is proposed for solving identification and prediction problems. The proposed RFNFN model has feedback connections added in the membership function layer that can solve temporal problems. Moreover, an efficient learning algorithm, called symbiotic particle swarm optimization (SPSO...

This paper presents a new hybridization of VNS and path-relinking on a particle swarm framework for the permutational fowshop scheduling problem with total flowtime criterion. The operators of the proposed particle swarm are based on path-relinking and variable neighborhood search methods. The performance of the new approach was tested on the bechmark suit of Taillard, and five novel solutions ...

Electric power line overhaul plan is an important issue on power system and engineering practice. As particle swarm optimization is to be a new intelligent algorithm. It is gradually applied into power system these years. This paper provides a relative mathematical model to solve the problems in power line overhaul. Particle swarm optimization algorithm has advantages of less parameters setting...

Particle swarm optimization techniques are typically made up of a population of simple agents interacting locally with one another and with their environment, with the goal of locating the optima within the operational environment. In this paper, a robust and intelligent particle swarm optimization framework based on multi-agent system is presented, where learning capabilities are incorporated ...

The Particle Swarm Optimizer, like many other evolutionary and classical minimization methods, su ers the problem of occasional convergence to local minima, especially in multimodal and scattered landscapes. In this work we propose a modi cation of the Particle Swarm Optimizer that makes use of a new technique, named Function \Stretching", to alleviate the local minima problem. Function \Stretc...