Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligen...

This paper proposes an improved approach based on conventional particle swarm optimization (PSO) for solving an economic dispatch(ED) problem with considering the generator constraints. The mutation operators of the differential evolution (DE) are used for improving diversity exploration of PSO, which called particle swarm optimization with mutation operators (PSOM). The mutation operators are ...

one of the most important extensions of the capacitated vehicle routing problem (cvrp) is the vehicle routing problem with simultaneous pickup and delivery (vrpspd) where customers require simultaneous delivery and pick-up service. in this paper, we propose an effective ant colony optimization (eaco) which includes insert, swap and 2-opt moves for solving vrpspd that is different with common an...

dynamic economic load dispatch is one of the most important roles of power generation’s operation and control. it determines the optimal controls of production of generator units with predicted load demand over a certain period of time. economic dispatch at minimum production cost is one of the most important subjects in the power network’s operation, which is a complicated nonlinear constraine...

This paper presents a Particle Swarm Optimization (PSO) based algorithm for Optimal Power Flow (OPF) in Combined Economic Emission Dispatch (CEED) environment of thermal units while satisfying the constraints such as generator capacity limits, power balance and line flow limits. Particle Swarm Optimization is a population based stochastic optimization, developed by Kennedy and Eberhart [12], in...

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...

The implementation of meta-heuristic algorithm in defining the optimal solution for distribution network reconfiguration has been addressed by many researchers over the years. Nonetheless, the solution in minimizing the main objective which is total power losses could not be solved until today. In this study, the hybridization of evolutionary particle swarm optimization has been modified in ord...

In the last decennium, particle swarms have received considerable attention in the fields of optimization and control. Inspired by swarms of social animals, such as birds, fish, and termites, simple behavior on the local level has been shown to result in useful complex behavior on the global level. Particle Swarm Optimization has proven to be a very powerful optimization heuristic, and swarm ag...

Particle Swarm Optimization algorithm is based on iterative optimization tools, system initialization of a group of random solutions, through iterative search for the optimal value. The basic idea of the fuzzy C-means clustering algorithm is to determine each sample data belonging to a certain degree of clustering, and the degree of membership of sample data is grouped into a cluster. Favor opt...

Aiming at the task allocation in multi-target tracking of wireless sensor networks, the discrete particle swarm optimization based on nearest-neighbor is presented to reduce the communication energy consumption between nodes. First, task allocation is initialized with nearest neighbor algorithm. Then the fitness function is compared through change task allocation matrix to achieve task allocati...