This paper presents a comprehensive study on monoand multi-objective approaches for electrical distribution network design using particle swarmoptimization (PSO). Specifically, two distribution network design problems, i.e., static and expansion planning, are solved using PSO. The network planning involves optimization of both network topology and branch conductor sizes. Both the planning probl...

Inspired by the estimation capability of Kalman filter, we have recently introduced a novel population-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, which includes prediction, measurement, and estimation, the global minimum/maximum can be estimated. Measurement process, which is r...

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the alg...

This paper presents a new Artificial Bee Colony (ABC) optimization algorithm to solve function optimization problems. The proposed approach is called OCABC, which introduces opposition-based learning concept and dynamic Cauchy mutation into the standard ABC algorithm. To verify the performance of OCABC, eight well-known benchmark function optimization problems are used in the experiments. Exper...

Particle swarm optimization (PSO) has been employed on several optimization problems, including the clustering problem. PSO has also been employed in the clustering of data of different structure and dimensionality. In this paper it is employed in the clustering of nucleic acid sequences. The application of clustering, as a statistical tool, in the analysis of data of varied complexity has been...

This paper firstly presents a novel constraint-handling technique , called dynamicobjective method (DOM), based on the search mechanism of the particles of particle swarm optimization (PSO). DOM converts the constrained optimization problem into a bi-objective optimization problem, and then enables each particle to dynamically adjust its objectives according to its current position in the searc...

Particle Swarm Optimization (PSO) is an evolutionary algorithm based off of swarm intelligence and is used as a stochastic optimization technique. One major characteristic of PSO is many configuration parameters, which allow the algorithm to be adjusted to various problem landscapes. In the first half of this paper, we empirically benchmark performance of different parameterizations of PSO in a...

Grid computing which is based on the high performance computing environment, basically used for solving complex computational demands. In the grid computing environment, scheduling of tasks is a big challenge. The task scheduling problem can be defined as a problem of assigning the number of resources to tasks where number of resources is less than the number of available tasks. Particle swarm ...

This paper present the hybrid approaches of Particle Swarm Optimization (PSO) with Genetic Algorithm (GA). PSO and GA are population based heuristic search technique which can be used to solve the optimization problems modeled on the concept of Evolutionary Approach. In standard PSO, the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on subop...