Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the “dying probability” for the individuals and the “war/disease process” for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Sim...

This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of conver...

Our main objective in this article is to achieve minimum side lobe levels for a specific first null beam-width and also a minimum size of the circumference by an optimization-based design method for non-uniform, planar, and circular antenna arrays. Our approach is based on a new variant of Particle swarm Optimization technique. This new technique is a hybrid of Local Neighborhood based PSO with...

Distribution network reconfiguration is an important aspect of automation and optimization of distribution network system. To handle massive binary code infeasible solutions in distribution network reconfiguration, a kind of sequence code is presented in which a loop is a gene and the label of each switch in the loop is the gene value. To solve mutation probability and slow the convergence of c...

Several biology inspired optimization algorithms such as Genetic Algorithms, Ant Colony Optimization (ACO)and Particle Swarm Optimization (PSO) have previously been proposed by researchers. Recent approaches in numerical optimization have shifted to motivate from complex human social behaviors. In this paper, a new optimization algorithm, namely parliamentary optimization algorithm (POA) is pro...

the traveling salesman problem (tsp) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. the tsp is probably the most famous and extensively studied problem in the field of combinatorial optimization. because this problem is an np-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...

Many scientific and engineering applications require optimization methods to find more than one solution to multimodal optimization problems. This paper presents a new particle swarm optimization (PSO) technique to locate and refine multiple solutions to such problems. The technique, NichePSO, extends the inherent unimodal nature of the standard PSO approach by growing multiple swarms from an i...

Polygonal approximation of digital curves is one of the crucial steps prior to many image analysis tasks. This paper presents a new polygonal approximation approach based on the particle swarm optimization (PSO) algorithm. Each particle represented as a binary vector corresponds to a candidate solution to the polygonal approximation problem. A swarm of particles are initiated and fly through th...

Loan default evaluation and discrimination is a complicated issue because of its nonlinearity and uncertainty. Least square support vector machine (LS-SVM) has been successfully employed to solve regression and time series problem. This paper proposes a novel PSO-LS-SVM model based on the improved PSO algorithm to optimize parameters of LS-SVM, which is a new improved form by synthesizing the e...

The paper discusses an improved modeling of transformer windings based on particle swarm optimization algorithm (PSO) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings parameters a model-based identification approach is introduced using a well-known lumped parameter model. It includes search space estimation using analytical calculations, which ...