In the paper a modified particle swarm optimization (MPSO) is proposed where concepts from firefly algorithm (FA) are borrowed to enhance the performance of particle swarm optimization (PSO). The modifications focus on the velocity vectors of the PSO, which fully use beneficial information of the position of particles and increase randomization item in the PSO. Finally, the performance of the p...

This paper presents a modified particle swarm optimization (PSO) with simulated annealing (SA) technique. An improved PSO-based artificial neural network (ANN) is developed. The results show that the proposed SAPSO-based ANN has a better ability to escape from a local optimum and is more effective than the conventional PSO-based ANN. r 2004 Elsevier B.V. All rights reserved.

A modified particle swarm optimization (PSO) algorithm applied to planar array synthesis considering complex weights and directive element patterns is presented in this paper. The modern heuristic classical PSO scheme with asynchronous updates of the swarm and a global topology has been modified by introducing tournament selection, one of the most effective selection strategies performing in ge...

Markowitz cardinality constraint mean-variance (MCCMV) model is a well studied and important one in the portfolio optimization literature. It is formulated as mixed integer quadratic programming problem (MIQP) which belongs to class of NP-hard problems, thus various heuristic and meta-heuristic algorithms are applied to solve it. In this paper, two modified versions of particle swarm optimizati...

This paper proposes a hierarchical hybrid particle swarm optimization (PSO) and differential evolution (DE) based algorithm (HHPSODE) to deal with bi-level programming problem (BLPP). To overcome the shortcomings of basic PSO and basic DE, this paper improves PSO and DE, respectively by using a velocity and position modulation method in PSO and a modified mutation strategy in DE. HHPSODE employ...

This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

Particle swarm optimization (PSO) is a population-based stochastic optimization originating from artificial life and evolutionary computation. PSO is motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and ability of adapting to the dynamic environment make PSO b...

This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy fu...

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 presents a modified Iteration Particle Swarm Optimization (IPSO) algorithm to tune optimal gains of a Proportional Integral Derivative (PID) type multiple stabilizers and non-smooth nonlinear parameters (such as saturation limits) for multi machine power system, simultaneously. The problem of robustly tuning of PID based multiple stabilizer design is formulated as an optimization pro...