A novel parallel hybrid particle swarm optimization algorithm named hmPSO is presented. The new algorithm combines particle swarm optimization with a local search method which aims to accelerate the rate of convergence. The hybrid global optimization algorithm adjusts its searching space through the local search results. Parallelization is based on the client-server model, which is ideal for as...

Memetic algorithms (MAs) are hybrid evolutionary algorithms (EAs) that combine global and local search by using an EA to perform exploration while the local search method performs exploitation. Combining global and local search is a strategy used by many successful global optimization approaches, and MAs have in fact been recognized as a powerful algorithmic paradigm for evolutionary computing....

Bilevel programming problem (BLP) is a nested optimization problem that contains one optimization task as a constraint to another optimization task. In this paper, a hybrid differential evolution and particle swarm (DE-PSO) is proposed for solving the linear BLP, in which the hybrid strategy can efficiently prevent the premature convergence of the swarm. Finally, we use the test problems from t...

Mining Sequential Patterns in large databases has become an important data mining task with broad applications. It is an important task in data mining field, which describes potential sequenced relationships among items in a database. There are many different algorithms introduced for this task. Conventional algorithms can find the exact optimal Sequential Pattern rule but it takes a long time,...

Ant colony optimization (ACO) and particle swarm optimization (PSO) are two popular algorithms in swarm intelligence. Recently, a continuous ACO named ACOR was developed to solve the continuous optimization problems. This study incorporated ACOR with PSO to improve the search ability, investigating four types of hybridization as follows: (1) sequence approach, (2) parallel approach, (3) sequenc...

This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotacti...

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for solving successfully one of the most popular logistics management problems, the location routing problem (LRP). The proposed algorithm for the solution of the location routing problem, the hybrid particle swarm optimization (HybPSO-LRP), combines a particle swarm optimization (PSO) ...

In this paper a novel invisible robust watermarking scheme for embedding and extracting a digital watermark in an image is presented. The novelty lies in determining perceptually important coefficients of transform in the host image using simple Haar Wavelet Transform (HWT) and Genetic Algorithm(GA)-Particle Swarm Optimization(PSO) based hybrid optimization. Invisible watermark is embedded such...

in this paper, simultaneous placement of distributed generation, capacitor bank and protective devices are utilized to improve the efficiency of the distribution network. the objectives of the problem are reduction of active and reactive power losses, improvement of voltage profile and reliability indices and increasing distribution companies’ profit. the combination of firefly algorithm, parti...

In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. In this paper, motivated by the behavior and the spatial characteristics of the social and cognitive experience of eachparticle in the swarm,wedevelop ahybrid framework that combines theParticle Swarm Optimization and the Differentia...