In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles' diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this...

Because image segmentation is the base of image identification, analysis and interpretation, the image segmentation has been widely used in many fields. And PSO (Particle Swarm Optimization) algorithm is one of the most common image segmentation algorithms. However, it has the problems of premature convergence and local optimum. To solve the problems, ISABEP (Image Segmentation Algorithm Base o...

A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the oth...

Besides the difficulty of the application problem to be solved with Genetic Algorithms (GAs), an additional difficulty arises because the quality of the solution found, or the computational resources required to find it, depends on the selection of the Genetic Algorithm’s characteristics. The purpose of this paper is to gain some insight into one of those characteristics: the difficult problem ...

We studied the effects of time-correlation of subsequent patterns on the convergence of on-line learning by a feedforward neural network with backpropagation algorithm. By using chaotic time series as sequences of correlated patterns, we found that the unexpected scaling of converging time with learning parameter emerges when time-correlated patterns accelerate learning process. PACS numbers: 8...

We investigate the search properties of pre-evolutionary random catalytic reaction networks, where reactions might be reversible, and replication is not taken for granted. Since it counts only on slow growth rates and weak selective pressure to steer the search process, catalytic search is an inherently slow process. However it presents interesting properties worth exploring, such as the potent...

This paper describes a selection approach for evolutionary algorithms – called feminine selection – that is inspired in the fact that in some animal species the female actively select their reproduction partners. In these species, the males exhibit their attributes, sometimes fighting with other males, and the female choose the one she considers the best. To implement this approach, the algorit...

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled frog leaping (SFL). The SFL divides a population into several memeplexes and then improves each memeplex in an evolutionary process. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to in...

Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...

Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm’s ability of exploration...