Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. This paper proposes a Multi Swarm Particle Swarm Optimization (MS-PSO) algorithm inspired by the animal collective behavior, the movement of the swarm and the intelligence of the ...

PSO is a population-based technique for optimization, which simulates the social behavior of the fish schooling or bird flocking. Two significant weaknesses of this method are: first, falling into local optimum and second, the curse of dimensionality. In this work we present the FCPSO-H to overcome these weaknesses. Our approach was implemented in the cooperative PSO, which employs fuzzy logic ...

This paper presents a Particle SwarmOptimization (PSO) algorithm for solving Resource-Constrained Project Scheduling Problems (RCPSP). The PSO model is a new population based optimization strategy introduced by Kennedy and Eberhart in 1995. The PSO is a cooperative and competitive algorithm who belongs to the class of the evolutionary algorithms. We here specialize the algorithm of PSO to the p...

The particle swarm optimizer (PSO) is a populationbased optimization technique that can be widely utilized to many applications. The cooperative particle swarm optimization (CPSO) applies cooperative behavior to improve the PSO on finding the global optimum in a high-dimensional space. This is achieved by employing multiple swarms to partition the search space. However, independent changes made...

In light of the accuracy of particle swarm optimization-particle filter (PSO-PF) inadequate for multi-robot cooperative positioning, the paper presents population density particle swarm optimization-particle filter (PDPSO-PF), which draws cooperative coevolutionary algorithm in ecology into particle swarm optimization. By taking full account of the competitive relationship between the environme...

A novel cooperative evolutionary system, i.e., CGPNN, for automatic design artificial neural networks (ANN’s) is presented where ANN’s structure and parameters are tuned simultaneously. The algorithms used in CGPNN combine genetic algorithm (GA) and particle swarm optimization (PSO) on the basis of a direct encoding scheme. In CGPNN, standard (real-coded) PSO is employed to training ANN’s free ...

A high precision epipolar line restoration algorithm based on Multipopulation Cooperative PSO (MCPSO) is proposed in this paper. It adopts Harris operator to extract corner point and finishes gray cross -correlation matching. Firstly, the fundamental matrix initial value between matches in two images is calculated by 8 pairs matches algorithm. And then the optimal value of this matrix is gotten...