Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduc...

Abstract In this paper, we present an unified framework that encompasses both particle swarm optimization (PSO) and federated learning (FL). This shows can understand PSO FL in terms of a function to be optimized by set agents but which have different privacy requirements. is the most relaxed case, considers slightly stronger constraints. Even requirements considered will lead still privacy-pre...

This paper proposes a refined version of particle swarm optimization technique for the optimum design of steel structures. Swarm is composed of a number of particles and each particle in the swarm represents a candidate solution of the optimum design problem. Design constraints in accordance with ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution) are imposed by t...

Multi-class miner is well recognized method for stream data classification. For the process of multi-class miner evaluation of new feature during classification is major problem. The problem of feature evaluation decreases the performance of multi-class miner (MCM). For the improvement of multi-class miner particle of swarm optimization technique is used. Particle of swarm optimization controls...

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which ...

Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its performance seriously degrades when coping with problems a lot of local optima. To alleviate this issue, paper designs predominant cognitive learning particle (PCLPSO) method to effectively tackle complicated problems. Specifically, for each particle, new promising exemplar is constructed by...