Multi-dimensional particle swarm optimization in dynamic environments

Multi-dimensional particle swarm optimization in dynamic environments

Particle swarm optimization (PSO) has been initially proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. Thanks to its stochastic and population based nature, PSO may avoid becoming trapped in local optima and find the global optimum. However, this i...

Particle Swarm Optimization in Dynamic Environments

Unified Particle Swarm Optimization in Dynamic Environments

A first investigation of the recently proposed Unified Particle Swarm Optimization algorithm on dynamic environments is provided and discussed on widely used test problems. Results are very promising compared to the corresponding results of the standard Particle Swarm Optimization algorithm, indicating the superiority of the new scheme.

Compound Particle Swarm Optimization in Dynamic Environments

Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO) is proposed as a new variant of particle swarm optimization to enhance its performance in dynamic environments. Within CPSO, compound particles are constructed as a novel type of p...

A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

Optimization in dynamic environment is considered amongst prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization appro...