Particle swarm optimization (PSO) is a heuristic global optimization method, proposed
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …

The design of evolutionary algorithms has typically been focused on efficiently solving a
single optimization problem at a time. Despite the implicit parallelism of population-based …

Particle swarm optimization (PSO) has shown its advantages in various optimization
problems. Topology and updating strategies are among its key concepts and have …

Particle swarm optimization (PSO) is a stochastic population-based algorithm motivated by
intelligent collective behavior of birds. The performance of the PSO algorithm highly …

The human mind possesses the most remarkable ability to perform multiple tasks with
apparent simultaneity. In fact, with the present-day explosion in the variety and volume of …

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique
inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control …

Multifactorial evolutionary algorithm (MFEA) exploits the parallelism of population-based
evolutionary algorithm and provides an efficient way to evolve individuals for solving …

Path planning is of great significance in motion planning and cooperative navigation of
multiple robots. Nevertheless, because of its high complexity and nondeterministic …

A novel optimization algorithm called musical chairs algorithm (MCA) is introduced in this
paper for a shorter convergence time and lower failure rate compared to other optimization …

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the
performance of the original particle swarm optimization (PSO). However, linear decreasing …