Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has
gained prominence in the last two decades due to its ease of application in unsupervised …

Meta heuristics is an optimization approach that works as an intelligent technique to solve
optimization problems. Evolutionary algorithms, human-based algorithms, physics-based …

This study presents the analysis and principle of an innovative optimizer named weIghted
meaN oF vectOrs (INFO) to optimize different problems. INFO is a modified weight mean …

In this paper, a parameter adaptation-based ant colony optimization (ACO) algorithm based
on particle swarm optimization (PSO) algorithm with the global optimization ability, fuzzy …

In recent years, a variety of meta-heuristic nature-inspired algorithms have been proposed to
solve complex optimization problems. However, these algorithms suffer from the …

Traveling salesman problem (TSP) is one typical combinatorial optimization problem. Ant
colony optimization (ACO) is useful for solving discrete optimization problems whereas the …

The grey wolf optimizer (GWO) is a new efficient population-based optimizer. The GWO
algorithm can reveal an efficient performance compared to other well-established …

Feature selection (FS) has received significant attention since the use of a well-selected
subset of features may achieve better classification performance than that of full features in …

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and
slow global convergence speed in ant colony optimization (ACO) algorithm in solving …

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the
echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat …