A situation where the set of initial solutions lies near the position of the true optimality (most
favourable or desirable solution) by chance can increase the probability of finding the true …

The size, nature, and diversity of the initial population of population-based metaheuristic
algorithms and the number of times the algorithm iterates play a significant role in the …

Exact optimization algorithms are not able to provide an appropriate solution in solving
optimization problems with a high-dimensional search space. In these problems, the search …

As an essential step of metaheuristic optimizers, initialization seriously affects the
convergence speed and solution accuracy. The main motivation of the state-of-the-art …

All metaheuristic optimization algorithms require some initialization, and the initialization for
such optimizers is usually carried out randomly. However, initialization can have some …

Metaheuristics are popularly used in various fields, and they have attracted much attention
in the scientific and industrial communities. In recent years, the number of new metaheuristic …

To solve different kinds of optimization challenges, meta-heuristic algorithms have been
extensively used. Population initialization plays a prominent role in meta-heuristic …

Metaheuristic optimization is a higher-level optimization that uses a simple and efficient
procedure to solve optimization problems. Metaheuristic can understand higher-level …

Based on the analysis of the basic schemes of a variety of population-based metaheuristics
(PBMH), the main components of PBMH are described with functional relationships in this …

This paper proposes a new meta-heuristic algorithm called Pathfinder Algorithm (PFA) to
solve optimization problems with different structure. This method is inspired by collective …